Assays and methods for assisting in vitro fertilization

Abstract

The present invention relates to a method for determining the state of the endometrium using a sample from a subject, for example, a plasma sample, and includes: (a) obtaining a sample from a subject or using a sample obtained from a subject, where the miRNA expression profile includes the expression levels of multiple miRNAs; and (b) analyzing the miRNA expression profile using a computer-based machine learning model to obtain a predictive score.

Description

[Technical Field]

[0001] Cross-references to related applications This application claims the interests of U.S. Provisional Patent Application No. 63 / 507,444, filed on 9 June 2023, and U.S. Provisional Patent Application No. 63 / 613,056, filed on 20 December 2023. All the contents of each of these applications are incorporated herein by reference.

[0002] The present invention relates to a method for determining the endometrial receptivity of a subject, the method using (a) plasma, (b) a microRNA (miRNA) expression profile including the expression levels of multiple miRNAs, e.g., 281 miRNAs, and (c) a computer-based method for classifying the subject's endometrial state based on the miRNA expression profile. Aspects of the present invention further relate to a kit suitable for performing the method, as well as the use of the kit for diagnostic and therapeutic purposes. In some embodiments, the method and / or kit is used to classify a subject's responsiveness to in vitro fertilization (IVF) treatment. [Background technology]

[0003] Assisted reproductive technology (ART), including in vitro fertilization (IVF), has emerged as a potential approach to overcome the lack of reproductive success. A major factor influencing the success rate of IVF is the receptive state of the endometrium. The endometrium is receptive only for a relatively short period called the window of implantation (WOI). The WOI usually occurs around days 19-21 of the menstrual cycle. There has been a long-standing need to monitor the state of the endometrium based on a non-invasive approach that indicates the opportunity for embryo implantation in a more reliable way, rather than relying solely on the calendar approach, which tends to be unreliable.

[0004] The human endometrium is a tissue that is periodically regulated by both proteins and miRNAs. Extracellular vesicles can transport miRNAs and transmit cellular processes to other cell types. The human genome contains more than 2500 miRNAs, some of which have been shown to play a role in the reproductive cycle. For example, recent literature has demonstrated that certain miRNAs regulate the expression of genes involved in the establishment and progression of WOI. Certain miRNAs downregulate gene expression in endometrial epithelial cells, ultimately leading to the inhibition of endometrial secretion during the secretory phase. Traditionally, histological and imaging methods have been used to assess the state of the endometrium. However, these methods have long been recognized as time-consuming and often fail to clearly distinguish between the receptive and non-receptive states of the endometrium.

[0005] Therefore, there remains a need for improved methods to determine endometrial implantation potential that require less tissue input and / or provide a more reliable determination of the receptive or non-receptive state of the endometrium in a subject. [Overview of the Initiative]

[0006] In some embodiments, the present invention provides a method for determining the endometrial condition of a subject whose endometrial condition requires assessment. The method includes the following: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determine the microRNA (miRNA) expression profile in the sample. (c) Input the miRNA expression profile from step (b) into a computer-based method to generate a report. (d) Determine the endometrial condition of the subject based on the report.

[0007] In some embodiments of the above-described or related embodiments, the state of the endometrium is pre-receptive, receptive, or post-receptive. In some embodiments, if the state of the endometrium is receptive, the subject is receptive to embryo transfer. In some embodiments, if the state of the endometrium is pre-receptive, the subject is receptive to embryo transfer approximately 24 hours after the sample is taken from the subject. In some embodiments, if the state of the endometrium is post-receptive, the subject has passed the implantation window. In some embodiments, if the state of the endometrium is post-receptive, the subject has passed the receptive stage.

[0008] In some embodiments of the above-described or related embodiments, the subject is receptive to embryo transfer approximately 24 hours before the sample is obtained from the subject. In some embodiments, the subject is receptive to embryo transfer approximately 12 hours before the sample is obtained from the subject.

[0009] In some embodiments of the above-described or related embodiments, the subject is infertile.

[0010] In some embodiments of the above-described or related embodiments, the subject has a history of implantation failure, has few remaining high-quality embryos, has a BMI that is lower or higher than the normal range, is overweight, and / or underweight.

[0011] In some embodiments of the above-described or related embodiments, the subject is undergoing in vitro fertilization. In some embodiments, the subject is receiving infertility treatment. In some embodiments, the subject is receiving assisted reproductive technology.

[0012] In some embodiments, the present invention provides a method for determining when a subject is receptive to embryo transfer, the method including the following: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determining the microRNA (miRNA) expression profile in the sample. (c) Inputting the miRNA expression profile from step (b) into a computer-based method to generate a report. (d) Identifying the state of the endometrium of the subject based on the report. (e) Determining the time when the subject is receptive to embryo transfer based on the state of the endometrium of the subject.

[0013] [[ID=ll]] In some embodiments of any of the above aspects or related aspects, the state of the endometrium is before the receptive phase, during the receptive phase, or after the receptive phase. In some embodiments, when the state of the endometrium is during the receptive phase, the subject is receptive to embryo transfer.

[0014] In some embodiments of any of the above aspects or related aspects, the method includes transferring an embryo to the subject.

[0015] In some embodiments of any of the above aspects or related aspects, when the state of the endometrium is before the receptive phase, the subject is receptive to embryo transfer about 24 hours after the sample is obtained from the subject. In some embodiments, the method includes embryo transfer to the subject about 24 hours after a blood sample is taken from the subject.

[0016] In some embodiments of any of the above aspects or related aspects, when the state of the endometrium is after the receptive phase, the subject has passed the implantation window. In some embodiments of any of the above aspects or related aspects, when the state of the endometrium is after the receptive phase, the subject has passed the receptive phase.

[0017] In some embodiments of the above-described or related embodiments, the subject is receptive to embryo transfer approximately 24 hours before the sample is obtained from the subject. In some embodiments, the subject is receptive to embryo transfer approximately 12 hours before the sample is obtained from the subject.

[0018] In some embodiments of the above-described or related embodiments, the method is performed during the first menstrual cycle. In some embodiments of the above-described or related embodiments, the method is repeated during the subject's next menstrual cycle. In some embodiments, the method is repeated in subsequent menstrual cycles. In some embodiments, the subsequent menstrual cycle is the second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh, twelfth, or subsequent menstrual cycles after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the second menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the third menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the fourth menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the fifth menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the sixth menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the seventh menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the eighth menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the ninth menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the tenth menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the eleventh menstrual cycle after the first menstrual cycle. In some embodiments, the subsequent menstrual cycle is the twelfth menstrual cycle after the first menstrual cycle.

[0019] In some embodiments of the above-described or related embodiments, the method includes implanting an embryo in the subject during the subject's next menstrual cycle, wherein the embryo is implanted at a time identified in the previous menstrual cycle as a period in which the subject is receptive to embryo transfer. In some embodiments of the above-described or related embodiments, the method includes implanting an embryo in the subject during a subsequent menstrual cycle, wherein the embryo is implanted at a time identified in the first menstrual cycle as a period in which the subject is receptive to embryo transfer.

[0020] In some embodiments of the above-described or related embodiments, the sample is taken approximately 5 days after the start of progesterone administration during the assisted reproductive technology cycle. In some embodiments, the sample is taken approximately 7 days after a surge in LH is detected in the subject. In some embodiments, the sample is taken approximately 7 days after hCG is administered to the subject. In some embodiments, the sample is taken approximately 4 and 5 days after the start of progesterone administration during the assisted reproductive technology cycle. In some embodiments, the sample is taken approximately 6 and 7 days after a surge in LH is detected in the subject. In some embodiments, the sample is taken approximately 6 and 7 days after hCG is administered to the subject.

[0021] In some embodiments of the above-described or related embodiments, the sample is a blood sample. In some embodiments, the sample is a plasma sample.

[0022] In some embodiments of the above-described or related embodiments, the subject is human. In some embodiments, the subject is human female.

[0023] In some embodiments of the above-described or related embodiments, the subject is between 21 and 45 years of age. In some embodiments, the subject is 35 years of age or older.

[0024] In some embodiments of the above-described or related embodiments, the subject is undergoing assisted reproductive technology.

[0025] In some aspects, the present invention provides a kit for determining the condition of a subject's endometrium.

[0026] In some embodiments, the present invention provides methods including the following: To obtain a blood sample from a patient during an in vitro fertilization (IVF) implantation cycle. Here, the blood sample is collected from the patient on the day of embryo transfer during the IVF implantation cycle. To determine the predicted endometrial state of the patient at the time of embryo transfer based on the miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on the miRNA expression profile data derived from the blood sample. To generate a predicted embryo transfer date based on the predicted state of the uterine lining. The patient is to undergo a subsequent in vitro fertilization cycle by performing embryo transfer on the predicted embryo transfer date.

[0027] In some embodiments, the present invention provides methods including the following: To obtain a blood sample from a patient during an in vitro fertilization (IVF) cycle. Here, the blood sample is collected from the patient on the day of embryo transfer during the IVF cycle. To determine the predicted endometrial state of the patient at the time of embryo transfer based on the miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on the miRNA expression profile data derived from the blood sample. To generate a predicted embryo transfer date based on the predicted state of the uterine lining. The patient is to undergo a subsequent in vitro fertilization cycle by performing embryo transfer on the predicted embryo transfer date.

[0028] In some embodiments of the above-described or related embodiments, the blood sample is collected before embryo transfer.

[0029] In some embodiments of the above-described or related embodiments, the method further comprises preserving the blood sample, wherein preserving the blood sample preserves subsequent extraction and sequencing of miRNA.

[0030] In some embodiments of the above-described or related embodiments, embryo transfer is fresh embryo transfer. In some embodiments of the above-described or related embodiments, embryo transfer is frozen embryo transfer (FET).

[0031] In some embodiments of the above-described or related embodiments, the method includes performing a pregnancy test on the patient, and if the pregnancy test is negative, obtaining the blood sample from storage, and providing a miRNA expression profile by sequencing the blood sample for miRNA.

[0032] In some embodiments of the above-described or related embodiments, the method includes performing a pregnancy test on the patient and, if the pregnancy test is negative, providing a miRNA expression profile by sequencing the blood sample for miRNA.

[0033] In some embodiments, the present invention provides methods including the following: To determine endometrial condition data for multiple patients. Here, the endometrial condition data is obtained from at least one of the following: endometrial samples and pregnancy outcomes. The individual miRNA expression profiles from the aforementioned multiple patients are associated with endometrial state data. Here, each patient's individual miRNA expression profile is associated with each patient's endometrial state. The process involves training a machine learning model based on associated miRNA expression profiles and associated endometrial state data. Here, the trained machine learning model is trained to output a predicted endometrial state based on the input miRNA expression profiles.

[0034] In some embodiments of any of the above-described or related embodiments, the endometrial state is at least one of PRE, WOI, and POST. In some embodiments of any of the above-described or related embodiments, the endometrial state is at least one of pre-receptive, receptive, and post-receptive. In some embodiments of any of the above-described or related embodiments, the endometrial state is pre-receptive. In some embodiments of any of the above-described or related embodiments, the endometrial state is receptive. In some embodiments of any of the above-described or related embodiments, the endometrial state is post-receptive.

[0035] In some embodiments of the above-described or related embodiments, the machine learning model is a plurality of machine learning models, each machine learning model generates one or more predictions from PRE, WOI, and POST. In some embodiments of the above-described or related embodiments, the machine learning model is a plurality of machine learning models, each machine learning model generates one or more predictions from pre-receptive, receptive, and post-receptive.

[0036] In some embodiments of the above-described or related embodiments, the model further associates one or more of each patient's age, body mass index (BMI), pregnancy history, and implantation failure with the patient's endometrial condition, so that input data for each patient's age, body mass index (BMI), pregnancy history, and implantation failure can be input to the machine learning model in addition to the miRNA expression profile.

[0037] In some embodiments of the above-described or related embodiments, the machine learning is trained on a server in a network, and the server is accessible to a patient or clinician for uploading the miRNA expression data.

[0038] In some embodiments, the present invention provides methods including the following: The machine learning model in a networked server receives a digital representation of the patient's miRNA expression profile, where the miRNA expression profile is determined by sequencing of the patient's blood sample. The machine learning model processes the miRNA expression profile. Here, the machine learning model is trained to output a predicted endometrial state based on the input miRNA expression profile, and the processing obtains the patient's predicted endometrial state based on the digital representation of the provided miRNA expression profile.

[0039] In some embodiments of the above-described or related embodiments, input data is received regarding each patient's age, body mass index (BMI), pregnancy history, and implantation failure, and the system is trained to predict the patient's endometrial condition based on the received input data. The system further includes:

[0040] The machine learning model receives input data for each patient's age, body mass index (BMI), pregnancy history, and implantation failure, so that it outputs the predicted endometrial state based on the input data in addition to the miRNA expression profile.

[0041] In some embodiments of the above-described or related embodiments, the trained machine learning model is trained on a plurality of miRNA expression profiles, each miRNA expression profile is determined from a sample of a predetermined patient among a plurality of patients, each miRNA expression profile is associated with a known endometrial state of the predetermined patient, thereby training the trained machine learning model to output the endometrial state of a patient other than the plurality of patients based on a miRNA expression profile determined from a sample of a patient other than the plurality of patients.

[0042] In some embodiments of the above-described or related embodiments, the machine learning model is a classifier. [Brief explanation of the drawing]

[0043] [Figure 1A] This figure shows that approximately 30% of infertile women have a displacement of the implantation window (WOI). [Figure 1B] This diagram provides a schematic representation of the endometrial state during the menstrual cycle in human women. The standard receptive window (or implantation window) typically occurs approximately 19–21 days after the start of the menstrual cycle. [Figure 1C] The diagram provides an illustration showing different windows of endometrial implantation capacity, including the pre-receptive, receptive, and post-receptive stages. [Figure 2A] This diagram provides a schematic representation of the workflow for sample collection, processing, and data analysis using the Optimal Receptivity Assay (ORA) to determine the implantation window (WOI) in patients. [Figure 2B]This provides a schematic diagram illustrating the workflow for sample collection, processing, and data analysis using ORA to determine the implantation window for patients. [Figure 2C] This provides a schematic diagram illustrating the workflow for sample collection, processing, and data analysis using ORA to determine the implantation window for patients. [Figure 2D] This provides a schematic diagram illustrating the workflow for sample collection, processing, and data analysis using ORA to determine the implantation window for patients. [Figure 2E] This provides a schematic diagram illustrating the workflow for sample collection, processing, and data analysis using ORA to determine the implantation window for patients. [Figure 3] This diagram illustrates the presence of miRNAs in the blood. miRNAs are known to regulate immune mechanisms, cell apoptosis, and angiogenesis during pregnancy. These miRNAs are measured in the blood using ORA analysis to determine endometrial implantation potential. [Figure 4] A diagram illustrating the ORA study group is provided. Patients include those aged 21–45 years, with a BMI greater than 18.5, and possessing one or more good-grade embryos. Biopsies for MIRA analysis and blood samples for ORA are collected during a mock cycle of hormone replacement therapy (HRT) on day 5 after progesterone administration. Subsequently, additional blood samples are collected for ORA analysis to validate the predictive model during the treatment / implantation cycle after the implantation window has been determined using MIRA. In some embodiments, additional blood samples are collected for ORA analysis to validate the predictive model during the treatment cycle (e.g., in vitro fertilization) after the implantation window has been determined using MIRA. [Figure 5] This provides a schematic diagram illustrating the workflow for sample preparation, predictive model setup, quality control, verification, and validation. [Figure 6A]This is a schematic diagram illustrating the process by which computer-based methods for optimal receptivity assays (ORAs) are constructed and how ORAs generate test results. [Figure 6B] This is a schematic diagram illustrating the process by which computer-based methods for optimal receptivity assays (ORAs) are constructed and how ORAs generate test results. [Figure 7] This figure shows novel miRNA biomarkers identified in the ORA predictive model compared to the MIRA predictive model. The ORA and MIRA predictive models share 65 similar miRNAs that contribute to various biological processes. [Figure 8] This is a schematic diagram showing blood sample collection during hormone replacement therapy (HRT) or natural cycle (LH) in subjects. If embryo implantation is unsuccessful, the blood is processed and used to determine the personalized embryo transfer (pET) window based on ORA predictions. [Figure 9A] This document provides graphs showing the differential expression of miRNAs between different endometrial implantation potential states. According to the expression levels of miRNAs in different states, they can be classified into three conditions. Figure 9A shows miRNAs whose expression levels decrease from the pre-receptive group to the receptive group and then to the post-receptive group. First, the ratio of the total number of miRNA reads to the total number of miRNA reads was determined, and then this ratio was multiplied by 1,000,000 to determine the expression level of each miRNA. Finally, the obtained values ​​were calculated by performing a log2 transformation. For each box plot, the median is shown as a black horizontal line within the box, and the boxes represent the upper and lower quartiles. The black whiskers at the top and bottom indicate the maximum and minimum values, respectively. Significant post-hoc comparisons are indicated by an asterisk (*) (* indicates a P value < 0.05, ** indicates a P value < 0.01, *** indicates a P value < 0.001, ns indicates no significant difference). FC indicates a multiplicative change. [Figure 9B]This graph shows the differentially expressed miRNAs between different endometrial implantation potential states. According to their expression levels in different states, miRNAs can be classified into three conditions. Figure 9B shows miRNAs showing differential expression levels in the post-receptive group compared to the other two groups. First, the ratio of total miRNA reads to miRNA reads was determined, and then this ratio was multiplied by 1,000,000 to determine the expression level of each miRNA. Finally, the obtained values ​​were calculated by performing a log2 transformation. For each box plot, the median is shown as a black horizontal line within the box, and the boxes represent the upper and lower quartiles. The black whiskers at the top and bottom indicate the maximum and minimum values, respectively. Significant post-hoc comparisons are indicated by an asterisk (*) (* indicates a P value < 0.05, ** indicates a P value < 0.01, *** indicates a P value < 0.001, ns indicates no significant difference). FC means multiplicative change. [Figure 9C] This document provides graphs showing the differentially expressed miRNAs between different endometrial implantation potential states. Based on the expression levels of miRNAs in different states, they can be classified into three conditions. Figure 9C shows the differentially expressed miRNAs between the pre-receptive and receptive groups. First, the ratio of total miRNA reads to miRNA reads was determined, and then this ratio was multiplied by 1,000,000 to determine the expression level of each miRNA. Finally, the obtained values ​​were calculated by performing a log2 transformation. For each box plot, the median is shown as a black horizontal line within the box, and the boxes represent the upper and lower quartiles. The black whiskers at the top and bottom indicate the maximum and minimum values, respectively. Significant post-hoc comparisons are indicated by an asterisk (*) (* indicates a P value < 0.05, ** indicates a P value < 0.01, *** indicates a P value < 0.001, ns indicates no significant difference). FC indicates a multiplicative change. [Figure 10A] This diagram shows the workflow for a clinical trial in which miRNA expression profiles are determined during a hormone replacement therapy (HRT) treatment cycle following progesterone treatment on day 3, 4, 5, 6, or 7. [Figure 10B] This figure shows research groups in clinical trials that determine miRNA expression profiles during hormone replacement therapy (HRT) treatment cycles following progesterone treatment on day 3, 4, 5, 6, or 7. [Figure 11A] This figure shows the clinical and environmental factors that affect endometrial receptivity. Various factors such as environment, age, body mass index (BMI), and lifestyle contribute to changes in the endometrium, potentially affecting endometrial receptivity and causing displacement of the implantation window in subjects. [Figure 11B] This diagram illustrates how miRNAs alter bodily functions such as cell growth, reproduction, organ development, and embryonic development. [Figure 12] This schematic diagram illustrates how miRNAs alter different cells and cellular functions, including the implantation process. Because miRNAs can regulate a wide range of genes, they can play a role in patients with recurrent implantation failure. These miRNAs can function as biomarkers to predict endometrial receptivity using biopsies and fluid samples. [Figure 13] This figure shows an exemplary embodiment of training a predictive model. [Figure 14] This figure shows an exemplary embodiment that uses a pre-trained predictive model. [Figure 15] This figure shows an exemplary embodiment of the method employed by the present invention. [Figure 16] This diagram shows a computer system / server within a computing node in the form of a general-purpose computing device. [Modes for carrying out the invention]

[0044] The disclosures and embodiments described herein should be construed as illustrative only and not intended to limit the scope of the invention. While certain terms are used herein, unless otherwise specified, they are used in a general and descriptive sense only and not for limiting purposes.

[0045] definition As used herein, the singular forms "a," "an," and "the" are intended to include the plural form unless the context clearly indicates otherwise.

[0046] The term "cDNA" refers to complementary DNA produced by reverse transcription of an RNA preparation using reverse transcriptase. In some embodiments, the RNA preparation contains miRNA extracted from an endometrial tissue sample.

[0047] The term "expression" refers to the transcription and / or accumulation of RNA molecules in a biological sample, such as an endometrial tissue sample from a subject. In this context, the term "miRNA expression" refers to the amount of one or more miRNAs in a biological sample, and miRNA expression can be detected using appropriate methods known in the art.

[0048] As used herein, the term “implantation” refers to the stage of pregnancy in which the embryo attaches to the uterine wall.

[0049] The term "in vitro fertilization (IVF)" has been previously defined as the process by which oocytes are fertilized outside the body by sperm in vitro. IVF is the primary treatment for infertility when in vivo conception fails. This method can also be performed when fertilization is carried out by "intracytoplasmic sperm injection" or "ICSI," which refers to the in vitro fertilization procedure in which a single sperm is directly injected into an oocyte. This procedure is most commonly used to overcome male infertility factors, but is also used as a method of in vitro fertilization when oocytes are not easily penetrated by sperm, and occasionally in many conditions (poor response, unexplained infertility, endometriosis). In some embodiments, IVF includes fresh embryo transfer. In some embodiments, IVF includes frozen embryo transfer (FET).

[0050] In some embodiments, the implantation window (WOI) is estimated based on a regular menstrual pattern. In some embodiments, the implantation window occurs approximately seven days before the first day of the expected menstrual period. For example, in some embodiments, the implantation window corresponds to days 19–21 of the ideal 28-day human menstrual cycle. In some embodiments, the implantation window corresponds to days 19–23 of the ideal 28-day human menstrual cycle. Since similar cycles have been disclosed in other mammals, the method of the present invention can be applied to female mammals of any species.

[0051] The terms “microRNA” and “miRNA” are used interchangeably herein and refer to a class of non-coding RNAs, approximately 18 to 25 nucleotides long, derived from endogenous genes. miRNAs function as post-transcriptional regulators of gene expression by base-pairing the 3' untranslated region (UTR) of target mRNA for mRNA degradation or translation inhibition. In some embodiments, miRNAs are cell-free miRNAs in the blood.

[0052] The terms “nucleic acid,” “nucleotide,” and “polynucleotide” are used interchangeably and refer to polymers of DNA or RNA in either single-stranded or double-stranded forms. Unless otherwise specified, these terms encompass polynucleotides containing known analogs of natural nucleotides that have similar binding properties to reference nucleic acids and are metabolized in a similar manner to naturally occurring nucleotides.

[0053] The term "primer" refers to an oligonucleotide that, when placed under conditions that induce the synthesis of a primer extension product, acts to initiate the synthesis of a complementary nucleic acid chain in the presence of a polymerization inducer such as nucleotides and DNA or RNA polymerase, and at appropriate temperature, pH, metal ion concentration, and salt concentration.

[0054] The term "probe" refers to a polynucleotide-containing structure that includes a nucleic acid sequence complementary to the nucleic acid sequence present in a target nucleic acid analyte (e.g., a nucleic acid amplification product). The polynucleotide region of a probe may consist of DNA and / or RNA and / or synthetic nucleotide analogs. Probes are generally of a length suitable for their use in the specific detection of all or part of the target sequence of the target nucleic acid.

[0055] The term "targeting" refers to the selection of the appropriate nucleotide sequence to hybridize with the nucleic acid sequence of interest.

[0056] The term “window of implantation” refers to the self-limiting period of endometrial receptivity from day 19 to day 23 (in women) of the menstrual cycle, during which crosstalk exists between the receptive endometrium and the functional blastocyst. In some embodiments, the window of implantation occurs approximately from day 19 to day 21 of the menstrual cycle. In some embodiments, the window of implantation occurs approximately from day 19 to day 23 of the menstrual cycle. In some embodiments, the window of implantation occurs before day 19. In some embodiments, the window of implantation occurs after day 23. The terms “window of implantation,” “WOI,” “receptivity,” “receptive state,” and “receptive period” are used interchangeably herein. In a normal menstrual cycle, this is achieved by the local effects of ovarian estrogen and progesterone, which induce a series of cellular and molecular events in the endometrium, resulting in appropriate endometrial receptivity.

[0057] Overview of methods for determining the state of the uterine lining Methods based on gene expression level testing are being developed. Early studies focused on a small number of marker genes. Igenomix developed an Endometrial Receptivity Analysis (ERA) test that relies on a microarray of 238 specific genes involved in endometrial receptivity. However, microarray-based ERA tests have certain drawbacks. For example, microarray-based gene expression measurements are known to require large amounts of tissue samples. Furthermore, microarray technology is generally less specific than quantitative polymerase chain reaction (qPCR) technology. Next-generation sequencing (NGS)-based ERA tests are still in their infancy. Other known methods require expensive and time-consuming invasive biopsies (see Table 1).

[0058] In some embodiments, the present invention provides a non-invasive method for determining endometrial receptivity. Endometrial receptivity is the state in which a subject's endometrium is prepared for embryo implantation. This occurs in every menstrual cycle during a period called the window of implantation (WOI). In a natural cycle, ovulation occurs after a surge in LH, and the WOI is approximately 7 days after the LH surge (LH+7). In a hormone replacement therapy (HRT) cycle, the WOI is approximately 5 days after progesterone administration (P+5). These estimates provide some information regarding endometrial receptivity. However, the final answer regarding the state of the endometrium is generally determined by examining the endometrium itself.

[0059] Endometrial samples can be collected from a woman's uterine cavity either 5 days after progesterone administration in a hormone replacement therapy cycle (P+5) or 7 days after a surge in endogenous LH in a natural cycle (LH+7). The sample is then subjected to a molecular diagnostic tool to analyze the endometrial receptivity status. In some embodiments, the method for determining the endometrial state according to the present invention involves the molecular diagnostic tool analyzing the miRNA expression profile of a plasma sample. In some embodiments, the endometrial state determined by the plasma sample is validated against the endometrial sample.

[0060] The present invention provides a method for determining the state of the endometrium, and includes the following: (a) Perform an assay on a plasma sample to determine the miRNA expression profile in the blood. Here, the miRNA expression profile includes the expression levels of multiple miRNAs, for example, the miRNAs listed in Table 7. (b) Analyze the miRNA expression profile using a computer-based machine learning model to obtain a receptivity prediction score. Here, the receptivity prediction score classifies the endometrial state into a pre-receptive state, a receptive state, or a post-receptive state. In some embodiments, the receptivity prediction score is the probability of each endometrial state. In some embodiments, the receptivity prediction score is used internally to predict the most likely endometrial state by selecting the endometrial state with the highest receptivity prediction score (e.g., probability).

[0061] A pre-receptive state indicates that the endometrium is not yet ready to receive the embryo, and that embryo implantation may have occurred too early at the time of sample collection. A receptive state indicates that the endometrium is at the optimal stage for embryo implantation at the same time the sample was collected. A post-receptive state indicates that the endometrium has already passed the optimal stage for embryo implantation.

[0062] In some embodiments, the pre-receptive state indicates that the endometrium is not yet ready to receive the embryo approximately 5 days after progesterone administration. In some embodiments, the pre-receptive state indicates that the endometrium is not yet ready to receive the embryo approximately 7 days after the surge in endogenous LH (LH+7). In some embodiments, the receptive state indicates that the endometrium is at the optimal stage for embryo implantation approximately 5 days after progesterone administration. In some embodiments, the receptive state indicates that the endometrium is at the optimal stage for embryo implantation approximately 7 days after the surge in endogenous LH (LH+7). In some embodiments, the post-receptive state indicates that the endometrium has already passed the optimal stage for embryo implantation approximately 5 days after progesterone administration. In some embodiments, the post-receptive state indicates that the endometrium has already passed the optimal stage for embryo implantation approximately 7 days after the surge in endogenous LH (LH+7).

[0063] subject In some embodiments, the subject is a mammal. In some embodiments, the subject is a human. In some embodiments, the subject is a female mammal. The term “mammal” includes, for example, any mammal such as humans and non-human primates, cattle, goats, sheep, horses, pigs, dogs, etc. In some embodiments, the female mammal is a human female or a non-human primate. In some embodiments, the female mammal is a woman (i.e., a human female).

[0064] In some embodiments, the subjects are between 18 and 65 years old. In some embodiments, the subjects are between 18 and 50 years old. In some embodiments, the subjects are between 18 and 45 years old. In some embodiments, the subjects are between 18 and 40 years old. In some embodiments, the subjects are between 18 and 35 years old. In some embodiments, the subjects are between 18 and 30 years old. In some embodiments, the subjects are between 18 and 30 years old. In some embodiments, the subjects are between 21 and 45 years old. In some embodiments, the subjects are between 21 and 38 years old. In some embodiments, the subjects are at least 35 years old. In some embodiments, the subjects are at least 36 years old. In some embodiments, the subjects are at least 37 years old. In some embodiments, the subjects are at least 38 years old. In some embodiments, the subjects are at least 39 years old. In some embodiments, the subjects are at least 40 years old.

[0065] In some embodiments, the subject is 18 years old. In some embodiments, the subject is 19 years old. In some embodiments, the subject is 20 years old. In some embodiments, the subject is 21 years old. In some embodiments, the subject is 22 years old. In some embodiments, the subject is 23 years old. In some embodiments, the subject is 24 years old. In some embodiments, the subject is 25 years old. In some embodiments, the subject is 26 years old. In some embodiments, the subject is 27 years old. In some embodiments, the subject is 28 years old. In some embodiments, the subject is 29 years old. In some embodiments, the subject is 30 years old. In some embodiments, the subject is 31 years old. In some embodiments, the subject is 32 years old. In some embodiments, the subject is 33 years old. In some embodiments, the subject is 34 years old. In some embodiments, the subject is 35 years old. In some embodiments, the subject is 36 years old. In some embodiments, the subject is 37 years old. In some embodiments, the subject is 38 years old. In some embodiments, the subject is 39 years old. In some embodiments, the subject is 40 years old. In some embodiments, the subject is 41 years old. In some embodiments, the subject is 42 years old. In some embodiments, the subject is 43 years old. In some embodiments, the subject is 44 years old. In some embodiments, the subject is 45 years old. In some embodiments, the subject is 46 years old. In some embodiments, the subject is 47 years old. In some embodiments, the subject is 48 years old. In some embodiments, the subject is 49 years old. In some embodiments, the subject is 50 years old.

[0066] In some embodiments, the subject is approximately 18 years old. In some embodiments, the subject is approximately 19 years old. In some embodiments, the subject is approximately 20 years old. In some embodiments, the subject is approximately 21 years old. In some embodiments, the subject is approximately 22 years old. In some embodiments, the subject is approximately 23 years old. In some embodiments, the subject is approximately 24 years old. In some embodiments, the subject is approximately 25 years old. In some embodiments, the subject is approximately 26 years old. In some embodiments, the subject is approximately 27 years old. In some embodiments, the subject is approximately 28 years old. In some embodiments, the subject is approximately 29 years old. In some embodiments, the subject is approximately 30 years old. In some embodiments, the subject is approximately 31 years old. In some embodiments, the subject is approximately 32 years old. In some embodiments, the subject is approximately 33 years old. In some embodiments, the subject is approximately 34 years old. In some embodiments, the subject is approximately 35 years old. In some embodiments, the subject is approximately 36 years old. In some embodiments, the subject is approximately 37 years old. In some embodiments, the subject is approximately 38 years old. In some embodiments, the subject is approximately 39 years old. In some embodiments, the subject is approximately 40 years old. In some embodiments, the subject is approximately 41 years old. In some embodiments, the subjects are approximately 42 years old. In some embodiments, the subjects are approximately 43 years old. In some embodiments, the subjects are approximately 44 years old. In some embodiments, the subjects are approximately 45 years old. In some embodiments, the subjects are approximately 46 years old. In some embodiments, the subjects are approximately 47 years old. In some embodiments, the subjects are approximately 48 years old. In some embodiments, the subjects are approximately 49 years old. In some embodiments, the subjects are approximately 50 years old.

[0067] In some embodiments, subjects have a BMI of approximately 18 to approximately 30. In some embodiments, subjects have a BMI of approximately 18.5 to approximately 30. In some embodiments, subjects have a BMI of approximately 18.5 to approximately 24.9. In some embodiments, subjects have a BMI of approximately 18.5 to approximately 25. In some embodiments, subjects have a BMI of approximately 25 to approximately 29.9. In some embodiments, subjects have a BMI of approximately 25 to approximately 30. In some embodiments, subjects have a BMI greater than 18.5. In some embodiments, subjects have a BMI greater than 19. In some embodiments, subjects have a BMI greater than 20. In some embodiments, subjects have a BMI greater than 21. In some embodiments, subjects have a BMI greater than 22. In some embodiments, subjects have a BMI greater than 23. In some embodiments, subjects have a BMI greater than 24. In some embodiments, subjects have a BMI greater than 25. In some embodiments, subjects have a BMI greater than 26. In some embodiments, subjects have a BMI greater than 27. In some embodiments, the subject has a BMI greater than 28. In some embodiments, the subject has a BMI greater than 29. In some embodiments, the subject has a BMI greater than 30. In some embodiments, the subject has a BMI greater than 31. In some embodiments, the subject has a BMI greater than 32. In some embodiments, the subject has a BMI greater than 33. In some embodiments, the subject has a BMI greater than 34.

[0068] In some embodiments, the subject has a BMI of approximately 18.5. In some embodiments, the subject has a BMI of approximately 19. In some embodiments, the subject has a BMI of approximately 20. In some embodiments, the subject has a BMI of approximately 21. In some embodiments, the subject has a BMI of approximately 22. In some embodiments, the subject has a BMI of approximately 23. In some embodiments, the subject has a BMI of approximately 24. In some embodiments, the subject has a BMI of approximately 25. In some embodiments, the subject has a BMI of approximately 26. In some embodiments, the subject has a BMI of approximately 27. In some embodiments, the subject has a BMI of approximately 28. In some embodiments, the subject has a BMI of approximately 29. In some embodiments, the subject has a BMI of approximately 30. In some embodiments, the subject has a BMI of approximately 31. In some embodiments, the subject has a BMI of approximately 32. In some embodiments, the subject has a BMI of approximately 33. In some embodiments, the subject has a BMI of approximately 34.

[0069] In some embodiments, the subjects are overweight. In some embodiments, the subjects are underweight.

[0070] In some embodiments, the subject has a regular menstrual cycle. In some embodiments, the subject has an irregular menstrual cycle.

[0071] In some embodiments, the subject has recurrent implantation failure (RIF). In some embodiments, recurrent implantation failure is the failure of approximately 2 to 4 in vitro fertilization (IVF) attempts. In some embodiments, recurrent implantation failure is the failure of at least 2 in vitro fertilization (IVF) attempts. In some embodiments, recurrent implantation failure is the failure of at least 3 in vitro fertilization (IVF) attempts. In some embodiments, recurrent implantation failure is the failure of at least 4 in vitro fertilization (IVF) attempts.

[0072] In some embodiments, the subjects do not have ovulation disorders. In some embodiments, the subjects do not have one or more of the following: endometriosis, uterine fibroids, polyps, and hydrosalpinx. In some embodiments, the subjects do not have endometriosis. In some embodiments, the subjects do not have uterine fibroids. In some embodiments, the subjects do not have polyps. In some embodiments, the subjects do not have hydrosalpinx.

[0073] In some embodiments, the subject has an ovulation disorder. In some embodiments, the subject has one or more of the following: endometriosis, uterine fibroids, polyps, and hydrosalpinx. In some embodiments, the subject has endometriosis. In some embodiments, the subject has uterine fibroids. In some embodiments, the subject has polyps. In some embodiments, the subject has hydrosalpinx.

[0074] In some embodiments, the subject has at least one average-grade embryo. Embryo grading is a method known to those skilled in the art. Embryos can be graded as good (i.e., high quality), average, and poor grade. In some embodiments, embryo grading is based at least partially on the number of cells in the embryo and the appearance of the cells. In some embodiments, a good-quality embryo has many densely packed cells. In some embodiments, an average-quality embryo has cells that are not densely packed. In some embodiments, a poor-quality embryo has few cells and the cells are not densely packed.

[0075] In some embodiments, the subject has at least one good quality embryo. In some embodiments, the subject has more than one good quality embryo. In some embodiments, the subject has more than two good quality embryos. In some embodiments, the subject has at least two good quality embryos. In some embodiments, the subject has more than three good quality embryos. In some embodiments, the subject has at least three good quality embryos. In some embodiments, the subject has at least one medium-grade embryo. In some embodiments, the subject has more than one medium-grade embryo. In some embodiments, the subject has more than two medium-grade embryos. In some embodiments, the subject has at least two medium-grade embryos. In some embodiments, the subject has more than three average-grade embryos. In some embodiments, the subject has at least three medium-grade embryos. In some embodiments, the subject has at least three medium-grade embryos. In some embodiments, the subject has at least one low-grade embryo. In some embodiments, the subject has more than one low-grade embryo. In some embodiments, the subject has more than two low-grade embryos. In some embodiments, the subject has at least two low-grade embryos. In some embodiments, the subject has more than three low-grade embryos. In some embodiments, the subject has at least three low-grade embryos. The determination of embryo grade is known to those skilled in the art. In some embodiments, embryo grade is determined using methods known in the art. In some embodiments, embryo grade is determined using the method described in "Bouillon, C. et al., Obstetric and perinatal outcomes of singletons after single blastocyst transfer: is there any difference according to blastocyst morphology, RMBO, Vol. 35, Issue 2, 197-207, June 07, 2017".In some embodiments, embryo grade is determined using the method described in "Complete Guides to Embryo Grading and Success Rates" (remembryo.com / embryo-grading / published October 13, 2018 and updated February 17, 2023).

[0076] In some embodiments, good quality embryos are graded as 6AA, 6AB, 6BA, 5AA, 5BA, 4AA, 4AB, 4BA, 3AA, 3AB, or 3BA. In some embodiments, average quality embryos are graded as 6BB, 5BB, 4BB, or 3BB. In some embodiments, low-grade embryos are graded as 6CA, 6AC, 6CB, 6BC, 6CC, 5CA, 5AC, 5CB, 5BC, 5CC, 4CA, 4AC, 4CB, 4BC, 4CC, 3CA, 3AC, 3CB, 3BC, or 3CC.

[0077] In some embodiments, the subject has high-quality embryos. In some embodiments, the subject has few remaining high-quality embryos. In some embodiments, the few remaining high-quality embryos is one embryo. In some embodiments, the few remaining high-quality embryos is two embryos. In some embodiments, the few remaining high-quality embryos is three embryos. In some embodiments, the few remaining high-quality embryos is four embryos. In some embodiments, the few remaining high-quality embryos is five embryos.

[0078] In some embodiments, the subject has moderately developed embryos. In some embodiments, the subject has few remaining moderately developed embryos. In some embodiments, the few remaining moderately developed embryos is one embryo. In some embodiments, the few remaining moderately developed embryos is two embryos. In some embodiments, the few remaining moderately developed embryos is three embryos. In some embodiments, the few remaining moderately developed embryos is four embryos. In some embodiments, the few remaining moderately developed embryos is five embryos.

[0079] In some embodiments, the subject has low-grade embryos. In some embodiments, the subject has few remaining low-grade embryos. In some embodiments, the few remaining low-grade embryos is one embryo. In some embodiments, the few remaining low-grade embryos is two embryos. In some embodiments, the few remaining low-grade embryos is three embryos. In some embodiments, the few remaining low-grade embryos is four embryos. In some embodiments, the few remaining low-grade embryos is five embryos.

[0080] In some embodiments, the subject's endometrium is in a receptive state.

[0081] In some embodiments, the patient is 21-45 years old and has a BMI of 18.5 kg / m². 2 In some embodiments, the patient is 21-45 years old and has more than one good quality embryo. In some embodiments, the patient weighs 18.5 kg / m². 2 The patient has a BMI exceeding 18.5 kg / m² and one or more high-quality embryos. In some embodiments, the patient is 21-45 years old and weighs 18.5 kg / m². 2 The patient has a BMI exceeding 18.5 kg / m² and has one or more high-quality embryos. In some embodiments, the patient is 21-45 years old and has one or more high-quality embryos. In some embodiments, the patient has a BMI of 18.5 kg / m². 2 It exceeds 1.5 kg and has one or more high-quality embryos. In some embodiments, the patient is 21-45 years old and weighs 18.5 kg / m². 2 They have a BMI exceeding [a certain value] and possess one or more high-quality embryos.

[0082] In some embodiments, the patient is 35 years of age or older and has a BMI of 18.5 kg / m². 2 In some embodiments, the patient is 35 years of age or older and has more than one good quality embryo. In some embodiments, the patient weighs 18.5 kg / m². 2 The patient has a BMI exceeding 18.5 kg / m² and possesses more than one good quality embryo. In some embodiments, the patient is 35 years of age or older and weighs 18.5 kg / m². 2has a BMI exceeding 2 and has more than one good-quality embryo. In some embodiments, the patient is 35 years of age or older and has one or more good-quality embryos. In some embodiments, the patient has a BMI exceeding 18.5 kg / m 2 and has more than one good-quality embryo.

[0083] In some embodiments, the patient is 21 to 45 years of age and has a BMI exceeding 18.5 kg / m 2 In some embodiments, the patient is 21 to 45 years of age and has more than one medium-quality embryo. In some embodiments, the patient has a BMI exceeding 18.5 kg / m 2 and has more than one medium-quality embryo. In some embodiments, the patient is 21 to 45 years of age and has a BMI exceeding 18.5 kg / m 2 and has more than one medium-quality embryo. In some embodiments, the patient is 21 to 45 years of age and has one or more medium-quality embryos. In some embodiments, the patient has a BMI exceeding 18.5 kg / m 2 and has one or more medium-quality embryos. In some embodiments, the patient is 21 to 45 years of age and has a BMI exceeding 18.5 kg / m 2 and has one or more medium-quality embryos.

[0084] In some embodiments, the patient is 35 years of age or older and has a BMI exceeding 18.5 kg / m 2 In some embodiments, the patient is 35 years of age or older and has more than one medium-quality embryo. In some embodiments, the patient has a BMI exceeding 18.5 kg / m 2 and has more than one medium-quality embryo. In some embodiments, the patient is 35 years of age or older and has a BMI exceeding 18.5 kg / m 2 and has more than one medium-quality embryo. In some embodiments, the patient is 35 years of age or older and has one or more medium-quality embryos. In some embodiments, the patient has a BMI exceeding 18.5 kg / m 2The patient has a BMI exceeding 18.5 kg / m² and has one or more moderately sized embryos. In some embodiments, the patient is 35 years of age or older and weighs 18.5 kg / m². 2 The patient has a BMI exceeding [a certain value] and possesses one or more moderately sized embryos.

[0085] In some embodiments, the patient is 21-45 years old and has a BMI of 18.5 kg / m². 2 In some embodiments, the patient is 21-45 years old and has more than one low-grade embryo. In some embodiments, the patient weighs 18.5 kg / m². 2 The patient has a BMI exceeding 18.5 kg / m² and more than one low-grade embryo. In some embodiments, the patient is 21-45 years old and weighs 18.5 kg / m². 2 The patient has a BMI exceeding a certain value and has one or more low-grade embryos. In some embodiments, the patient is 21-45 years old and has one or more low-grade embryos. In some embodiments, the patient weighs 18.5 kg / m². 2 The patient has a BMI exceeding 18.5 kg / m² and one or more low-grade embryos. In some embodiments, the patient is 21-45 years old and weighs 18.5 kg / m². 2 The patient has a BMI exceeding a certain threshold and possesses one or more low-grade embryos.

[0086] In some embodiments, the patient is 35 years of age or older and has a BMI of 18.5 kg / m². 2 In some embodiments, the patient is 35 years of age or older and has more than one low-grade embryo. In some embodiments, the patient weighs 18.5 kg / m². 2 The patient has a BMI exceeding 18.5 kg / m² and has more than one low-grade embryo. In some embodiments, the patient is 35 years of age or older and weighs 18.5 kg / m². 2 The patient has a BMI exceeding a certain value and has one or more low-grade embryos. In some embodiments, the patient is 35 years of age or older and has one or more low-grade embryos. In some embodiments, the patient weighs 18.5 kg / m². 2 The patient has a BMI exceeding 18.5 kg / m² and one or more low-grade embryos. In some embodiments, the patient is 35 years of age or older and weighs 18.5 kg / m². 2The patient has a BMI exceeding a certain threshold and possesses one or more low-grade embryos.

[0087] Sample collection In some embodiments, a blood sample is taken from the subject. In some embodiments, the sample is a plasma sample. In some embodiments, the blood sample is taken during a mock hormone replacement therapy cycle. In some embodiments, the blood sample is taken 4 to 6 days after progesterone administration during a mock hormone replacement therapy cycle. In some embodiments, the blood sample is taken 4 to 6 days after progesterone administration. In some embodiments, the blood sample is taken approximately 4 to 6 days after progesterone administration. In some embodiments, the blood sample is taken 5 days after progesterone administration during a mock hormone replacement therapy cycle. In some embodiments, the blood sample is taken 5 days after progesterone administration. In some embodiments, the blood sample is taken approximately 5 days after progesterone administration. In some embodiments, the blood sample is taken 6 days after progesterone administration during a mock hormone replacement therapy cycle. In some embodiments, the blood sample is taken 6 days after progesterone administration. In some embodiments, the blood sample is taken approximately 6 days after progesterone administration. In some embodiments, the blood sample is taken 4 days after progesterone administration during a mock hormone replacement therapy cycle. In some embodiments, blood samples are taken 4 days after progesterone administration. In some embodiments, blood samples are taken approximately 4 days after progesterone administration.

[0088] In some embodiments, blood samples are taken during a hormone replacement therapy (HRT) cycle. In some embodiments, blood samples are taken approximately 4–6 days after progesterone administration during an HRT cycle. In some embodiments, blood samples are taken approximately 5 days after progesterone administration during an HRT cycle. In some embodiments, blood samples are taken approximately 6 days after progesterone administration during an HRT cycle. In some embodiments, blood samples are taken approximately 4 days after progesterone administration during an HRT cycle. In some embodiments, blood samples are taken during a hormone replacement therapy (HRT) cycle. In some embodiments, blood samples are taken 4–6 days after progesterone administration during an HRT cycle. In some embodiments, blood samples are taken 5 days after progesterone administration during an HRT cycle. In some embodiments, blood samples are taken 6 days after progesterone administration during an HRT cycle. In some embodiments, blood samples are taken 4 days after progesterone administration during an HRT cycle.

[0089] In some embodiments, blood samples are taken after a surge in luteinizing hormone (LH). In some embodiments, blood samples are taken about 6-8 days after a surge in LH. In some embodiments, blood samples are taken about 6 days after a surge in LH. In some embodiments, blood samples are taken about 7 days after a surge in LH. In some embodiments, blood samples are taken about 8 days after a surge in LH. In some embodiments, blood samples are taken 6-8 days after a surge in LH. In some embodiments, blood samples are taken 6 days after a surge in LH. In some embodiments, blood samples are taken 7 days after a surge in LH. In some embodiments, blood samples are taken 8 days after a surge in LH.

[0090] In some embodiments, blood samples are collected after administration of human chorionic gonadotropin (hCG). In some embodiments, blood samples are collected approximately 4–7 days after hCG administration during the HRT cycle. In some embodiments, blood samples are collected approximately 4 days after hCG administration during the HRT cycle. In some embodiments, blood samples are collected approximately 5 days after hCG administration during the HRT cycle. In some embodiments, blood samples are collected approximately 6 days after hCG administration during the HRT cycle. In some embodiments, blood samples are collected approximately 7 days after hCG administration during the HRT cycle. In some embodiments, blood samples are collected 4–7 days after hCG administration during the HRT cycle. In some embodiments, blood samples are collected 4 days after hCG administration during the HRT cycle. In some embodiments, blood samples are collected 5 days after hCG administration during the HRT cycle. In some embodiments, blood samples are collected 6 days after hCG administration during the HRT cycle. In some embodiments, blood samples are collected 7 days after hCG administration during the HRT cycle.

[0091] In some embodiments, blood samples are collected approximately 4 and 5 days after the start of progesterone administration. In some embodiments, blood samples are collected approximately 4 and 5 days after the start of progesterone administration during the assisted reproductive technology cycle.

[0092] In some embodiments, blood samples are taken approximately 4 and 5 days after the start of progesterone administration. In some embodiments, blood samples are taken approximately 4 and 5 days after the start of progesterone administration during the hormone replacement therapy cycle. In some embodiments, blood samples are taken approximately 6 and 7 days after a surge in LH is detected. In some embodiments, blood samples are taken approximately 6 and 7 days after hCG administration.

[0093] In some embodiments, approximately 5 mL to approximately 10 mL of blood is collected. In some embodiments, approximately 1 mL to approximately 10 mL of blood is collected. In some embodiments, approximately 0.5 mL of blood is collected. In some embodiments, approximately 1 mL of blood is collected. In some embodiments, approximately 2 mL of blood is collected. In some embodiments, approximately 3 mL of blood is collected. In some embodiments, approximately 4 mL of blood is collected. In some embodiments, approximately 5 mL of blood is collected. In some embodiments, approximately 6 mL of blood is collected. In some embodiments, approximately 7 mL of blood is collected. In some embodiments, approximately 8 mL of blood is collected. In some embodiments, approximately 9 mL of blood is collected. In some embodiments, approximately 10 mL of blood is collected. In some embodiments, approximately 11 mL of blood is collected. In some embodiments, approximately 12 mL of blood is collected.

[0094] In some embodiments, blood samples are collected with a needle size that reduces hemolysis. In some embodiments, blood samples are collected using a needle of at least 21 gauge (21G). In some embodiments, hemolysis is monitored during sample collection. In some embodiments, hemolysis is monitored during blood pretreatment.

[0095] In some embodiments, blood sample pretreatment is performed within 30 minutes of sample collection. In some embodiments, blood sample pretreatment is performed within 1 hour of sample collection. In some embodiments, blood sample pretreatment is performed within 2 hours of sample collection. In some embodiments, pretreatment includes the following: i) Invert the blood collection tube at least five times. ii) Centrifuge the sample at room temperature in a centrifuge at 1200 g for 10 minutes. iii) Transfer the sample to a new tube and centrifuge at 12000g for 10 minutes.

[0096] Methods for short-term or long-term storage of blood are well known to technicians in the art. In some embodiments, blood is stored before processing. In some embodiments, blood is stored after processing. In some embodiments, processed blood samples are stored at room temperature. In some embodiments, processed blood samples are stored at -80°C. In some embodiments, processed blood samples are stored at approximately 1.7°C to approximately 3.3°C. In some embodiments, processed blood samples are stored at 0°C. In some embodiments, blood samples are pre-treated and then stored at -80°C. In some embodiments, blood samples are pre-treated and then stored at -80°C for up to one year. In some embodiments, blood samples are pre-treated and then stored at approximately 1.7°C to approximately 3.3°C. In some embodiments, blood samples are pre-treated and then stored at approximately 1.7°C to approximately 3.3°C for up to one year. In some embodiments, blood samples are pre-treated and then stored at 0°C. In some embodiments, blood samples are pre-treated and then stored at 0°C for up to one year.

[0097] In some embodiments, a blood sample is collected. In some embodiments, the blood is collected in a plasma preparation tube (PPT). In some embodiments, the blood is collected in a Streck tube. In some embodiments, the blood is collected in an RNA Complete BCT tube (Streck). In some embodiments, the blood is collected in a PAXgene Blood ccfDNA tube (Qiagen). In some embodiments, the blood is collected in a DNA / RNA Shield blood collection tube (Zymo). In some embodiments, the blood is collected in a cf-DNA / cf-RNA storage tube (Norgen Biotek). In some embodiments, the blood is collected in a Lbgard blood collection tube (Biomatrica). In some embodiments, the blood is collected in a STASIS DNA / RNA blood collection tube (MagBio Genomics).

[0098] In some embodiments, an intrauterine fluid sample is collected from the subject. In some embodiments, the intrauterine fluid sample is collected during a mock hormone replacement therapy cycle. In some embodiments, the intrauterine fluid sample is collected 4 to 6 days after progesterone administration during a mock hormone replacement therapy cycle. In some embodiments, the intrauterine fluid sample is collected 4 to 6 days after progesterone administration. In some embodiments, the intrauterine fluid sample is collected approximately 4 to 6 days after progesterone administration. In some embodiments, the intrauterine fluid sample is collected 5 days after progesterone administration during a mock hormone replacement therapy cycle. In some embodiments, the intrauterine fluid sample is collected 5 days after progesterone administration. In some embodiments, the intrauterine fluid sample is collected approximately 5 days after progesterone administration. In some embodiments, the intrauterine fluid sample is collected 6 days after progesterone administration during a mock hormone replacement therapy cycle. In some embodiments, the intrauterine fluid sample is collected 6 days after progesterone administration. In some embodiments, the intrauterine fluid sample is collected approximately 6 days after progesterone administration. In some embodiments, an intrauterine fluid sample is collected 4 days after progesterone administration during a mock hormone replacement therapy cycle. In some embodiments, an intrauterine fluid sample is collected 4 days after progesterone administration. In some embodiments, an intrauterine fluid sample is collected approximately 4 days after progesterone administration.

[0099] In some embodiments, an endometrial fluid (UTF) sample is collected during a hormone replacement therapy (HRT) cycle. In some embodiments, an endometrial fluid (UTF) sample is collected during an HRT cycle approximately 4–6 days after progesterone administration. In some embodiments, an endometrial fluid (UTF) sample is collected during an HRT cycle approximately 5 days after progesterone administration. In some embodiments, an endometrial fluid (UTF) sample is collected during an HRT cycle approximately 6 days after progesterone administration. In some embodiments, an endometrial fluid (UTF) sample is collected during an HRT cycle approximately 4 days after progesterone administration. In some embodiments, an endometrial fluid (UTF) sample is collected during a hormone replacement therapy (HRT) cycle. In some embodiments, an endometrial fluid (UTF) sample is collected during an HRT cycle 4–6 days after progesterone administration. In some embodiments, an endometrial fluid (UTF) sample is collected during an HRT cycle 5 days after progesterone administration. In some embodiments, an endometrial fluid (UTF) sample is collected during an HRT cycle 6 days after progesterone administration. In some embodiments, an endometrial fluid (UTF) sample is collected during an HRT cycle 4 days after progesterone administration.

[0100] In some embodiments, the uterine fluid sample is collected after a surge in luteinizing hormone (LH). In some embodiments, the uterine fluid sample is collected approximately 6-8 days after the surge in LH. In some embodiments, the uterine fluid sample is collected approximately 6 days after the surge in LH. In some embodiments, the uterine fluid sample is collected approximately 7 days after the surge in LH. In some embodiments, the uterine fluid sample is collected approximately 8 days after the surge in LH. In some embodiments, the uterine fluid sample is collected 6 days after the surge in LH. In some embodiments, the uterine fluid sample is collected 7 days after the surge in LH. In some embodiments, the uterine fluid sample is collected 8 days after the surge in LH.

[0101] In some embodiments, an intrauterine fluid sample is collected after administration of human chorionic gonadotropin (hCG). In some embodiments, an intrauterine fluid sample is collected during the HRT cycle approximately 4–7 days after hCG administration. In some embodiments, an intrauterine fluid sample is collected during the HRT cycle approximately 4 days after hCG administration. In some embodiments, an intrauterine fluid sample is collected during the HRT cycle approximately 5 days after hCG administration. In some embodiments, an intrauterine fluid sample is collected during the HRT cycle approximately 6 days after hCG administration. In some embodiments, an intrauterine fluid sample is collected during the HRT cycle approximately 7 days after hCG administration. In some embodiments, an intrauterine fluid sample is collected during the HRT cycle approximately 4–7 days after hCG administration. In some embodiments, an intrauterine fluid sample is collected during the HRT cycle 4 days after hCG administration. In some embodiments, an intrauterine fluid sample is collected during the HRT cycle 5 days after hCG administration. In some embodiments, an intrauterine fluid sample is collected during the HRT cycle 6 days after hCG administration. In some embodiments, an intrauterine fluid sample is collected during the HRT cycle 7 days after hCG administration.

[0102] In some embodiments, intrauterine fluid samples are collected approximately 4 and 5 days after the start of progesterone administration. In some embodiments, intrauterine fluid samples are collected approximately 4 and 5 days after the start of progesterone administration during the assisted reproductive technology cycle.

[0103] In some embodiments, uterine fluid samples are collected approximately 4 and 5 days after the start of progesterone administration. In some embodiments, uterine fluid samples are collected approximately 4 and 5 days after the start of progesterone administration during the HRT cycle. In some embodiments, uterine fluid samples are collected approximately 6 and 7 days after a surge in LH is detected. In some embodiments, uterine fluid samples are collected approximately 6 and 7 days after hCG administration. In some embodiments, uterine fluid is obtained by washing. In some embodiments, uterine fluid is obtained by washing after washing the uterus with phosphate-buffered saline (PBS). In some embodiments, uterine fluid is collected using an intrauterine artificial insemination catheter inserted into the uterine cavity through the cervix.

[0104] Library preparation miRNAs in blood samples can be extracted and concentrated using methods known in the art. The miRNA concentrate can be stored at -80°C. The quantity and quality of miRNAs can be analyzed using methods known in the art. For example, miRNAs can be analyzed using a commercially available Agilent bioanalyzer.

[0105] cDNA can be synthesized from the extracted and concentrated miRNA in a reverse transcription reaction, and a qPCR reaction can be performed to quantify the miRNA expression level. Therefore, in some embodiments, the miRNA expression profile is optionally determined by qPCR using one or more miRNA profiling chips disclosed herein.

[0106] In some embodiments, cDNA synthesis is carried out using a universal reverse transcription primer disclosed in U.S. Patent No. 10,590,478, which is incorporated herein by reference.

[0107] In some embodiments, the library for sequencing is prepared using methods known in the art. In some embodiments, the library is prepared using two-adaptor ligation. In some embodiments, the library is prepared using randomized adapters. In some embodiments, the library is prepared using single-adaptor ligation and cyclic ligation. In some embodiments, the library is prepared using unique molecular identifiers. In some embodiments, the library is prepared using polyadenylation and template switching. In some embodiments, the library is prepared using sequencing of hybridization probes.

[0108] In some embodiments, the library is prepared using a commercially available protocol. In some embodiments, the library is one of the following: Small RNA-Seq Library Prep Kit (Lexogen GmBH), Small RNA Library Prep Kit (Morgen Biotek Corp.), TruSeq Small RNA Library Prep Kit (Illumina), TailorMix miRNA Sample Preparation Kit (SeqMatic), NEBNExt Multiplex Small RNA Library Prep Set (New England Biolabs), CleanTag Small RNA Library Prep Kit (TriLink BioTechnologies, In.), ScriptMiner Library preparation (Cambio), NEXTflex Small RNA Sequencing Kit (PerkinElmer), RealSeq-AC Kit (Somagenics), RealSeq-biofluids Kit (Somagenics), SMARTer microRNA-Seq Kit (Takara Bio), TrueWuant Small RNA Seq Kit for Ultra Low Input (GenXPro GmBH), QIAseq miRNA Library Kit (Qiagen), SMARTer The assay is prepared using one or more of the following: smRNA-seq Kit (Takara Bio), CATS Small RNA-seq Kit (Biagenode), HTG EdgeSeq miRNA Whole Transcriptome Assay (HTG Molecular Diagonists), or FirePlex miRNA assays (Abcam).

[0109] In some embodiments, the library is prepared using the iCatcher (CatchGene) platform. In some embodiments, the library is prepared using the QIAsymphony (Qiagen) platform. In some embodiments, the library is prepared using the KingFisher (ThermoFisher) platform.

[0110] In some embodiments, the library is prepared using the method described in the examples.

[0111] miRNA sequencing miRNA expression levels can be analyzed by quantitative methods known in the art, including qPCR, sequencing, microarrays, or RNA-DNA hybrid capture techniques. In some embodiments, miRNA expression levels are analyzed using qPCR. In some embodiments, miRNA expression levels are measured using qRT-PCR. In some embodiments, miRNA expression levels are analyzed using next-generation sequencing (NGS). In some embodiments, one or more miRNA profiling chips targeting 281 miRNAs can be used to facilitate the analysis. In some embodiments, one or more chips additionally target specific RNA sequences, such as 18s rRNA, which can be used as endogenous controls for miRNA expression analysis. In some embodiments, miRNA expression is measured by OpenArray. In some embodiments, miRNA expression is measured by microarray.

[0112] In some embodiments, miRNA expression is determined using next-generation sequencing. Methods of next-generation sequencing are known to those skilled in the art. In some embodiments, miRNA expression is determined using NextSeq 550. In some embodiments, miRNA expression is determined using iSeq 100. In some embodiments, miRNA expression is determined using MiniSeq. In some embodiments, miRNA expression is determined using the MiniSeq series. In some embodiments, miRNA expression is determined using NextSeq 1000. In some embodiments, miRNA expression is determined using NextSeq 2000. In some embodiments, miRNA expression is determined using NovaSeq. In some embodiments, miRNA expression is determined using DNBSEQ G99. In some embodiments, miRNA expression is determined using DNBSEQ G50.

[0113] In some embodiments, the presence of miRNA is determined. In some embodiments, the absence of miRNA is determined. In some embodiments, the presence of miRNA indicates the pre-receptive stage. In some embodiments, the absence of miRNA indicates the pre-receptive stage. In some embodiments, the presence of miRNA indicates the post-receptive stage. In some embodiments, the absence of miRNA indicates the post-receptive stage. In some embodiments, the presence of miRNA indicates the receptive stage. In some embodiments, the absence of miRNA indicates the receptive stage.

[0114] In some embodiments, the presence of one or more miRNAs is determined. In some embodiments, the deletion of one or more miRNAs is determined. In some embodiments, the presence of one or more miRNAs indicates a pre-receptive stage. In some embodiments, the deletion of one or more miRNAs indicates a pre-receptive stage. In some embodiments, the presence of one or more miRNAs indicates a post-receptive stage. In some embodiments, the deletion of one or more miRNAs indicates a post-receptive stage. In some embodiments, the presence of one or more miRNAs indicates a receptive stage. In some embodiments, the deletion of one or more miRNAs indicates a receptive stage.

[0115] The present invention provides a method for determining the miRNA expression profile of a blood sample. The method generally comprises (i) obtaining or having obtained a blood sample from a subject, and (ii) performing an assay to determine the miRNA expression profile of the blood sample. Here, the miRNA expression profile includes the expression levels of several miRNAs, for example, the 281 miRNAs provided in Table 7.

[0116] miRNA analysis method and its use for determining endometrial implantation potential In some embodiments of the present invention, miRNA expression profiles can be used to generate implantation potential prediction scores using computer-based miRNA analysis methods. In some embodiments, the implantation potential prediction score classifies the endometrial state (also referred herein to as endometrial state or endometrial stage) into one of four states: pre-receptive state, receptive state, post-receptive state (short window), or post-receptive state (average window).

[0117] In some embodiments, the pre-receptive state indicates that the subject is receptive to embryo transfer approximately 24 hours after sample acquisition. In some embodiments, the receptive state indicates that the subject is receptive to transfer at the time of sample acquisition. In some embodiments, the post-receptive state (short window) indicates that the subject is receptive to embryo transfer approximately 12 hours before sample acquisition. In some embodiments, the post-receptive state (average window) indicates that the subject is receptive to embryo transfer approximately 24 hours before sample acquisition.

[0118] In some embodiments, computer-based miRNA analysis methods are mathematical predictive classifiers that use miRNA expression data to learn to distinguish between classes according to different implantation potential states.

[0119] In some embodiments, raw data of miRNA expression levels is split into a training set and a validation set to build a machine learning model. In some embodiments, the training set is used to train a predictive classifier, and the validation set is used to evaluate and improve the performance of the predictive classifier. In some embodiments, one or more of the following steps are performed to build and validate the machine learning model: data normalization, data scaling, data transformation, predictive modeling, and cross-validation.

[0120] In some embodiments, the data are normalized through a read ratio, which is obtained by dividing the number of individual miRNA reads by the total number of RNA reads (see, for example, Yu et al., "Analysis of microRNA expression profile by small RNA sequencing in Down syndrome fetuses." Int. J. Mol. Med. Vol. 32 Issue 5; 1115-1125; September 18, 2013). In some embodiments, the range of values ​​can be standardized so that the data have a mean of zero and unit variance.

[0121] In some embodiments, an Optimal Receptivity Assay (ORA) is constructed by performing one or more of the following steps: quality control, data normalization (converting the number of miRNA sequencing reads to a ratio of the total number of reads), target eligibility (by selecting miRNA targets that have a stable expression pattern and reliable ratio values ​​across different patient samples), data transformation (converting the miRNA ratios to a processed format for use in a modeling system), predictive modeling (establishing an ORA model), and cross-validation (assessing accuracy by validating the predictive model with independent datasets).

[0122] In some embodiments, quality control is performed on miRNA datasets for ORA analysis. In some embodiments, quality control includes measuring the amount of miRNA used for sequencing, as well as the total number of reads from sequencing, total miRNA reads, spike-in control reads, and the number of detectable miRNAs. In some embodiments, quality control measures the total number of reads in sequencing. In some embodiments, quality control measures the total number of miRNA reads in sequencing. In some embodiments, quality control measures the number of spike-in control reads in sequencing. In some embodiments, quality control measures the number of detectable miRNAs in sequencing.

[0123] In some embodiments, the method includes performing validation of a dataset. In some embodiments, validation of a dataset using ORA analyzes one or more of the following: accuracy, reproducibility, repeatability, limit of blank (LoB), and interference. In some embodiments, the dataset is validated by measuring one or more of the following: accuracy, sensitivity, and specificity.

[0124] In some embodiments, the method uses logistic regression.

[0125] In some embodiments, the method includes cross-validation. In some embodiments, the method includes 10-fold cross-validation.

[0126] In some embodiments, pregnancy rates can be used to evaluate the predictions of computer-based miRNA analysis models.

[0127] In some embodiments, after validation and refinement, a computer-based miRNA analysis model is generated. The model receives a blood sample from a subject and classifies the subject's endometrial state.

[0128] In some embodiments, the predictive model includes method steps for predicting the state before the receptive phase. In some embodiments, the predictive model includes method steps for predicting the receptive state. In some embodiments, the predictive model includes method steps for predicting the state after the receptive phase.

[0129] In some embodiments, the method has a predictive accuracy of at least 90%. In some embodiments, the method has a predictive accuracy of at least 91%. In some embodiments, the method has a predictive accuracy of at least 92%. In some embodiments, the method has a predictive accuracy of at least 93%. In some embodiments, the method has a predictive accuracy of at least 94%. In some embodiments, the method has a predictive accuracy of at least 95%. In some embodiments, the method has a predictive accuracy of at least 96%. In some embodiments, the method has a predictive accuracy of at least 97%. In some embodiments, the method has a predictive accuracy of at least 98%. In some embodiments, the method has a predictive accuracy of at least 99%.

[0130] In some embodiments, the method has a predictive accuracy of about 90%. In some embodiments, the method has a predictive accuracy of about 91%. In some embodiments, the method has a predictive accuracy of about 92%. In some embodiments, the method has a predictive accuracy of about 93%. In some embodiments, the method has a predictive accuracy of about 94%. In some embodiments, the method has a predictive accuracy of about 95%. In some embodiments, the method has a predictive accuracy of about 96%. In some embodiments, the method has a predictive accuracy of about 97%. In some embodiments, the method has a predictive accuracy of about 98%. In some embodiments, the method has a predictive accuracy of about 99%. In some embodiments, the method has a predictive accuracy of about 100%. In some embodiments, the method has a predictive accuracy of about 98.1%.

[0131] Analysis of miRNA expression profiles to determine endometrial implantation potential This invention determines the microRNA (miRNA) expression profile of a blood sample. miRNAs are small molecules in the body that influence various cellular processes such as cell growth, development, and metabolism. In some embodiments, miRNAs are cell-free miRNAs in the blood. In some embodiments, the expression signature of miRNAs can reflect different physiological states of the endometrium in response to changes in factors such as age, BMI, AMH (anti-mullerian hormone), lifestyle, and environmental influences. In some embodiments, miRNAs have regulatory functions throughout the entire process of embryo implantation and pregnancy (e.g., tissue development, embryo attachment and growth, pre-eclampsia, etc.). In some embodiments, miRNAs that regulate these endometrial states can be measured in the bloodstream. For example, miRNAs in the bloodstream have been found to regulate numerous immune mechanisms during pregnancy and are important physiological factors for cell growth and angiogenesis. Through the regulation of these mechanisms, miRNAs can influence the endometrial environment, developmental processes, and ultimately, pregnancy outcomes. In some embodiments, the methods described herein identify blood-based miRNA biomarkers that accurately reflect the state of the endometrium of a subject.

[0132] In some embodiments, the miRNA expression profile includes expression levels of multiple miRNAs. In some embodiments, the miRNA expression profile includes expression levels of multiple miRNAs, for example, at least 10, 25, 50, 75, 100, 125, 150, 200, 250, or 300 miRNAs, all of which may be involved in regulating endometrial implantation ability. In some embodiments, the miRNA expression profile includes expression levels of approximately 50 to approximately 300 miRNAs. In some embodiments, the miRNA expression profile includes expression levels of approximately 50 miRNAs. In some embodiments, the miRNA expression profile includes expression levels of approximately 75 miRNAs. In some embodiments, the miRNA expression profile includes expression levels of approximately 100 miRNAs. In some embodiments, the miRNA expression profile includes expression levels of approximately 125 miRNAs. In some embodiments, the miRNA expression profile includes expression levels of approximately 150 miRNAs. In some embodiments, the miRNA expression profile includes expression levels of approximately 175 miRNAs. In some embodiments, the miRNA expression profile includes expression levels of approximately 200 miRNAs. In some embodiments, the miRNA expression profile includes the expression levels of approximately 225 miRNAs. In some embodiments, the miRNA expression profile includes the expression levels of approximately 250 miRNAs. In some embodiments, the miRNA expression profile includes the expression levels of approximately 275 miRNAs. In some embodiments, the miRNA expression profile includes the expression levels of approximately 300 miRNAs.

[0133] In some embodiments, the method includes determining the expression level of at least one miRNA. In some embodiments, the method includes determining at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 7 5, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 20 0, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,This includes determining the expression levels of miRNAs 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, or 281.

[0134] In some embodiments, the method selects at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 7 from the miRNAs provided in Table 7. 2, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 11 0, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141 ,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 2 04, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 23 5, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266,This includes determining the expression levels of miRNAs 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, or 281.

[0135] In some embodiments, the present invention provides a selection of 281 miRNAs whose expression levels have been suggested to be involved in regulating endometrial receptivity. These 281 miRNAs were selected by first identifying genes involved in reproductive disorders from the Human Disease Ontology database, and then selecting potential regulatory miRNAs using miRTARBase, TargetScan, and miRDB.

[0136] In some embodiments, to determine the state of the endometrium, the method according to the present invention comprises performing an assay to determine the miRNA expression profile of a blood sample, the miRNA expression profile comprising the expression levels of 281 miRNAs shown in Table 7.

[0137] In some embodiments, the miRNA is selected from let-7a, miR-21, miR-25, miR-103, miR-192, miR-181a, miR-24, miR-210, miR-25, miR-16, or any combination thereof. In some embodiments, the miRNA is selected from miR-25, miR-27a, miR-31, miR-93, miR-106b, miR-146a, miR-152, miR-155, or any combination thereof.

[0138] In some embodiments, hsa-let-7b-3p, hsa-let-7d-3p, hsa-let-7i-5p, hsa-miR-1-3p, hsa-miR-100-5p, hsa-miR-106b-3p, hsa-miR-10a-3p, hsa-miR-10a-5p, hsa-miR-10b-5p, hsa-miR-1180-3p, hsa-miR-1255b-5p, hsa-miR-125a-3p, hsa-miR-125b-5p, hsa-miR-1260a, hsa-miR-1260b, hsa-miR-1270, hsa-miR-128-3p, hsa-miR-1285-3p, hsa-miR-1287-5p, hsa-miR-1292-5p, hsa-miR-1294, hsa-miR-1298-5p, hsa-miR-1301-3p, hsa-miR-1303, hsa-miR-1304-3p, hsa-miR-1306-5p, hsa-miR-1307-3p, hsa-miR-130b-3p, hsa-miR-139-3p, hsa-miR-140-5p, hsa-miR-141-3p, hsa-miR-142-3p, hsa-miR-143-3p, hsa-miR-145-5p, hsa-miR-1469, hsa-miR-146a-5p, hsa-miR-146b-5p, hsa-miR-148a-3p, hsa-miR-151b, hsa-miR-1538, hsa-miR-155-5p, hsa-miR-15b-3p, hsa-miR-17-3p, hsa-miR-17-5p, hsa-miR-181b-5p, hsa-miR-183-3p, hsa-miR-183-5p, hsa-miR-186-5p, hsa-miR-18a-5p, hsa-miR-1908-5p, hsa-miR-192-5p, hsa-miR-193a-5p, hsa-miR-193b-5p, hsa-miR-194-5p, hsa-miR-196b-5p, hsa-miR-197-3p, hsa-miR-199a-3p, hsa-miR-199b-3p, hsa-miR-19b-3p, hsa-miR-200c-3p, hsa-miR-204-3p, hsa-miR-205-5p, hsa-miR-210-3p, hsa-miR-2110, hsa-miR-22-3phsa-miR-22-5p, hsa-miR-221-3p, hsa-miR-223-5p, hsa-miR-23b-3p, hsa-miR-24-3p, hsa-miR-25-5p, hsa-miR-27a-3p, hsa-miR-27b-3p, hsa-miR-27b-5p, hsa-miR-28-3p, hsa-miR-29b-3p, hsa-miR-29c-3p, hsa-miR-30a-3p, hsa-miR-30a-5p, hsa-miR-30d-5p, hsa-miR-30e-3p, hsa-miR-31- 5p, hsa-miR-3135b, hsa-miR-3143, hsa-miR-32-3p, hsa-miR-32-5p, hsa-miR-320d, hsa-miR-324-5p, hsa-miR-328-3p, hsa-miR-335-5p, hsa-miR- 338-3p, hsa-miR-339-3p, hsa-miR-340-5p, hsa-miR-345-5p, hsa-miR-34a-5p, hsa-miR-3529-3p, hsa-miR-3605-3p, hsa-miR-3605-5p, hsa-miR-3 61-5p, hsa-miR-3612, hsa-miR-3615, hsa-miR-362-5p, hsa-miR-363-3p, hsa-miR-365a-3p, hsa-miR-365b-3p, hsa-miR-3688-3p, hsa-miR-374a- 5p, hsa-miR-376a-3p, hsa-miR-376c-3p, hsa-miR-382-5p, hsa-miR-383-3p, hsa-miR-3913-3p, hsa-miR-3913-5p, hsa-miR-3960, hsa-miR-3972, h sa-miR-421, hsa-miR-423-3p, hsa-miR-424-3p, hsa-miR-424-5p, hsa-miR-432-5p, hsa-miR-4429, hsa-miR-450a-5p, hsa-miR-4510, hsa-miR-454 -3p、hsa-miR-4644、hsa-miR-4732-5p、hsa-miR-483-5p、hsa-miR-497-5p、hsa-miR-5010-5p、hsa-miR-502-3p、hsa-miR-503-5p、hsa-miR-505-3p、hsa-miR-505-5p、hsa-miR-5189-3p、hsa-miR-532-3p、hsa-miR-542-3p、hsa-miR-548am-5p、hsa-miR-548c-5p、hsa-miR-548h-3p、hsa-miR-548o-5p、hsa-miR-548z、hsa-miR-550a-5p、hsa-miR-5585-5p、hsa-miR-574-3p、hsa-miR-576-5p、hsa-miR-589-5p、hsa-miR-590-3p、hsa-miR-598-3p、hsa-miR-625-3p、hsa-miR-625-5p、hsa-miR-628-3p、hsa-miR-642a-3p、hsa-miR-642a-5p、hsa-miR-642b-3p、hsa-miR-642b-5p、hsa-miR-652-3p、hsa-miR-657、hsa-miR-660-5p、hsa-miR-663b、hsa-miR-664a-5p、hsa-miR-671-5p、hsa-miR-6815-5p、hsa-miR-7-5p、hsa-miR-744-5p、hsa-miR-766-3p、hsa-miR-769-5p、hsa-miR-877-5p、hsa-miR-885-3p、hsa-miR-885-5p、hsa-miR-93-3p、hsa-miR-941、hsa-miR-95-3p、hsa-miR-96-5p、hsa-miR-99a-5p、hsa-miR-99b-5p、hsa-let-7a-5p、hsa-let-7b-5p、hsa-let-7c-5p、hsa-let-7d-5p、hsa-let-7e-5p、hsa-let-7f-5p、hsa-let-7g-5p、hsa-miR-103a-3p、hsa-miR-103b、hsa-miR-106a-5p、hsa-miR-106b-5p、hsa-miR-107、hsa-miR-12116、hsa-miR-122-5p、hsa-miR-122b-3p、hsa-miR-1246、hsa-miR-125a-5p、hsa-miR-126-3p、hsa-miR-126-5p、hsa-miR-1290、hsa-miR-130a-3p、hsa-miR-132-3p、hsa-miR-139-5p、hsa-miR-140-3p、hsa-miR-142-5p、hsa-miR-144-3p、hsa-miR-150-3p、hsa-miR-150-5p、h sa-miR-151a-3p、hsa-miR-151a-5p、hsa-miR-15a-5p、hsa-miR-15b-5p、h sa-miR-16-5p、hsa-miR-181a-2-3p、hsa-miR-181a-3p、hsa-miR-181a-5 p、hsa-miR-182-5p、hsa-miR-185-5p、hsa-miR-18b-5p、hsa-miR-191-5p、 hsa-miR-195-5p, hsa-miR-200a-3p, hsa-miR-206, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-21-5p, hsa-miR-222-3p, hsa-miR-223-3p, hsa-miR-23a-3p, hsa-miR-25-3p, hsa-miR-26a-5p, hsa-miR-26b-5p, hsa-miR-28-5p, hsa-miR-29a-3p, hsa-miR-3065-5p, hsa-miR-3074-5p, hsa-miR-30 b-5p、hsa-miR-30c-5p、hsa-miR-30e-5p、hsa-miR-3157-5p、hsa-miR-31 84-3p、hsa-miR-3184-5p、hsa-miR-3200-5p、hsa-miR-320a-3p、hsa-miR- 320b, hsa-miR-320c, hsa-miR-324-3p, hsa-miR-339-5p, hsa-miR-342-3p, hsa-miR-3614-3p, hsa-miR-3652, hsa-miR-374b-5p, hsa-miR-374c-3p hsa-miR-375-3p, hsa-miR-378a-3p, hsa-miR-378c, hsa-miR-3940-3p, hsa-miR-423-5p, hsa-miR-425-3p, hsa-miR-425-5p, hsa-miR-4298, hsa-m iR-451a, hsa-miR-4635, hsa-miR-4685-5p, hsa-miR-4707-3p, hsa-miR-4771, hsa-miR-484, hsa-miR-486-3p, hsa-miR-486-5p, hsa-miR-499a-5pThe expression of one or more miRNAs selected from hsa-miR-501-3p, hsa-miR-532-5p, hsa-miR-550a-3-5p, hsa-miR-574-5p, hsa-miR-629-5p, hsa-miR-651-5p, hsa-miR-6734-5p, hsa-miR-6786-3p, hsa-miR-6873-3p, hsa-miR-7706, hsa-miR-7847-3p, hsa-miR-8485, hsa-miR-92a-3p, hsa-miR-92b-3p, hsa-miR-93-5p, hsa-miR-98-5p, or any combination thereof, is used to determine the stage of endometrial implantation.

[0139] In some embodiments, hsa-let-7i-5p, hsa-miR-10a-5p, hsa-miR-10b-5p, hsa-miR-1180-3p, hsa-miR-1260b, hsa-miR-128-3p, hsa-miR-1285-3p, hsa-miR-1303, hsa-miR-130b-3p, hsa-miR-143-3p, hsa-miR-151b, hsa-miR-181b-5p, hsa-miR-193a-5p, hsa-miR-199a-3p, hsa-miR-199b-3p, hsa-miR-23b-3p, hsa-miR-24-3p, hsa-miR-28-3p, hsa-miR-29c-3p, hsa-miR-30a-3p, hsa-miR-30d-5p, hsa-miR-30e-3p, hsa-miR-3135b, hsa-miR-320d, hsa-miR-339-3p, hsa-miR-3529-3p, hsa-miR-361-5p, hsa-miR-3960, hsa-miR-421, hsa-miR-4510, hsa-miR-5585-5p, hsa-miR-625-5p, hsa-miR-660-5p, hsa-miR-7-5p, hsa-miR-744-5p, hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let-7d-5p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-let-7g-5p, hsa-miR-103a-3p, hsa-miR-103b, hsa-miR-106b-5p, hsa-miR-107, hsa-miR-12116, hsa-miR-122-5p, hsa-miR-122b-3p, hsa-miR-1246, hsa-miR-126-3p, hsa-miR-126-5p, hsa-miR-1290, hsa-miR-130a-3p, hsa-miR-139-5p, hsa-miR-140-3p, hsa-miR-142-5p, hsa-miR-144-3p, hsa-miR-150-5p, hsa-miR-151a-3p, hsa-miR-151a-5p, hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-16-5p, hsa-miR-181a-3p, hsa-miR-181a-5p, hsa-miR-182-5phsa-miR-185-5p, hsa-miR-191-5p, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-21-5p, hsa-mi R-223-3p, hsa-miR-23a-3p, hsa-miR-25-3p, hsa-miR-26a-5p, hsa-miR-26b-5p, hsa-miR-29a-3 p, hsa-miR-3074-5p, hsa-miR-30e-5p, hsa-miR-3184-3p, hsa-miR-320a-3p, hsa-miR-320b, hsa -miR-320c, hsa-miR-339-5p, hsa-miR-342-3p, hsa-miR-375-3p, hsa-miR-378a-3p, hsa-miR-37 8c, hsa-miR-423-5p, hsa-miR-425-5p, hsa-miR-451a, hsa-miR-4635, hsa-miR-4685-5p, hsa-m iR-4771, hsa-miR-484, hsa-miR-486-3p, hsa-miR-486-5p, hsa-miR-532-5p, hsa-miR-574-5p, h The expression of one or more miRNAs selected from sa-miR-629-5p, hsa-miR-6873-3p, hsa-miR-7847-3p, hsa-miR-8485, hsa-miR-92a-3p, hsa-miR-93-5p, hsa-miR-98-5p, or any combination thereof, is used to determine the stage of endometrial implantation potential.

[0140] In some embodiments, the expression of one or more miRNAs selected from hsa-let-7b-5p, hsa-let-7g-5p, hsa-miR-423-5p, hsa-miR-5585-5p, hsa-miR-629-5p, hsa-miR-3960, hsa-miR-191-5p, hsa-let-7d-5p, has-miR-122-5p, hsa-miR-375-3p, hsa-miR-143-3p, hsa-miR-12116, or any combination thereof, is used to determine the endometrial receptivity stage. In some embodiments, the expression of one or more miRNAs selected from hsa-miR-5585-5p, hsa-miR-629-5p, hsa-miR-3960, hsa-miR-191-5p, hsa-let-7d-5p, has-miR-122-5p, or any combination thereof, is used to determine the endometrial receptivity stage. In some embodiments, the expression of one or more miRNAs selected from hsa-miR-375-3p, hsa-miR-143-3p, hsa-miR-12116, or any combination thereof, is used to determine the endometrial receptivity stage. In some embodiments, the expression of one or more miRNAs selected from hsa-miR-375-3p, hsa-miR-143-3p, hsa-let-7d-5p, hsa-let-7g-5p, hsa-miR-191-5p, and hsa-miR-423-5p, or any combination thereof, is used to determine the stage of endometrial implantation.

[0141] In some embodiments, the expression of hsa-let-7b-5p, hsa-let-7g-5p, hsa-miR-423-5p, or any combination thereof is reduced in the receiving phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-let-7b-5p, hsa-let-7g-5p, hsa-miR-423-5p, or any combination thereof is reduced in the post-receptive phase compared to the receiving phase. In some embodiments, the expression of hsa-let-7b-5p, hsa-let-7g-5p, hsa-miR-423-5p, or any combination thereof is reduced in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-let-7b-5p, hsa-let-7g-5p, hsa-miR-423-5p, or any combination thereof is reduced in both the receiving and post-receptive phases compared to the pre-receptive phase.

[0142] In some embodiments, hsa-let-7b-5p expression decreases during the receptive phase compared to expression during the pre-receptive phase. In some embodiments, hsa-let-7b-5p expression decreases during the post-receptive phase compared to expression during the receptive phase. In some embodiments, hsa-let-7b-5p expression decreases during the post-receptive phase compared to expression during the pre-receptive phase. In some embodiments, hsa-let-7b-5p expression decreases during both the receptive and post-receptive phases compared to expression during the pre-receptive phase.

[0143] In some embodiments, hsa-miR-423-5p expression decreases during the receptive phase compared to expression during the pre-receptive phase. In some embodiments, hsa-miR-423-5p expression decreases during the post-receptive phase compared to expression during the receptive phase. In some embodiments, hsa-miR-423-5p expression decreases during the post-receptive phase compared to expression during the pre-receptive phase. In some embodiments, hsa-miR-423-5p expression decreases during both the receptive and post-receptive phases compared to expression during the pre-receptive phase.

[0144] In some embodiments, hsa-let-7g-5p expression is reduced during the receiving phase compared to expression during the pre-receptive phase. In some embodiments, hsa-let-7g-5p expression is reduced during the post-receptive phase compared to expression during the receiving phase. In some embodiments, hsa-let-7g-5p expression is reduced during the post-receptive phase compared to expression during the pre-receptive phase. In some embodiments, hsa-let-7g-5p expression is reduced during both the receiving and post-receptive phases compared to expression during the pre-receptive phase.

[0145] In some embodiments, the expression of hsa-miR-5585-5p, hsa-miR-629-5p, hsa-miR-3960, hsa-miR-191-5p, hsa-let-7d-5p, or any combination thereof decreases in the post-receptive stage compared to the pre-receptive and receptive stages. In some embodiments, the expression of hsa-miR-5585-5p, hsa-miR-629-5p, hsa-miR-3960, hsa-miR-191-5p, hsa-let-7d-5p, or any combination thereof, decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-122-5p increases in the post-receptive phase compared to the pre-receptive phase and the receptive phase. In some embodiments, the expression of hsa-miR-122-5p increases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-122-5p increases in the post-receptive phase compared to the receptive phase.

[0146] In some embodiments, the expression of hsa-miR-5585-5p decreases in the post-receptive phase compared to the pre-receptive phase and the receptive phase. In some embodiments, the expression of hsa-miR-5585-5p decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-5585-5p decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-5585-5p decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-5585-5p decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-5585-5p decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-629-5p decreases in the post-receptive phase compared to the pre-receptive phase and the receptive phase. In some embodiments, the expression of hsa-miR-629-5p decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-629-5p decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-629-5p decreases in the post-receptive phase compared to the pre-receptive phase and the receptive phase. In some embodiments, the expression of hsa-miR-629-5p decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-629-5p decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-3960 decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-3960 decreases in the post-receptive phase compared to the receptive phase.In some embodiments, the expression of hsa-miR-3960 decreases in the post-receptive phase compared to the pre-receptive phase and the receptive phase. In some embodiments, the expression of hsa-miR-3960 decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-3960 decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-191-5p decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-191-5p decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-191-5p decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-191-5p decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-191-5p decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-191-5p decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-let-7d-5p decreases in the post-receptive phase compared to the pre-receptive phase and the receptive phase. In some embodiments, the expression of hsa-let-7d-5p decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-let-7d-5p decreases in the post-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-let-7d-5p decreases in the post-receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-let-7d-5p decreases in the post-receptive phase compared to the receptive phase.

[0147] In some embodiments, the expression of hsa-miR-375-3p, hsa-miR-143-3p, hsa-miR-12116, or any combination thereof, increases in the pre-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-375-3p, hsa-miR-143-3p, hsa-miR-12116, or any combination thereof, decreases in the receptive phase compared to the pre-receptive phase. In some embodiments, the expression of hsa-miR-375-3p increases in the pre-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-143-3p increases in the pre-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-12116 increases in the pre-receptive phase compared to the receptive phase. In some embodiments, the expression of hsa-miR-375-3p, hsa-miR-143-3p, hsa-miR-12116, or any combination thereof, is the same between the receptive and post-receptive phases. In some embodiments, the expression of hsa-miR-375-3p is the same between the receptive and post-receptive phases. In some embodiments, the expression of hsa-miR-143-3p is the same between the receptive and post-receptive phases. In some embodiments, the expression of hsa-miR-12116 is the same between the receptive and post-receptive phases.

[0148] In some embodiments, miRNAs measured by ORA are involved in one or more biological functions of gland development, response to oxygen levels, cell cycle G1 / S phase transition, response to decreased oxygen levels, response to hypoxia, mitotic cell cycle GI.A transition, mitotic cell cycle phase transition, epithelial cell proliferation, cellular response to oxygen levels, regulation of apoptotic signaling pathways, cell proliferation, cell cycle phases, intrauterine embryo development, cell migration, or any combination thereof. In some embodiments, miRNAs measured by ORA are involved in gland development. In some embodiments, miRNAs measured by ORA are involved in the response to oxygen levels. In some embodiments, miRNAs measured by ORA are involved in cell cycle G1 / S phase transition. In some embodiments, miRNAs measured by ORA are involved in the response to decreased oxygen levels. In some embodiments, miRNAs measured by ORA are involved in the response to hypoxia. In some embodiments, miRNAs measured by ORA are involved in decidualization. In some embodiments, miRNAs measured by ORA are involved in GI / A transition in the mitotic cell cycle. In some embodiments, miRNAs measured by ORA are involved in phase transitions in the mitotic cell cycle. In some embodiments, miRNAs measured by ORA are involved in epithelial cell proliferation. In some embodiments, miRNAs measured by ORA are involved in the cellular response to oxygen levels.In some embodiments, miRNAs measured by ORA are involved in the regulation of apoptotic signaling pathways. In some embodiments, miRNAs measured by ORA are involved in cell proliferation. In some embodiments, miRNAs measured by ORA are involved in cell cycle phases. In some embodiments, miRNAs measured by ORA are involved in intrauterine embryonic development. In some embodiments, miRNAs measured by ORA are involved in cell migration.

[0149] In some embodiments, endometrial receptivity is determined by one or more of the following steps. Blood collection: Blood is collected from the subject. In some embodiments, blood is collected during a hormone replacement therapy cycle or a natural cycle. Blood is collected either 4 (96 hours) and 5 (120 hours) after progesterone administration in an HRT cycle, or from the time a surge in LH is detected in a natural cycle, or 6 (144 hours) and 7 (168 hours) after human chorionic gonadotropin (hGC) administration. Blood samples are collected using a needle of at least 21G to minimize hemolysis. Hemolysis is monitored during the pre-processing step of processing the blood into plasma, as hemolysis distorts data analysis. Pre-processing: Whole blood is processed into plasma. This step is performed at the clinic where the blood was collected. Hemolysis is monitored during the pre-processing step because it distorts data analysis. Sample extraction: Pre-treated plasma is then sent to the laboratory for RNA extraction. Library preparation: The extracted miRNAs are prepared for next-generation sequencing. Sequencing: Next-generation sequencing is performed on the sample. Data analysis: miRNAs measured in the sample are analyzed using an ORA predictive model. Final Report: Based on ORA analysis, samples are classified into one of four endometrial state groups, an indeterminate result group, or a non-effective / insufficient RNA group (less than 1% of samples will result in an indeterminate or non-effective / insufficient RNA result using the ORA method). The six groups are described as follows: a. Pre-receptive period: The uterine lining is not yet ready for embryo implantation, and implantation at the time of blood collection may not be ideal. It is recommended to delay embryo implantation during the next treatment cycle. Embryo transfer may be delayed by 24 hours. b. Receptive period: The optimal time for embryo transfer is when blood is drawn. c. Post-receptive period (short window): The endometrium has passed its optimal time for embryo implantation. It is recommended to adjust the timing of embryo implantation by 12 hours during the next treatment cycle. d. Post-receptive period (average window): The endometrium has passed its optimal time for embryo implantation. It is recommended to adjust the timing of embryo implantation by 24 hours during the next treatment cycle. e. Uncertain Results: The obtained data does not match ORA's models and databases, making it impossible to proceed with the analysis. This may be due to pre-existing physiological conditions or variations that occurred during the sample submission process. f. Ineffective / Insufficient RNA: The concentration of the extracted substance (miRNA) is too low to obtain a result. Blood should be drawn again in the hope of obtaining a higher concentration to advance ORA. Embryo transfer: The healthcare provider will perform embryo transfer during the next treatment cycle according to the ORA test results.

[0150] In some embodiments, endometrial receptivity is determined using one or more of the following steps. Step i) Collect blood from the subject. Here, the blood is collected during a hormone replacement therapy cycle or a natural cycle. Here, a) In the HRT cycle, blood is collected either 4 days (96 hours) or 5 days (120 hours) after progesterone administration, or b) Six days (144 hours) and seven days (168 hours) after the time a surge in LH was detected in the natural cycle, or after the administration of human chorionic gonadotropin (hGC), Furthermore, blood samples are collected using a needle of at least 21G to minimize hemolysis. Step ii) The blood is processed into plasma at the clinic where it was collected, and hemolysis is monitored. Step iii) The treated plasma is sent to the laboratory for RNA extraction and sequencing, which includes the following steps. a) miRNA extraction b) Library preparation c) Next-generation sequencing Step iv) The sequenced miRNAs are analyzed using the ORA prediction method described herein. Step v) The blood sample is classified into one of the following categories: a) Pre-receptive period. Delaying embryo implantation in the next treatment cycle is recommended, and embryo transfer can be delayed by 24 hours. b) Receptive period. The optimal time for embryo transfer is when blood is drawn. c) Post-receptive period, short window. It is recommended to adjust the timing of embryo implantation by advancing it by 12 hours in the next treatment cycle. d) Post-receptive period, mean window. It is recommended to adjust the timing of embryo implantation by advancing it by 24 hours in the next treatment cycle. e) Results are inconclusive. The data obtained cannot determine acceptance. f) Ineffective / Insufficient RNA. Results cannot be obtained due to the low concentration of the extracted substance (miRNA). Step vi) The healthcare provider performs embryo transfer during the next treatment cycle, according to the results determined in steps i) through v).

[0151] Application of the method according to the present invention In some embodiments, the present invention provides a method for determining the state of the endometrium using a sample, for example, an endometrial biopsy, the method including the following: (a) Perform an assay on blood samples from subjects to determine the miRNA expression profile of the blood samples. Here, the miRNA expression profile includes the expression levels of multiple miRNAs, for example, 281 miRNAs selected from those listed in Table 7. (b) Analyze the miRNA expression profile and obtain a predictive score for receptivity, for example, using a computer-based method.

[0152] The method of the present invention includes, but is not limited to, in vitro fertilization therapy and can be used for a variety of diagnostic and therapeutic purposes. For example, in some embodiments, based on endometrial results, the method may further include implanting an embryo in a subject or administering one or more treatments to a subject who has or has had implantation failure.

[0153] In some embodiments, the present invention provides a method for detecting endometrial receptivity for embryo transfer, the method including the following: (a) Perform an assay on blood samples from subjects to determine the miRNA expression profile of the blood samples. Here, the miRNA expression profile includes the expression levels of multiple miRNAs, for example, 281 miRNAs selected from those listed in Table 7. (b) Analyze the miRNA expression profile to obtain a predictive receptivity score. Here, the predictive receptivity score determines whether the subject has endometrial receptivity. (c) Transferring an embryo into the endometrium of a subject determined to be endometrial receptive.

[0154] In some embodiments, the present invention provides a method for detecting endometrial receptivity for embryo implantation, the method including the following: (a) Perform an assay on blood samples from subjects to determine the miRNA expression profile of the blood samples. Here, the miRNA expression profile includes the expression levels of multiple miRNAs, for example, 281 miRNAs selected from those listed in Table 7. (b) Analyze the miRNA expression profile to obtain a predictive receptivity score. Here, the predictive receptivity score determines whether the subject has endometrial receptivity. (c) Transferring an embryo into the endometrium of a subject determined to be endometrial receptive.

[0155] In some embodiments, a method for determining the state of the endometrium can be used to determine the timing of embryo transfer to a subject. In some embodiments, if the state of the endometrium is in the receptive stage, the subject is considered suitable for embryo transfer. In some embodiments, if the state of the endometrium is in the pre-receptive or post-receptive stage, the subject is considered unsuitable for embryo transfer. In some embodiments, if the state of the endometrium is determined to be in the pre-receptive or post-receptive stage, the present invention provides an embryo transfer method based on information regarding the state of the endometrium. For example, in some embodiments, if the state of the endometrium is determined to be in the pre-receptive stage, embryo transfer is performed between 5.5 and 7.5 days after progesterone administration during the next cycle. In some embodiments, if the state of the endometrium is determined to be in the pre-receptive stage, embryo transfer is performed between approximately 5.5 and 7.5 days during the next cycle. In some embodiments, if the state of the endometrium is determined to be in the pre-receptive stage, embryo transfer is performed between 5.5 and 7.5 days during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed between 5 and 8 days into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed between approximately 5 and 8 days into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed between 5 and 8 days into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is delayed by 24 hours. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed between approximately 5 days after progesterone administration into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed between approximately 5.5 days after progesterone administration into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed between approximately 6 days after progesterone administration into the next cycle.In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed approximately 6.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed approximately 7 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed approximately 7.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed approximately 8 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed 5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed 5.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed 6 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed 6.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed 7 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed 7.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is performed 8 days after progesterone administration during the next cycle.

[0156] In some embodiments, a method for determining the state of the endometrium can be used to determine the timing of embryo implantation in a subject. In some embodiments, if the state of the endometrium is in the receptive phase, the subject is considered suitable for embryo implantation. In some embodiments, if the state of the endometrium is in the pre-receptive or post-receptive phase, the subject is considered unsuitable for embryo implantation. In some embodiments, if the state of the endometrium is determined to be in the pre-receptive or post-receptive phase, the present invention provides a method for embryo implantation based on information regarding the state of the endometrium. For example, in some embodiments, if the state of the endometrium is determined to be in the pre-receptive phase, embryo implantation occurs between 5.5 and 7.5 days after progesterone administration during the next cycle. In some embodiments, if the state of the endometrium is determined to be in the pre-receptive phase, embryo implantation occurs between approximately 5.5 and 7.5 days during the next cycle. In some embodiments, if the state of the endometrium is determined to be in the pre-receptive phase, embryo implantation occurs between 5.5 and 7.5 days during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs between 5 and 8 days into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs between approximately 5 and 8 days into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs between 5 and 8 days into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo transfer is delayed by 24 hours. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs between approximately 5 days after progesterone administration into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs between approximately 5.5 days after progesterone administration into the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs between approximately 6 days after progesterone administration into the next cycle.In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs approximately 6.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs approximately 7 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs approximately 7.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs approximately 8 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs 5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs 5.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs 6 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs 6.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs 7 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs 7.5 days after progesterone administration during the next cycle. In some embodiments, if the endometrial state is determined to be pre-receptive, embryo implantation occurs 8 days after progesterone administration during the next cycle.

[0157] In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed in the next cycle between 2.5 and 4.5 days after progesterone administration, for example, on day 2.5, 3, 3.5, 4, or 4.5. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed in the next cycle between approximately 2.5 and 4.5 days. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed in the next cycle between approximately 2.5 and 4.5 days. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is advanced by 12 hours. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is advanced by 24 hours. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed approximately 2.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed approximately 3 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed approximately 3.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed approximately 4 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed approximately 4.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 2.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 3 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 3.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 4 days after progesterone administration.In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 4.5 days after progesterone administration.

[0158] In some embodiments, if the endometrial state is determined to be post-receptive, embryo implantation occurs in the next cycle between 2.5 and 4.5 days after progesterone administration, for example, on days 2.5, 3, 3.5, 4, or 4.5. In some embodiments, if the endometrial state is determined to be post-receptive, embryo implantation occurs in the next cycle between approximately 2.5 and 4.5 days. In some embodiments, if the endometrial state is determined to be post-receptive, embryo implantation occurs in the next cycle between 2.5 and 4.5 days. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is advanced by 12 hours. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is advanced by 24 hours. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer occurs approximately 2.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed approximately 3 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed approximately 3.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed approximately 4 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed approximately 4.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 2.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 3 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 3.5 days after progesterone administration. In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 4 days after progesterone administration.In some embodiments, if the endometrial state is determined to be post-receptive, embryo transfer is performed 4.5 days after progesterone administration.

[0159] In some embodiments, if the endometrium is in a non-receptive state at the time of sampling, the information obtained is useful and the method can be repeated by taking a blood sample at another time point modified according to the results of the initial decision. For example, if the endometrium is in a pre-receptive state, the next time to take a blood sample may be more than 7 days after a surge in endogenous LH or more than 5 days after progesterone administration. For example, the next time to take a blood sample may be between 7.5 and 10.5 days after a surge in endogenous LH, e.g., 7.5, 8, 8.5, 9, 9.5, 10, or 10.5 days, or between 5.5 and 7.5 days after progesterone administration, e.g., 5.5, 6, 6.5, 7, or 7.5 days. Alternatively, in some embodiments, if the endometrial state is post-receptive, the next time to collect a blood sample may be less than 7 days after a surge in endogenous LH or less than 5 days after progesterone administration. For example, the next time to collect a blood sample may be between 3.5 and 6.5 days after a surge in endogenous LH, e.g., 3.5, 4, 4.5, 5, 5.5, 6, or 6.5 days, or between 2.5 and 4.5 days after progesterone administration, e.g., 2.5, 3, 3.5, 4, or 4.5 days. In some embodiments, following these procedures can identify the receptive state and improve the success rate of in vitro fertilization treatment. In some embodiments, the subject has or has had implantation failure. In some embodiments, the subject is undergoing in vitro fertilization treatment. In some embodiments, the subject is undergoing assisted reproductive technology (ART) treatment. In some embodiments, assisted reproductive technology is in vitro fertilization-embryo transfer (IVF-ET). In some embodiments, assisted reproductive technology is gamete intrafallopian transfer (GIFT). In some embodiments, assisted reproductive technology is zygote intrafallopian transfer (ZIFT).In some embodiments, assisted reproductive technology is frozen embryo transfer (FET). In some embodiments, assisted reproductive technology is fresh embryo transfer.

[0160] In some embodiments, in vitro fertilization treatment includes fresh embryo transfer. In some embodiments, in vitro fertilization treatment includes frozen embryo transfer (FET).

[0161] In some embodiments, if the endometrial state is determined to be either pre-receptive or post-receptive, the method for determining the endometrial state can be repeated at least once, or until the endometrial state is determined to be receptive.

[0162] In some embodiments, the method for determining the state of the endometrium according to the present invention can be used to determine the implantation window of a subject. In some embodiments, the method according to the present invention can be used to classify the responsiveness of a subject to in vitro fertilization treatment. In any of these uses, in some embodiments, the subject has or has had implantation failure. In some embodiments, the subject is undergoing in vitro fertilization treatment. In some embodiments, the subject is undergoing assisted reproductive technology.

[0163] In some embodiments, the method for determining the state of the endometrium according to the present invention can be used as a valuable tool for investigating the effects of pregnancy drugs on a subject's endometrium. In these embodiments, the subject has or has had implantation failure. In some embodiments, the subject is undergoing in vitro fertilization treatment.

[0164] In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization treatment cycles in patients. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization treatment cycles in a subject population.

[0165] In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an in vitro fertilization (IVF) treatment cycle in a subject by 5% to 90% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an IVF treatment cycle in a subject by 50% to 90% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an IVF treatment cycle in a subject by 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an in vitro fertilization treatment cycle in a subject by about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, or about 99% compared to the implantation success rate in a subject undergoing an in vitro fertilization treatment cycle without using the method for determining the state of the endometrium described herein.

[0166] In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an in vitro fertilization (IVF) treatment cycle in a subject by at least 5% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an IVF treatment cycle in a subject by at least 10% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an IVF treatment cycle in a subject by at least 20% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an IVF treatment cycle in a subject by at least 30% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an in vitro fertilization (IVF) treatment cycle in a subject by at least 40% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an IVF treatment cycle in a subject by at least 50% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an IVF treatment cycle in a subject by at least 60% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein.In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an in vitro fertilization (IVF) treatment cycle in a subject by at least 70% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an IVF treatment cycle in a subject by at least 80% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation success rate during an IVF treatment cycle in a subject by at least 90% compared to the implantation success rate of a subject undergoing an IVF treatment cycle without using the method for determining the state of the endometrium described herein.

[0167] In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by 5% to 90% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by 50% to 90% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization treatment cycles in a subject population by about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, or about 99% compared to the implantation rate in a subject population undergoing in vitro fertilization treatment cycles without using the method for determining the state of the endometrium described herein.

[0168] In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 5% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 10% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 20% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 30% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 40% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 50% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 60% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein.In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 70% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 80% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the overall implantation rate during in vitro fertilization (IVF) treatment cycles in a subject population by at least 90% compared to the implantation rate of a subject population undergoing IVF treatment cycles without using the method for determining the state of the endometrium described herein.

[0169] In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by 5% to 90% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by 50% to 90% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by approximately 5%, approximately 10%, approximately 20%, approximately 30%, approximately 40%, approximately 50%, approximately 60%, approximately 70%, approximately 80%, approximately 90%, approximately 95%, or approximately 99% compared to subjects who did not undergo analysis of their endometrial state.

[0170] In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 5% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 10% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 20% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 30% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 40% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 50% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 60% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 70% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 80% compared to subjects who did not undergo analysis of their endometrial state. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in subjects by at least 90% compared to subjects who did not undergo analysis of their endometrial state.

[0171] In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by 5% to 90% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by 50% to 90% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject population by approximately 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% compared to a subject population that did not undergo analysis of the state of the endometrium.

[0172] In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by at least 10% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by at least 20% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by at least 30% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by at least 40% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by at least 50% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by at least 60% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by at least 70% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by at least 80% compared to a subject group that did not undergo analysis of the state of the endometrium. In some embodiments, the method for determining the state of the endometrium according to the present invention increases the pregnancy success rate in a subject group by at least 90% compared to a subject group that did not undergo analysis of the state of the endometrium.

[0173] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient in an in vitro fertilization (IVF) cycle. In some embodiments, the blood sample is taken from the patient before embryo transfer and on the day of embryo transfer in the IVF cycle. In some embodiments, the present invention provides a method for determining a patient's predicted endometrial state at the time of embryo transfer based on a miRNA expression profile from a blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from a blood sample. In some embodiments, the present invention provides a method for generating a predicted embryo transfer date based on a predicted endometrial state determined based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from a blood sample.

[0174] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient during an in vitro fertilization (IVF) implantation cycle. The blood sample is taken from the patient before embryo transfer and on the day of embryo transfer during the IVF implantation cycle. In some embodiments, the present invention provides a method for determining a patient's predicted endometrial state at the time of embryo transfer based on miRNA expression profiles from a blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from a blood sample. In some embodiments, the present invention provides a method for generating a predicted embryo transfer date based on a predicted endometrial state determined based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from a blood sample.

[0175] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient in an in vitro fertilization (IVF) cycle. In some embodiments, the blood sample is taken from the patient before embryo transfer and on the day of embryo transfer in the IVF cycle. In some embodiments, the present invention provides a method comprising determining the predicted endometrial state of the patient at the time of embryo transfer based on a miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from the blood sample. In some embodiments, the present invention provides a method comprising generating a predicted embryo transfer date based on the predicted endometrial state. In some embodiments, the present invention provides a method comprising performing a subsequent in vitro fertilization cycle on the patient by performing embryo transfer on the predicted embryo transfer date. In some embodiments, the method comprises storing the blood sample. In some embodiments, storing the blood sample preserves subsequent extraction and sequencing of miRNA. In some embodiments, the method comprises performing a pregnancy test on the patient, and if the pregnancy test is negative, obtaining the blood sample from storage and providing a miRNA expression profile by sequencing the blood sample for miRNA.

[0176] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient during an in vitro fertilization (IVF) implantation cycle. The blood sample is taken from the patient before embryo transfer and on the day of embryo transfer during the IVF implantation cycle. In some embodiments, the present invention provides a method comprising determining the predicted endometrial state of the patient at the time of embryo transfer based on a miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data derived from the blood sample. In some embodiments, the present invention provides a method comprising generating a predicted embryo transfer date based on the predicted endometrial state. In some embodiments, the present invention provides a method comprising performing a subsequent in vitro fertilization cycle on the patient by performing embryo transfer on the predicted embryo transfer date. In some embodiments, the method comprises storing the blood sample. In some embodiments, storing the blood sample preserves subsequent extraction and sequencing of miRNA. In some embodiments, the method comprises performing a pregnancy test on the patient, and if the pregnancy test is negative, obtaining the blood sample from storage and providing a miRNA expression profile by sequencing the blood sample for miRNA.

[0177] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient in an in vitro fertilization (IVF) cycle. In some embodiments, the blood sample is taken from the patient before embryo transfer and on the day of embryo transfer in the IVF cycle. In some embodiments, the present invention provides a method comprising determining the predicted endometrial state of the patient at the time of embryo transfer based on a miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from the blood sample. In some embodiments, the present invention provides a method comprising generating a predicted embryo transfer date based on the predicted endometrial state. In some embodiments, the present invention provides a method comprising performing a subsequent in vitro fertilization cycle on the patient by performing embryo transfer on the predicted embryo transfer date. In some embodiments, the present invention provides a method comprising storing the blood sample, wherein storing the blood sample preserves subsequent extraction and sequencing of miRNA. In some embodiments, the present invention provides providing a miRNA expression profile by performing a pregnancy test on the patient, and if the pregnancy test is negative, obtaining the blood sample from storage and sequencing the blood sample for miRNA.

[0178] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient during an in vitro fertilization (IVF) implantation cycle. The blood sample is taken from the patient before embryo transfer and on the day of embryo transfer during the IVF implantation cycle. In some embodiments, the present invention provides a method comprising determining the predicted endometrial state of the patient at the time of embryo transfer based on a miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from the blood sample. In some embodiments, the present invention provides a method comprising generating a predicted embryo transfer date based on the predicted endometrial state. In some embodiments, the present invention provides a method comprising performing a subsequent in vitro fertilization cycle on the patient by performing embryo transfer on the predicted embryo transfer date. In some embodiments, the present invention provides a method comprising storing the blood sample, wherein storing the blood sample preserves subsequent extraction and sequencing of miRNA. In some embodiments, the present invention provides providing a miRNA expression profile by performing a pregnancy test on the patient, and if the pregnancy test is negative, obtaining the blood sample from storage and sequencing the blood sample for miRNA.

[0179] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient in an in vitro fertilization (IVF) cycle. The blood sample is taken from the patient before embryo transfer and on the day of embryo transfer in the IVF cycle. In some embodiments, the present invention provides a method comprising determining the predicted endometrial state of the patient at the time of embryo transfer based on a miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from the blood sample. In some embodiments, the present invention provides a method comprising generating a predicted embryo transfer date based on the predicted endometrial state. In some embodiments, if the IVF cycle is unsuccessful, embryo transfer is performed in a subsequent IVF cycle based on the predicted embryo transfer date. In some embodiments, the method comprises storing the blood sample, the storage of the blood sample preserving subsequent extraction and sequencing of miRNA. In some embodiments, the method comprises performing a pregnancy test on the patient, and if the pregnancy test is negative, obtaining the blood sample from storage and providing a miRNA expression profile by sequencing the blood sample for miRNA.

[0180] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient in an in vitro fertilization (IVF) implantation cycle. The blood sample is taken from the patient before embryo transfer and on the day of embryo transfer in the IVF implantation cycle. In some embodiments, the present invention provides a method comprising determining the predicted endometrial state of the patient at the time of embryo transfer based on a miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from the blood sample. In some embodiments, the present invention provides a method comprising generating a predicted embryo transfer date based on the predicted endometrial state. In some embodiments, if the IVF implantation cycle is unsuccessful, embryo transfer is performed in a subsequent IVF cycle based on the predicted embryo transfer date. In some embodiments, the method comprises storing the blood sample, the storage of the blood sample preserving subsequent extraction and sequencing of miRNA. In some embodiments, the method comprises performing a pregnancy test on the patient, and if the pregnancy test is negative, obtaining the blood sample from storage and providing a miRNA expression profile by sequencing the blood sample for miRNA.

[0181] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient in an in vitro fertilization (IVF) cycle. The blood sample is taken from the patient before embryo transfer and on the day of embryo transfer in the IVF cycle. In some embodiments, the present invention provides a method comprising determining the predicted endometrial state of the patient at the time of embryo transfer based on a miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from the blood sample. In some embodiments, the present invention provides a method comprising generating a predicted embryo transfer date based on the predicted endometrial state. If the IVF cycle is unsuccessful, embryo transfer is performed in a subsequent IVF cycle based on the predicted embryo transfer date, and the method further comprises: storing the blood sample, wherein storing the blood sample preserves subsequent extraction and sequencing of miRNA; performing a pregnancy test on the patient, and if the pregnancy test is negative, obtaining the blood sample from storage; and providing a miRNA expression profile by sequencing the blood sample for miRNA.

[0182] In some embodiments, the present invention provides a method comprising obtaining a blood sample from a patient in an in vitro fertilization (IVF) implantation cycle. The blood sample is taken from the patient before embryo transfer and on the day of embryo transfer in the IVF implantation cycle. In some embodiments, the present invention provides a method comprising determining the predicted endometrial state of the patient at the time of embryo transfer based on a miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on miRNA expression profile data from the blood sample. In some embodiments, the present invention provides a method comprising generating a predicted embryo transfer date based on the predicted endometrial state. If the IVF implantation cycle is unsuccessful, embryo transfer is performed in a subsequent IVF cycle based on the predicted embryo transfer date, and the method further comprises storing the blood sample, wherein storing the blood sample preserves subsequent extraction and sequencing of miRNA; performing a pregnancy test on the patient, and if the pregnancy test is negative, obtaining the blood sample from storage; and providing a miRNA expression profile by sequencing the blood sample for miRNA.

[0183] In some embodiments, the method includes determining endometrial state data for multiple patients. Endometrial state data can be obtained from endometrial samples and at least one of pregnancy outcomes. The method includes associating each miRNA expression profile from multiple patients with endometrial state data. Each miRNA expression profile for each patient is associated with the endometrial state of that patient. The method further includes training a machine learning model based on the associated miRNA expression profiles and associated endometrial state data. The resulting trained machine learning model is trained to output a predicted endometrial state based on the input miRNA expression profiles.

[0184] In some embodiments, the endometrial state is at least one of PRE (e.g., pre-receptive), WOI (e.g., receptive, implantation window), and POST (e.g., post-receptive).

[0185] In some embodiments, the machine learning model is a plurality of machine learning models, each of which generates one or more predictions from PRE, WOI, and POST.

[0186] In some embodiments, the model further associates one or more of each patient's age, body mass index (BMI), pregnancy history, and implantation failure with the patient's endometrial state, so that input data on each patient's age, body mass index (BMI), pregnancy history, and implantation failure can be input to the machine learning model in addition to the miRNA expression profile.

[0187] In some embodiments, the machine learning is trained on a server in a network. The server is accessible to the patient or clinician for uploading the miRNA expression data.

[0188] In some embodiments, the method includes receiving a digital representation of a patient's miRNA expression profile in a machine learning model in a networked server, wherein the miRNA expression profile is determined by sequencing of the patient's blood sample; processing the miRNA expression profile in the machine learning model, wherein the machine learning model is trained to output a predicted endometrial state based on the input miRNA expression profile; and processing such that the processing yields a predicted endometrial state for the patient based on the provided digital representation of the miRNA expression profile.

[0189] In some embodiments, the machine learning model is further trained to receive input data about each patient's age, body mass index (BMI), pregnancy history, and implantation failure, and to predict the patient's endometrial state based on the received input data. The method further includes receiving the input data about each patient's age, body mass index (BMI), pregnancy history, and implantation failure, so that the machine learning model outputs the predicted endometrial state based on the input data in addition to the miRNA expression profile.

[0190] In some embodiments, the trained machine learning model is trained based on multiple miRNA expression profiles. Each miRNA expression profile is associated with a known endometrial state of a predetermined patient, thereby training the machine learning model to output the endometrial state of a patient other than the multiple patients based on a miRNA expression profile determined from a sample of a patient other than the multiple patients.

[0191] In some embodiments, the machine learning model is a classifier.

[0192] kit Another aspect of the present invention relates to a kit for performing a method for determining the state of the endometrium. In some embodiments, the kit includes primers and / or probes suitable for detecting the expression levels of multiple miRNAs, e.g., 281 miRNAs provided in Table 7. In some embodiments, the primers and / or probes are suitable for performing a qPCR reaction to detect the expression levels of the 281 miRNAs in Table 7. In some embodiments, the kit includes one or more miRNA profiling chips that target the 281 miRNAs in Table 7. In some embodiments, one or more chips further target RNA sequences that can be used as endogenous controls for miRNA expression analysis, e.g., 18s rRNA.

[0193] The kit may further include instructions for (i) determining the miRNA expression profile of a blood sample from a subject (optionally using one or more miRNA profiling chips), and / or (ii) obtaining an implantation potential prediction score based on the miRNA expression profile using a computer-based machine learning model. In some embodiments, the kit includes instructions for how to interpret and use the implantation potential prediction score.

[0194] In some embodiments, the kit provides instructions for performing blood collection during a hormone replacement therapy cycle or a natural cycle. In some embodiments, the kit provides instructions for performing blood collection during a hormone replacement therapy cycle. In some embodiments, the kit provides instructions for performing blood collection during a natural cycle. In some embodiments, the kit provides instructions for performing blood collection either 4 days (96 hours) or 5 days (120 hours) after progesterone administration in a hormone replacement therapy (HRT) cycle. In some embodiments, the kit provides instructions for performing blood collection 6 days (144 hours) or 7 days (168 hours) after a surge in LH is detected or after chorionic gonadotropin (hCG) is administered in a natural cycle.

[0195] In some embodiments, the blood sample is sent to a laboratory for RNA extraction and analysis.

[0196] In some embodiments, the kit provides post-ORA analysis instructions regarding the subject's endometrial state. In some embodiments, the kit provides post-ORA analysis instructions classifying the subject's sample into one of four endometrial state groups, including pre-receptive, receptive, post-receptive (short window), or post-receptive (average window). In some embodiments, the healthcare provider performs personalized embryo transfer (pET) during the treatment cycle according to the ORA test results.

[0197] In some embodiments, the sample is determined to have an indeterminate result. In some embodiments, the sample is determined to have invalid / insufficient RNA. In some embodiments, the indeterminate result or invalid / insufficient RNA result occurs in less than 1% of the samples.

[0198] In some embodiments, the kit provides a test result accompanied by one of the following endometrial conditions or sample results: i) Receptive period (or WOI): The optimal time for embryo transfer is when blood is drawn. ii) Pre-receptive period: The uterine lining is not yet ready for embryo implantation, and implantation at the time of this blood draw may not be ideal. Delaying embryo implantation during the next treatment cycle is recommended. iii) Post-receptive period (short or average window): The endometrium has passed its optimal time for embryo implantation. It is recommended to adjust the timing of embryo implantation by advancing the time of implantation by 12 or 24 hours during the next treatment cycle. iv) Uncertain results: The obtained data does not match ORA's methods and databases, and analysis cannot be proceeded. This may be due to pre-existing physiological conditions or variations that occurred during the sample submission process. v) Ineffective / Insufficient RNA: The concentration of the extracted substance (miRNA) is too low to obtain a result. Blood should be drawn again in the hope of obtaining a higher concentration to improve the ORA score.

[0199] In some embodiments, the kit is useful for diagnostic and therapeutic purposes, including but not limited to in vitro fertilization treatment.

[0200] Clinical and Calculation Methods Figure 2D shows embodiments of an in vitro fertilization (IVF) cycle and subsequent IVF cycles in a patient using the method of the present invention. The IVF cycle is performed before the subsequent IVF cycle. In the IVF cycle, on day 5 of progesterone exposure (P+5), the healthcare provider performs blood collection and subsequent embryo transfer on the patient. In some embodiments, the embryos are evaluated as high-quality embryos. In some embodiments, the embryos are evaluated as average-grade embryos. In some embodiments, the embryos are evaluated as low-grade embryos. In some embodiments, blood collection is performed before embryo transfer. Performing blood collection before embryo transfer captures a blood sample of the patient in a preimplantation state and thus provides a measurement of the patient's blood indicating whether the patient is in a PRE, WOI, or POST state. The sample from the blood collection is sequenced for miRNAs. From the sequencing, a miRNA expression profile is determined (e.g., by a computer-implemented method).

[0201] At a point after in vitro fertilization treatment, a test or donor determines the success or failure of the transfer (e.g., whether the embryo implanted). This determination can be made by one or more methods, such as blood tests, urine tests, ultrasound, or other methods. In some embodiments, if the transfer is successful, subsequent in vitro fertilization may not be necessary. In some embodiments, the miRNA expression profile can be used as ground truth data to train a model classifier, and the endometrial state associated with the miRNA expression profile is WOI (Wound-on-In) because the transfer was successful.

[0202] In some embodiments or cases, if embryo transfer is unsuccessful, subsequent in vitro fertilization (IVF) cycles are performed. In some cases, it is understood that after successful embryo transfer (e.g., resulting in implantation), pregnancy may not reach full term for various reasons, including miscarriage. In such cases, the patient and clinician can determine, based on the successful transfer, that the timing of the cycle in which the embryo transfer was performed is within the implantation window. Therefore, the patient and clinician can choose to repeat the same embryo transfer timing based on the successful transfer. However, the patient and clinician may further order the tests described in the present invention to determine a predicted endometrial state or condition based on a blood sample for additional data. If the blood is sequenced after a successful transfer, the miRNA expression profile of the blood sample can be further used as additional training data for a model associated with the endometrial state or condition being WOI (Wounded Out of Infection).

[0203] Next, this method can determine the patient's endometrial state by inputting the miRNA expression profile obtained from blood sampling into a model. The model outputs a determination of whether the miRNA expression profile indicates the patient is PRE or POST. If the model outputs POST, embryo transfer can be shifted to either P+3 or a day before day 5 of progesterone exposure (e.g., P+1, P+2, P+3, P+4). If the model outputs PRE, embryo transfer can be shifted to either P+7 or a day after day 5 of progesterone exposure (e.g., P+6, P+7, P+8, P+9, P+10).

[0204] In some embodiments, it is understood that if implantation is unsuccessful in an in vitro fertilization cycle, the model outputs probabilities for each state. In these cases, subsequent in vitro fertilization cycles can be guided by a state with a higher probability between pre-referencing (PRE) and post-referencing (POST), except for WOI results.

[0205] In some embodiments, the in vitro fertilization (IVF) cycle includes patient blood collection and subsequent embryo transfer, but does not include simultaneous endometrial sampling. Thus, this IVF cycle differs from conventionally used "mock cycles," where the patient receives hormone replacement therapy, an endometrial sample is taken to determine the state of the endometrium, and embryo transfer is not performed in that "mock cycle." Advantageously, the patient can avoid the additional step of endometrial sample extraction and attempt embryo transfer in an earlier or first IVF cycle of hormone replacement therapy. In some embodiments, if the transfer in the IVF cycle is successful, the blood sample can be used to further train a predictive model by associating it with the state of the endometrium during the implantation window. In some embodiments, if the transfer in the IVF cycle is successful, the patient can avoid additional IVF cycles of hormone replacement therapy compared to treatment with "mock cycles" in which embryo transfer is performed in additional cycles. In some embodiments, if embryo transfer in an in vitro fertilization cycle is unsuccessful, a subsequent in vitro fertilization cycle is performed with embryo transfer on a predicted embryo transfer date determined by the model.

[0206] Figure 2E shows embodiments of the first and subsequent in vitro fertilization (IVF) cycles of a patient using the method of the present invention. In the IVF cycle, on day 5 of progesterone exposure (P+5), the healthcare provider performs blood collection and subsequent embryo transfer on the patient. In some embodiments, the embryos are evaluated as high-quality embryos. In some embodiments, the embryos are evaluated as average-grade embryos. In some embodiments, the embryos are evaluated as low-grade embryos. In some embodiments, blood collection is performed before embryo transfer. Performing blood collection before embryo transfer captures a blood sample of the patient in a preimplantation state and thus provides a measurement of the patient's blood indicating whether the patient is in a PRE, WOI, or POST state.

[0207] At a point after in vitro fertilization treatment, a pregnancy test or the healthcare provider determines whether the embryo transfer was successful (e.g., whether the embryo implanted). This determination can be made by one or more of the following methods: blood test, urine test, ultrasound, or other methods.

[0208] In some embodiments, if embryo transfer is successful, subsequent in vitro fertilization is not performed. In some cases, it is understood that after a successful embryo transfer (e.g., embryo implantation), pregnancy may not reach full term for various reasons, including miscarriage. In such cases, the patient and clinician can determine, based on the successful embryo transfer, that the timing of the cycle in which the embryo transfer was performed is the implantation window. Thus, the patient and clinician can choose to repeat the same embryo transfer timing based on the successful embryo transfer. However, the patient and clinician may further order the tests described in the present invention to determine a predicted endometrial state or condition based on a blood sample for additional data. If blood is sequenced after a successful embryo transfer, the miRNA expression profile of the blood sample can be further used as additional training data for a model, where the endometrial state or condition is associated with the ground truth that it is WOI. In some embodiments, the miRNA expression profile determined from the blood collection / sample can be used as ground truth data for training a model classifier, where the endometrial state associated with the miRNA expression profile is the WOI state because the embryo transfer was successful.

[0209] In some embodiments, if embryo transfer is unsuccessful, a subsequent in vitro fertilization cycle is performed. In some embodiments, at this point, a sample from blood collection is sequenced for miRNA. From this sequencing, a miRNA expression profile is determined (e.g., by a computer-implemented method). Blood collection can be stored for miRNA sequencing according to methods known in the art (e.g., freezing, refrigeration). Waiting until this point can reduce patient costs. On the other hand, performing the test in the case of a positive pregnancy test provides an additional data point to the model for further training the model by relating the sample's miRNA expression profile to the WOI / receptive endometrial state, thereby knowing the correct answer that embryo transfer was successful.

[0210] Next, this method can determine the patient's endometrial state by inputting the miRNA expression profile obtained from blood sampling into the model. The model outputs a decision on whether the miRNA expression profile indicates that the patient is pre-receptive or post-receptive. If the model outputs post-receptive, embryo transfer in the subsequent IVF cycle can be shifted to either P+3 or 5 days before progesterone exposure (e.g., P+1, P+2, P+3, P+4). If the model outputs pre-receptive, embryo transfer in the subsequent IVF cycle can be shifted to either P+7 or 5 days after progesterone exposure (e.g., P+6, P+7, P+8, P+9, P+10).

[0211] In some embodiments, it may be understood that the model outputs probabilities for each state when embryo transfer is unsuccessful in an in vitro fertilization cycle. In these cases, subsequent in vitro fertilization cycles may be led by a higher probability state between PRE and POST, except for the WOI outcome.

[0212] In some embodiments, the in vitro fertilization (IVF) cycle includes patient blood collection and subsequent embryo transfer, but does not include simultaneous endometrial sampling. Thus, this IVF cycle differs from conventionally used "mock cycles" where the patient receives hormone replacement therapy, an endometrial sample is taken to determine the state of the endometrium, and embryo transfer is not performed in that "mock cycle." Advantageously, the patient can avoid the additional procedure of endometrial sample extraction and attempt embryo transfer in an IVF cycle with hormone replacement therapy. In some embodiments, if the transfer in the IVF cycle is successful, the blood sample can be used to further train a predictive model by associating it with the endometrial state of WOI. In some embodiments, if the transfer in the IVF cycle is successful, the patient can avoid additional cycles of hormone replacement therapy compared to treatment in a "mock cycle" where embryo transfer is performed in an additional cycle. In some embodiments, if the transfer in the IVF cycle is unsuccessful, a subsequent IVF cycle is performed with embryo transfer on a predicted embryo transfer date determined by the model.

[0213] In some embodiments, the first in vitro fertilization cycle involves performing a standard embryo transfer on P+5, or day 5 of progesterone administration. Blood is drawn on P+5, and optionally on P+4. After blood collection, the samples are pre-processed for storage (e.g., processed into plasma), transported to a storage facility, and stored for one year (e.g., in a refrigerator or freezer). Embryo transfer is performed after blood collection on P+5.

[0214] In some embodiments, a pregnancy test is performed late in the cycle after embryo transfer. In some embodiments, if the test is positive (e.g., the patient is pregnant after embryo transfer), no further cycles are necessary. In some embodiments, if the test is negative (e.g., the patient is not pregnant after embryo transfer), the patient can have a blood sample taken during embryo transfer analyzed immediately. If the patient chooses to do so, the blood can be analyzed and a report can be provided before the next in vitro fertilization cycle. In some embodiments, the blood can be analyzed and a report can be issued within 12-16 days.

[0215] Clinical and Computational Methods—Training Machine Learning Models Figure 13 shows an exemplary embodiment of predictive model training. The illustrated training data includes miRNA expression profiles of blood samples and the endometrial status or pregnancy outcome of those samples. Optional input / predictive data include patient age, body mass index (BMI), history of implantation failure, or pregnancy history. Patient ID serves as an index to identify each patient, expression profile, demographic and medical data, and endometrial outcome, but is not part of the predictive data used to train the model.

[0216] The training data includes input data (e.g., miRNA expression profiles, and optionally patient age, BMI, history of infertility, and pregnancy history) and class data of endometrial state associated with the patient at the time of sample collection. The input data and class data are provided to the predictive model during the training process. After training, the predictive model learns to associate any new input data, having at least miRNA expression profiles and optional selective input data, with predictions of endometrial state. In some embodiments, the predictive model may include multiple machine learning models, such as classifiers. Examples of such classifiers include logistic regression classifiers, perceptrons, naive Bayes, decision trees, support vector machines, K-nearest neighbor classifiers, or random forest classifiers. The training process may include training one or more of these machine learning models, such as classifiers, trained in the manner described above. It is understood that other machine learning models other than classifiers, such as neural networks, may be employed.

[0217] In some embodiments, miRNA expression profiles can be later associated with endometrial states within a model to train or fine-tune the trained model. In other words, as more in vitro fertilization is performed on patients, miRNA data (e.g., anonymized) can be provided to further train the model. By providing miRNA expression profiles and associated endometrial states to a training set or trained model, the model is trained or further trained to associate miRNA expression profiles measured in blood samples with the ground truth of the measured endometrial states.

[0218] In some embodiments, the trained predictive model is stored in a network environment on one or more servers. In some embodiments, the servers comply with the Health Insurance Portability and Accountability Act (HIPAA). In some embodiments, the servers are cloud providers such as Amazon AWS® or Azure®.

[0219] In some embodiments, sample preparation includes sample preparation and plasma miRNA extraction. The method then executes the experimental protocol and sets up a predictive model. Samples are amplified and used to set up a library (e.g., a training dataset), which builds the predictive model. Quality control is performed on the model by validating miRNA levels and sequencing levels (e.g., normalization, expansion, qualification, conversion) based on total read count, total miRNA read count, spike-in control, and detectable miRNA levels. In some embodiments, quality control is performed before model construction. After model construction, it performs validation and verification (e.g., by 10-fold cross-validation). Validation includes determining reproducibility, repeatability, accuracy, sensitivity, specificity, precision, local observation-based validation, and interference.

[0220] In some embodiments, data input is provided for miRNA expression profile information read by next-generation sequencing (NGS) testing. The miRNA expression profile output by NGS is the number of reads for each miRNA found in the sample.

[0221] A five-step process handles the raw data input and prepares it for transmission to the predictive model. However, it is understood that the model can read other forms of data, such as unnormalized raw read counts, and these steps can be performed or omitted in any combination. First, the data is normalized by converting the number of miRNA sequencing reads to the ratio of the number of reads for each miRNA to the total number of miRNA reads. Second, targets are qualified by selecting miRNAs that have stable expression patterns and reliable ratio values ​​between samples. Third, the data is transformed by converting the miRNA ratio values ​​to a simply processed format that fits the model system.

[0222] Fourth, the data is modeled by establishing predictive models in different model systems and evaluating the modeling results. In some embodiments, the model system is one or more classifiers trained to determine a patient's PRE, WOI, or POST status based on miRNA expression profiles. An example of a training dataset includes a set of miRNA expression profiles, each of which is associated with an endometrial state. The endometrial state is expressed as follows:

number

[0223] Next, the model is trained to output results to the provider. Once trained, upon receiving a miRNA expression profile, the model outputs a PRE, POST, or WOI result based on the most likely state based on the input profile.

[0224] In some embodiments, the first stage of data processing (NGS data) is performed. Next, a predictive model is trained based on the data, and once trained, the model can output results.

[0225] Data input of miRNA expression profile information read by next-generation sequencing (NGS) is provided for training. The raw data can be expanded to include technical iterations to remove batch-induced variance for each sample. Targets are qualified by selecting miRNAs with stable expression patterns and reliable ratio values ​​between samples. The data is transformed by converting the miRNA ratio values ​​into a simply processed format that fits the model system. The data is then normalized by converting the number of miRNA sequencing reads to the ratio of the number of reads for each miRNA to the total number of miRNA reads. The miRNA expression profile output by NGS is the number of reads for each miRNA found in the sample. It is understood that these steps may occur in any order or combination, or that some or all of these steps may be omitted.

[0226] Model training is performed by associating the read ratio of each miRNA expression profile, the probability of each stage, and a representative endometrial stage for each miRNA as a training dataset. The training data is provided to one or more models for training. An example of a training dataset includes a set of miRNA expression profiles, where each miRNA expression profile is associated with an endometrial state. The endometrial states are expressed as follows:

number

[0227] The models to be trained may include classifiers such as logistic regression classifiers, random forest classifiers, and k-nearest neighbor classifiers. The constructed models are cross-validated in 10 parts for hyperparameter tuning by using logistic regression, random forest, and k-nearest neighbor models. The resulting predictive models are one or more classifiers (e.g., logistic regression, random forest, k-nearest neighbors) trained to determine a patient's PRE, WOI, or POST status based on their miRNA expression profile.

[0228] Once trained, upon receiving a miRNA expression profile, the model outputs a PRE, POST, or WOI result based on which state is most likely based on the input profile. In some embodiments, the model outputs the probability of each state or one or more states.

[0229] Clinical and Computational Methods - Using Pre-trained Models Figure 14 shows an exemplary embodiment using a pre-trained predictive model. The pre-trained predictive model is trained in the manner described in relation to Figure 14. The pre-trained predictive model can be stored in a network environment on one or more servers. In some embodiments, the servers comply with the Health Insurance Portability and Accountability Act (HIPAA). In some embodiments, the servers are cloud providers such as Amazon AWS® or Azure®.

[0230] A patient or clinician can submit patient data to a trained predictive model. While the patient data belongs to the same category as the training data, it is understood that the data itself is novel compared to the training set. The trained predictive model (e.g., via a machine learning model / classifier) ​​outputs probabilities for each endometrial state: PRE, WOI, and POST. In some embodiments, the model may output probabilities for PRE and WOI, PRE and POST, and other combinations of probabilities including WOI and POST. In some embodiments, different models, including different types of classifiers disclosed herein, may compute the probabilities for each state or combination of states. A selection module determines the state with the highest probability among the output probabilities. The selection module outputs the state with the highest probability that best represents the patient's endometrial state at the time of sampling. The patient report then includes the endometrial state with the highest probability.

[0231] In some embodiments, it is understood that a trained predictive model can select an endometrial state, such as PRE, WOI, or POST, based on non-probability metrics, such as direct selection without outputting intermediate probabilities.

[0232] In some embodiments, the selection module and patient reports can be generated on a cloud server, a different server, or a local machine that houses the patient data. In some embodiments, the patient reports can be generated on a machine different from the machine that generates the selection module.

[0233] In some embodiments, the machine learning model can be used in the following manner. The first model receives a patient's miRNA expression profile and outputs whether the sample is in the POST state. If the first model outputs that POST is the most likely state, the patient's report indicates that the endometrial state is POST, and the second model may not be used. However, if the first model indicates that the endometrial state is either WOI or PRE, the second model also receives the miRNA expression profile. The second model outputs whether the endometrial state is either WOI or PRE, and the patient's report indicates that endometrial state. In some embodiments, the model can be any type of classifier. It is understood that in some embodiments, the second model can also be used if the first model outputs that POST is the most likely state.

[0234] In some embodiments, if the model outputs that PRE is the most likely state, the patient's report indicates that the endometrial state is PRE, and the second model may not be used. However, if the first model indicates that the endometrial state is either WOI or POST, the second model also receives the miRNA expression profile. The second model outputs whether the endometrial state is either WOI or POST, and the patient's report indicates that endometrial state. In some embodiments, the model can be any type of classifier. It is understood that in some embodiments, the second model can also be used if the first model outputs that POST is the most likely state.

[0235] In some embodiments, more than two models can be used.

[0236] Clinical and computational methods - Use of models in the clinical setting Figure 15 is a diagram showing an exemplary embodiment of the method employed by the present invention. An IVF cycle of in vitro fertilization treatment is performed on a patient at a clinic, hospital, IVF center, or other medical center. The clinic performs hormone replacement therapy. The clinic performs blood sampling on P+5 days and optionally P+4 days after progesterone (e.g., or other trigger drugs) is administered. The blood sampling is pre-treated and transported to a storage facility where it is stored (e.g., refrigerated or frozen) for a period (e.g., 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 13 months, 14 months, 15 months, 16 months, 17 months, 18 months, 2 years, 2.5 years, 3 years, or other period) requested by the patient or the clinic. After the blood sampling on P+5 days, embryo transfer is performed. In some embodiments, the blood sampling is processed immediately.

[0237] In an in vitro fertilization cycle of in vitro fertilization treatment, a pregnancy test is performed on the patient. If the patient is pregnant, the patient is already pregnant, so no subsequent in vitro fertilization cycle is needed. However, if the patient is not pregnant, the patient or the clinician can choose to request an analysis of the blood sample taken before embryo transfer. If the patient is not pregnant, the sample is sent to a sequencing laboratory. The miRNA sequence data is provided to a trained prediction model, and the model processes the data and generates a report to be distributed to the clinician or the patient. The report includes a predicted embryo transfer date based on whether the previous blood sample indicated a PRE or POST state of the endometrium. In some embodiments, the model outputs a prediction of the state of the endometrium (e.g., PRE before the window of implantation (WOI), WOI, or POST after WOI). From that prediction, the report and / or the healthcare provider can determine whether to transfer the embryo after P+5 (e.g., if the prediction is PRE), transfer the embryo at P+5 (e.g., if the prediction is WOI), or transfer the embryo before P+5 (e.g., if the prediction is POST).

[0238] In some embodiments, the option to generate a report is automated, and at the patient's discretion, the logistics of having a blood sample analyzed for miRNA expression profiles, analyzing the miRNA expression profiles in a model to determine the state of the endometrium, and generating a report are performed without any additional work by the patient or clinician after the report has been ordered.

[0239] In subsequent in vitro fertilization (IVF) cycles, embryo transfer is performed on the predicted embryo transfer day. In some embodiments, the subsequent IVF cycle may include the same events as the IVF cycle but use the timing of the predicted embryo transfer day (e.g., with blood sampling immediately before embryo transfer on the predicted embryo transfer day). However, for brevity, Figure 15 shows embryo transfer and pregnancy testing.

[0240] Clinical and Computational Methods - Computing Hardware and Environments Applicable to the Systems and Methods of the Invention Figure 16 shows a schematic diagram of an example computing node. Computing node 10 is merely an example of a suitable computing node and is not intended to imply any limitations on the scope or functionality of the embodiments described herein. Nevertheless, computing node 10 is capable of implementing and / or performing any of the functionalities described above.

[0241] Computing node 10 contains a computer system / server 12, which can operate with a number of other general-purpose or specialized computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations that may be suitable for use with computer system / server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices.

[0242] The computer system / server 12 can be described in the general context of computer system executable instructions, such as program modules, that are executed by the computer system. Generally, a program module may include routines, programs, objects, components, logic, data structures, etc., that perform a specific task or implement a specific abstract data type. The computer system / server 12 may be implemented in a distributed cloud computing environment where tasks are executed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules may reside on both local and remote computer system storage media, including memory storage devices.

[0243] Refer to Figure 16. The computer system / server 12 within the computing node 10 is shown in the form of a general-purpose computing device. The components of the computer system / server 12 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and a bus 18 connecting various system components, including the bus 18 that connects the system memory 28 to the processor 16.

[0244] Bus 18 represents one or more of several types of bus structures, including memory buses or memory controllers, peripheral buses, accelerated graphics ports, and processor or local buses using one of various bus architectures. Examples of such architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Extended ISA (EISA) bus, VESA (Video Electronics Standards Association) local bus, Peripheral Component Interconnect (PCI) bus, PCI Express (Peripheral Component Interconnect Express) and Advanced Microcontroller Bus Architecture (AMBA).

[0245] The computer system / server 12 typically includes various computer system-readable media. Such media can be any available media accessible by the computer system / server 12, and include both volatile and non-volatile media, and removable and non-removable media.

[0246] The system memory 28 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer system / server 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. For example only, a storage system 34 for reading and writing to a non-removable, non-volatile magnetic medium (not shown, commonly referred to as a “hard drive”) may be provided. Not shown, a magnetic disk drive for reading and writing to removable, non-volatile magnetic disks (e.g., “floppy disks”) and an optical disk drive for reading and writing to removable, non-volatile optical disks such as CD-ROMs, DVD-ROMs or other optical media may be provided. In such cases, each may be connected to the bus 18 by one or more data medium interfaces. As further illustrated and described below, the memory 28 may include at least one program product having a set of program modules (e.g., at least one) configured to perform the functions of embodiments of the present invention.

[0247] A program / utility 40 having a set (at least one) of program modules 42 may be stored in memory 28, for example, as well as an operating system, one or more application programs, other program modules, and program data, for example, but not limited to these. Each of the operating system, one or more application programs, other program modules, and program data, or any combination thereof, may include an implementation of a networking environment. The program modules 42 generally perform functions and / or methodologies of embodiments such as those described herein.

[0248] The computer system / server 12 may also communicate with one or more external devices 14, such as a keyboard, pointing device, display 24, one or more devices that enable a user to interact with the computer system / server 12, and / or any devices that enable the computer system / server 12 to communicate with one or more other computing devices (e.g., a network card, modem, etc.). Such communication may occur via an input / output (I / O) interface 22. Furthermore, the computer system / server 12 may communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN), and / or a public network (e.g., the Internet), via a network adapter 20. As illustrated, the network adapter 20 communicates with other components of the computer system / server 12 via a bus 18. It should be understood that other hardware and / or software components, not illustrated, may be used in conjunction with the computer system / server 12. These include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems.

[0249] The present invention may be embodied as a system, method, and / or computer program product. The computer program product may include a computer-readable storage medium (or medium) having computer-readable program instructions thereon for causing a processor to perform an aspect of the present invention.

[0250] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by instruction-executing devices. Computer-readable storage media may be, but are not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of these. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital multipurpose disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or grooved raised structures on which instructions are recorded, and any suitable combination of these. The computer-readable storage media used herein should not be interpreted as being transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through optical fiber cables), or electrical signals transmitted through wires.

[0251] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium within each computing / processing device.

[0252] The computer-readable program instructions for performing the operations of the present invention may be either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk and C++, and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or wide area network (WAN), or the connection may be made to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, an electronic circuit including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) may execute a computer-readable program instruction by personalizing the electronic circuit using state information of the computer-readable program instruction in order to perform an aspect of the present invention.

[0253] Aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block in the flowcharts and / or block diagrams, as well as combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0254] These computer-readable program instructions can be provided to the processor of a general-purpose computer, a dedicated computer, or other programmable data processing device to generate a machine, which in turn creates means for instructions executed via the processor of the computer or other programmable data processing device to implement functions / operations specified by blocks or multiple blocks in a flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that can instruct computers, programmable data processing devices, and / or other devices to function in a particular manner, which in turn constitutes a product containing instructions that implement modes of functions / operations specified by blocks or multiple blocks in a flowchart and / or block diagram.

[0255] Computer-readable program instructions can also be loaded into a computer, other programmable data processing device, or other device to execute a series of operational steps on the computer, other programmable device, or other device to generate a computer implementation process, in which the instructions executed on the computer, other programmable device, or other device implement the functions / operations specified by blocks or multiple blocks in a flowchart and / or block diagram.

[0256] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or part of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions described in a block may occur in an order different from the order shown in the diagram. For example, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, as well as combinations of blocks in a block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs a specified function or operation, or a combination of dedicated hardware and computer instructions.

[0257] In some embodiments, feature vectors are provided to a machine learning model (e.g., a learning system, an artificial intelligence system, or an artificial intelligence model). Based on the input features, the learning system generates one or more outputs. In some embodiments, the output of the learning system is a feature vector. It is understood that miRNA expression profiles may be formatted as feature vectors to the machine learning models described herein. It is further understood that input data, including miRNA expression profiles and any age, body mass index, implantation failure history, and pregnancy history, may also be input as one or more feature vectors of one or more dimensions. It is further understood that output data, including probabilities of PRE, POST, and WOI, may be output as feature vectors.

[0258] In some embodiments, the learning system includes an SVM. In other embodiments, the learning system includes an artificial neural network. In some embodiments, the learning system is pre-trained using training data. In some embodiments, the training data is backward data. In some embodiments, the backward data is stored in a data store. In some embodiments, the learning system can be additionally trained through manual curation of previously generated outputs.

[0259] In some embodiments, the learning system is a trained classifier. In some embodiments, the trained classifier is a random decision forest. However, it is understood that there are various other classifiers suitable for use according to the present invention, including linear classifiers, support vector machines (SVMs), random decision forests (e.g., random forest classifiers), clustering models, decision trees, nearest neighbors (e.g., K-nearest neighbors), binary classification, naive Bayes, or neural networks such as recurrent neural networks (RNNs).

[0260] Suitable artificial neural networks include, but are not limited to, feedforward neural networks, radial basis function networks, self-organizing maps, learning vector quantization, recurrent neural networks, Hopfield networks, Boltzmann machines, echo state networks, long short-term memory, bidirectional recurrent neural networks, hierarchical recurrent neural networks, probabilistic neural networks, modular neural networks, associative neural networks, deep neural networks, deep belief networks, convolutional neural networks, convolutional deep belief networks, large-capacity memory / search neural networks, deep Boltzmann machines, deep stacking networks, tensor deep stacking networks, spike-and-slab restricted Boltzmann machines, composite hierarchical deep models, deep encoding networks, multi-layer kernel machines, or deep Q-networks.

[0261] An artificial neural network (ANN) is a distributed computing system composed of a large number of neurons interconnected through connection points called synapses. Each synapse encodes the strength of the connection between the output of one neuron and the input of another neuron. The output of each neuron is determined by the aggregated inputs it receives from other neurons connected to it. Thus, the output of a given neuron is based on the outputs of connected neurons from preceding layers and the strength of the connection, which is determined by synaptic weights. An ANN is trained to solve a specific problem (e.g., pattern recognition) by adjusting the synaptic weights so that a particular class of input produces a desired output.

[0262] Various methods can be used in this learning process. Certain methods may be suitable for specific tasks such as image recognition, speech recognition, or natural language processing. The training method yields a pattern of synaptic weights that converges to the optimal solution of the given problem during the learning process. Backpropagation is one method suitable for supervised learning, where known correct outputs are available during the learning process. The goal of such learning is to obtain a system that generalizes to data that was not available during training.

[0263] Generally, during backpropagation, the network output is compared to a known correct output. An error value is calculated for each neuron in the output layer. The error values ​​are propagated backward, starting from the output layer, to determine the error value associated with each neuron. The error values ​​correspond to each neuron's contribution to the network output. The error values ​​are then used to update the weights. Through this stepwise correction, the network output is adjusted to fit the training data.

[0264] When backpropagation is applied, the ANN quickly achieves high accuracy for most examples in the training set. The majority of training time is spent further improving the accuracy of this test. During this time, most of the training data examples provide little correction because the system has already learned to recognize those examples. Generally, the performance of an ANN tends to improve with the size of the dataset, which can be explained by the fact that larger datasets contain more boundary examples between different classes on which the ANN is being trained.

[0265] The descriptions of various embodiments of the present invention are presented for illustrative purposes only and are not intended to be exhaustive or limiting to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terms used herein have been selected to best describe the principles of the embodiments, their practical applications or technical improvements to technologies available on the market, or to enable those skilled in the art to understand the embodiments disclosed herein.

[0266] Embodiment In some embodiments, the present invention provides a method for determining the endometrial condition of a subject requiring such determination. The method includes the following: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determine the microRNA (miRNA) expression profile in the sample. (c) Input the miRNA expression profile from step (b) into the trained machine learning model and generate a report. (d) Determine the condition of the subject's endometrium based on the report.

[0267] In some embodiments, the present invention provides a method for determining the state of the endometrium of a subject who requires it during a first menstrual cycle. The method includes the following: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determine the microRNA (miRNA) expression profile in the sample. (c) Input the miRNA expression profile from step (b) into the trained machine learning model and generate a report. (d) Determine the condition of the subject's endometrium based on the report.

[0268] In some embodiments, the present invention provides a method for determining when a subject is receptive to embryo transfer. The method includes the following: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determine the microRNA (miRNA) expression profile in the sample. (c) Input the miRNA expression profile from step (b) into the trained machine learning model and generate a report. (d) Identifying the endometrial condition of the subject based on the report. (e) Determining when the subject is receptive to embryo transfer based on the condition of the subject's endometrium.

[0269] In some embodiments, the present invention provides a method for determining when a subject is receptive to embryo transfer during a first menstrual cycle. The method includes the following: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determine the microRNA (miRNA) expression profile in the sample. (c) Input the miRNA expression profile from step (b) into the trained machine learning model and generate a report. (d) Identifying the endometrial condition of the subject based on the report. (e) Determining when the subject is receptive to embryo transfer based on the condition of the subject's endometrium.

[0270] In some embodiments, the present invention provides a method for determining when a subject is receptive to embryo transfer, the method including: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determine the microRNA (miRNA) expression profile in the sample. (c) Input the miRNA expression profile from step (b) into the trained machine learning model and generate a report. (d) Identifying the endometrial condition of the subject based on the report. (e) Determining when the subject is receptive to embryo transfer based on the condition of the subject's endometrium. (f) Generate an analytical report including the patient's endometrial receptivity and the recommended timing for embryo implantation.

[0271] In some embodiments, the present invention provides a method for determining when a subject is receptive to embryo transfer during a first menstrual cycle. The method includes the following: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determine the microRNA (miRNA) expression profile in the sample. (c) Input the miRNA expression profile from step (b) into the trained machine learning model and generate a report. (d) Identifying the endometrial condition of the subject based on the report. (e) Determining when the subject is receptive to embryo transfer based on the condition of the subject's endometrium. (f) Generate an analytical report including the patient's endometrial receptivity and the recommended timing for embryo implantation.

[0272] In some embodiments, the present invention provides a method for determining when a subject is receptive to embryo transfer during a first menstrual cycle. The method includes the following: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determine the microRNA (miRNA) expression profile in the sample. (c) Input the miRNA expression profile from step (b) into the trained machine learning model and generate a report. (d) Identifying the endometrial condition of the subject based on the report. (e) Determining when the subject is receptive to embryo transfer based on the condition of the subject's endometrium. (f) The embryo is implanted in the subject during a subsequent menstrual cycle, where the embryo is implanted during a period identified in steps (a) to (e) from the first menstrual cycle as a time when the subject is receptive to embryo transfer.

[0273] In some embodiments, the present invention provides a method for determining when a subject is receptive to embryo transfer during a first menstrual cycle. The method includes the following: (a) Obtaining a sample from a subject or using a sample obtained from a subject. (b) Determine the microRNA (miRNA) expression profile in the sample. (c) Input the miRNA expression profile from step (b) into the trained machine learning model and generate a report. (d) Identifying the endometrial condition of the subject based on the report. (e) Determining when the subject is receptive to embryo transfer based on the condition of the subject's endometrium. (f) Based on steps (a) to (e), generate an analytical report including the patient's endometrial receptivity and the recommended timing for embryo implantation, and provide the report to the physician. (g) The embryo is implanted in the subject during a subsequent menstrual cycle. The embryo is implanted during the recommended period, which was identified in the report from the first menstrual cycle as a time when the subject is receptive to embryo transfer.

[0274] In some embodiments, the present invention provides a method that includes the following: Obtain a blood sample from the patient during an in vitro fertilization (IVF) cycle. This blood sample is collected from the patient on the embryo transfer day of the IVF cycle. To determine the predicted endometrial state of the patient at the time of embryo transfer based on the miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on the miRNA expression profile data derived from the blood sample. To generate a predicted embryo transfer date based on the predicted state of the uterine lining. The patient is to undergo a subsequent in vitro fertilization cycle by performing embryo transfer on the predicted embryo transfer date.

[0275] In some embodiments, the present invention provides a method that includes the following: Obtain a blood sample from the patient during the first in vitro fertilization (IVF) cycle. This blood sample is collected from the patient on the embryo transfer day and before embryo transfer in the IVF cycle. To determine the predicted endometrial state of the patient at the time of embryo transfer based on the miRNA expression profile from the blood sample, based on a machine learning model trained to determine the endometrial state based on the miRNA expression profile data derived from the blood sample. To generate a predicted embryo transfer date based on the predicted state of the uterine lining. Perform a pregnancy test on the patient. If the pregnancy test is negative, The patient undergoes a subsequent in vitro fertilization cycle by performing embryo transfer on the predicted embryo transfer date determined by a machine learning model.

[0276] In some embodiments, the present invention provides a method that includes the following: To determine endometrial condition data for multiple patients. Here, the endometrial condition data is obtained from at least one of the following: endometrial samples and pregnancy outcomes. The individual miRNA expression profiles from the aforementioned multiple patients are associated with endometrial state data. Here, each patient's individual miRNA expression profile is associated with each patient's endometrial state. The process involves training a machine learning model based on associated miRNA expression profiles and associated endometrial state data. Here, the trained machine learning model is trained to output a predicted endometrial state based on the input miRNA expression profiles.

[0277] In some embodiments, the method described herein results in the determination of the patient's endometrial receptivity. In some embodiments, the patient's endometrial receptivity determined using the method described herein is provided in an analysis report. In some embodiments, the patient's endometrial receptivity determined using the method described herein is provided in an analysis report to the physician. In some embodiments, the patient's endometrial receptivity determined using the method described herein is provided in an analysis report including a recommended timing for embryo implantation. In some embodiments, the patient undergoes an IVF cycle using the recommended timing for embryo implantation provided in the analysis report. In some embodiments, the patient undergoes an IVF cycle using the recommended timing for embryo implantation provided in an analysis report including the endometrial receptivity determined using the method described herein.

[0278] In some embodiments, embryo transfer is fresh embryo transfer. In some embodiments, embryo transfer is frozen embryo transfer (FET).

[0279] Examples Example 1: Optimal implantation ability assay In recent years, identifying shifts in the implantation window (WOI) in patients with recurrent implantation failure (RIF) has become a crucial step in the in vitro fertilization (IVF) process to improve implantation success rates. Approximately 30% of infertile women have a shifted implantation window, contributing to IVF failure (see Figure 1A). The state of a patient's endometrium may be in the receptive, pre-receptive, or post-receptive stage (see Figures 1B and 1C). Several molecular assays and platforms have been developed to assess endometrial state. Previously, performing invasive biopsies to obtain endometrial tissue samples from IVF patients was the standard method for endometrial receptivity analysis (see Table 1). Generally, the WOI, or standard receptivity window, is approximately 19–21 days after the start of the menstrual cycle. However, a patient's optimal window may occur earlier or later than this timeframe (see Figure 1). Table 1 shows the endometrial implantation platform.

[0280] [Table 1]

[0281] The following examples describe the development of a non-invasive assay for predicting a patient's WOI using plasma samples. The method involves collecting a blood sample from the patient and performing next-generation sequencing to measure miRNAs. Using an analysis pipeline developed by the applicant, a miRNA biomarker panel for determining the WOI was discovered (workflow diagrams are shown in Figures 2A-2C). miRNAs are known to play a crucial role in the embryo implantation process. They function as biomarkers for predicting endometrial receptivity using biopsies and bodily fluids. The method described herein utilizes miRNAs present in the blood to provide information on various cellular functions for determining endometrial receptivity (see Figure 3).

[0282] The pipeline for using this clinical analysis is shown in Figures 2A and 2B and is described below. Blood test: Blood samples were collected from subjects. Blood samples were collected during hormone replacement therapy cycles or natural cycles. Blood samples were collected either 4 (96 hours) and 5 (120 hours) after progesterone administration in HRT cycles, or 6 (144 hours) and 7 (168 hours) after a surge in LH was detected in natural cycles, or 6 (144 hours) and 7 (168 hours) after human chorionic gonadotropin (hGC) administration. Blood samples were collected using at least a 21G needle to minimize hemolysis. Hemolysis was monitored during the pre-processing step of processing blood into plasma, as hemolysis distorts data analysis. Pre-processing: Whole blood was processed into plasma. This process was performed at the clinic where the blood was collected. Hemolysis was monitored during the pre-processing stage because it distorts data analysis. Sample extraction: The pre-treated plasma was then sent to the laboratory for RNA extraction. Library preparation: The extracted miRNAs were prepared for next-generation sequencing. Sequencing: Next-generation sequencing was performed on the sample. Data analysis: miRNAs measured in the samples were analyzed using an ORA predictive model. Final report: Based on ORA analysis, samples were classified into one or four endometrial state groups, an inconclusive result group, or a non-effective / insufficient RNA group (less than 1% of samples would result in an inconclusive or non-effective / insufficient RNA outcome using ORA). The six groups are described below. a. Pre-receptive period: The endometrium was not yet ready for embryo implantation, and implantation at the time of blood collection was not ideal. Delaying embryo implantation during the next treatment cycle was recommended. Embryo transfer was delayed by 24 hours. b. Receptive period: The optimal time for embryo transfer is when blood is drawn. c. Post-receptive period (short window): The endometrium had passed its optimal time for embryo implantation. It was recommended to adjust the timing of embryo implantation by 12 hours during the next treatment cycle. d. Post-receptive period (average window): The endometrium had passed its optimal time for embryo implantation. It was recommended to adjust the timing of embryo implantation by 24 hours during the next treatment cycle. e. Inconclusive results: The obtained data did not match ORA's methods and databases, and the analysis could not be carried out. In this case, it may be due to pre-existing physiological conditions or variations that occurred during the sample submission process. f. Invalid / insufficient RNA: The concentration of the extracted substance (miRNA) was too low to obtain a result. Blood sampling needs to be repeated in the hope of obtaining a higher concentration to advance the ORA. Embryo transfer: The healthcare provider performed embryo transfer during the next treatment cycle based on the ORA test results.

[0283] Study group The study population consisted of a cohort of 184 subjects collected between January 2021 and December 2022. Inclusion criteria included age between 21 and 45 years, absence of ovulation disorders such as endometriosis, uterine fibroids, polyps, or hydrosalpinx, and a body mass index (BMI) > 18.5 kg / m². 2(See Table 2). Subjects were divided into a predictive model construction dataset (111 samples) and a validation dataset (73 samples). Subjects were also required to have more than one good-grade embryo. Both endometrial tissue and peripheral blood samples were collected during a mock hormone replacement therapy cycle 5 days (approximately 116-117 hours) after the start of progesterone administration. To identify the endometrial stage of the implantation window, endometrial tissue samples were tested with commercially available, off-the-shelf endometrial receptivity tests. Subsequently, embryos were transferred in the next hormone replacement therapy (HRT) cycle based on the endometrial receptivity test results (i.e., transferred on day 4, day 5, or day 6 based on biopsy results) (see Figure 4). If implantation was successful, corresponding patient blood samples were incorporated for additional analysis of miRNA expression patterns and establishment of predictive models. In addition to miRNA expression patterns, clinical information such as age, body mass index (BMI), and history of implantation failure was collected to optimize the accuracy of the predictive models. Table 2 shows the clinical characteristics of the predictive model construction dataset and the validation dataset.

[0284] [Table 2]

[0285] Uterine sample collection Uterine fluid (UF) was obtained by rinsing the uterus with 0.5 ml of phosphate-buffered saline (PBS) for 30 seconds, followed by aspiration of the fluid. This procedure was performed using an intrauterine insemination catheter (Cooper Surgical, Connecticut, USA) inserted into the uterine cavity through the cervix, while avoiding contact with the uterine fundus.

[0286] UF samples were obtained by lavage of the endometrial cavity with 2.5 ml of sterile saline using a balloon hysteroscopy catheter to avoid vaginal contamination.

[0287] Plasma sample collection Peripheral blood samples (5–10 ml per subject) were obtained from subjects undergoing hormone replacement therapy cycles. Blood samples were collected in EDTA tubes (BD, USA, Cat. No. 367525) or plasma preparation tubes (BD, USA, Cat. No. 362788). After blood sample collection, the tubes were inverted at least five times for mixing and processed within 1–2 hours. Next, each sample was centrifuged at 1200 g for 10 minutes at room temperature to separate the plasma from residual cells. The supernatant was transferred to a new tube and centrifuged at 12000 g for 10 minutes. Finally, the plasma samples were transferred to a new tube and stored at -80°C.

[0288] The methods for extracting and sequencing miRNAs are described in the following examples.

[0289] Example 2: Method for extracting plasma miRNA Plasma samples are thawed on ice from a -80°C freezer. miRNAs are extracted from the plasma samples using the miRNeasy Serum / Plasma Advanced Kit (Qiagen, Germany, Cat. No. 217204) according to the manufacturer's protocol. The extraction method includes the following:

[0290] i) Add 200-600 μL of plasma sample to produce more than 10 ng of plasma miRNA. and

[0291] ii) Perform a pre-amplification process.

[0292] The extraction spike-in control "cel-miR-2-3p" used for miRNA extraction is prepared in 10 units of RPL buffer (Buffer RPL). 8The sample contains RNA oligos with SEQ ID NO: 1 ( / 5Phos / rUrArUrCrArCrArGrCrCrArGrCrUrUrUrGrArUrGrUrGrC) at a copy / μL level. The extracted spike-in control was measured for the total copy number of RNA oligos using Qubit fluorescence quantitative analysis according to the manufacturer's method, and the RNA oligos were tested in nuclease-free water containing 10 ng / μL of yeast tRNA (Invitrogen Catalog No. AM7119). 12 It is prepared by diluting to copies / μL. RNA oligos are 10 oz in nuclease-free water for use in miRNA extraction protocols. 12 ~10 8 The sample is further diluted to copies / μL. After dilution, the spike-in control is mixed with RPL buffer, and extraction is performed according to the manufacturer's protocol. After extraction, the quality of the miRNA sample is analyzed using the manufacturer's method of Qubit fluorescence quantitative analysis (Thermo Fisher Cat. No. Q32880).

[0293] Example 3: Sequencing of miRNAs in plasma The RNA extracted in Example 1 was prepared for next-generation sequencing.

[0294] Library preparation

[0295] The miRNA sequencing library was constructed using the QIAseq miRNA Library Kit (QIAGEN, Germany, Cat. No. 331502). RNA was enriched, and 3' ligation was performed on the prepared miRNA samples. Immediately after 3' ligation, 5' ligation of the miRNA was performed. Next, the samples were reverse transcribed using reverse transcription primers with molecular barcodes (UMI). cDNA cleanup was performed on the samples. The samples were then stored at -20°C until library sample indexing and amplification. Library amplification mixes were prepared, and the samples were run on a thermal cycler program to amplify the products. Immediately after amplification, library amplification cleanup was completed. Library quality was checked using the 5200 Fragment Analyzer System (Agilent Technologies). The size of the library products was 190–220 bp (base pair). The library was quantified using Quibit (Thermo Fisher Scientific, USA, Cat. No. Q32851). The concentration used for sequencing was greater than 1 ng / μL.

[0296] Sequencing The miRNA library was sequenced on a NextSeq 550 using the Illumina NextSeq 550 System Sequencing Reaction protocol. The data analysis pipeline included the following protocols: 1) Raw fastq data was preprocessed with quality control, including trimming of adapter sequences and removal of low-quality reads using FastQC and Trimmomatic. In addition to adapter removal, low-quality (Q-value < 20) ends were trimmed from the reads, and reads shorter than 17 bp or longer than 55 bp were discarded. 2) The processed reads were aligned to a specific set of small RNA sequences from the Human Genome Assembly GRCh38 (hg38) reference genome and miRBase using an aligner such as Bowtie. 3) Reads extracted from the data were quantified by mapping aligned reads using samtools and obtaining reference annotations using miRBase. The number of reads for each miRNA was used as an expression value for further data analysis. The total number of miRNAs was calculated by summing the values ​​of all miRNAs in each sample. The proportion of each miRNA was calculated by dividing the number of reads for each miRNA by the total number of miRNAs. The ratio was obtained by multiplying the resulting value by 1,000,000. After data normalization, the log2 transformation of the given final normalized values ​​was calculated. On average, miRNAs accounted for approximately 5% of the total sequencing reads. Therefore, no significant proportion of small RNAs was analyzed. Table 3 shows the sequencing results for the 111 samples used in the predictive model construction dataset. The average sequencing depth was 8,000,795×, and the average number of detectable miRNA reads was 395,433 reads, accounting for an average of 4.8% of the total sequencing reads. The average number of detectable miRNAs was 135. Table 3 shows the sequencing results for the prediction model construction dataset (111 samples).

[0297] [Table 3]

[0298] Example 4: Establishment of an optimal receptivity assay workflow After next-generation sequencing of miRNAs in patient plasma samples was completed, the implantation window (WOI) for subjects was identified using raw data. As shown in Figures 5 and 6, the computer-based optimal receptivity assay (ORA) analysis was constructed by performing one or more of the following steps: quality control, data normalization (converting the number of miRNA sequencing reads to a ratio of the total number of reads), target qualification (by selecting miRNA targets with stable expression patterns and reliable ratio values ​​across different patient samples), data transformation (converting the miRNA ratios to a processed format for use in the modeling system), predictive modeling (establishing the ORA model), and cross-validation (assessing accuracy by validating the predictive model with independent datasets; see Table 6). Quality control reviews the amount of miRNA used for sequencing, as well as the total number of reads from sequencing, total miRNA reads, spike-in control reads, and detectable miRNA count. Dataset validation using ORA examines precision, reproducibility, repeatability, upper limit of blanks (LoB), and interference. The dataset is validated by examining its accuracy, sensitivity, and specificity.

[0299] RNA sequencing data were grouped and analyzed based on different stages of receptivity determined by tissue-based endometrial receptivity testing. miRNAs from plasma samples were grouped based on receptive, pre-receptive, and post-receptive endometrial stages, and unsupervised clustering was used to demonstrate that miRNAs could distinguish between the three stages. This model used logistic regression with 10-fold cross-validation, comparing subjects' known pregnancy outcomes with blood samples taken for miRNA analysis. Furthermore, miRNA expression was combined with patient clinical characteristics such as age, BMI, and history of implantation failure. The accuracy of ORA for WOI determination is predicted to be approximately 98.1% (see Table 4). After establishing a predicted implantation window using MIRA, ORA was able to adequately predict the subjects' receptive window (see Table 5).

[0300] To validate the performance of the predictive model dataset, a validation dataset (see Table 2) was used, pre-determined by MIRA, containing 3 samples from the pre-receptive stage, 66 samples from the receptive stage, and 4 samples from the post-receptive stage. Each patient had achieved successful implantation. After analyzing all 73 samples using the predictive model, the overall accuracy was determined to be 95.9%. Specifically, accuracy of 95.9%, 95.9%, and 100.0% were achieved for the pre-receptive group, the receptive group, and the post-receptive group, respectively (see Table 6).

[0301] The ORA prediction model was based on 281 individual mRNAs (see Table 7). This model utilizes 216 novel miRNA biomarkers and 65 miRNA biomarkers previously identified using the MIRA method (U.S. Patent Publication No. 2021 / 0002698) (see Figure 7). Common biomarkers are associated with processes such as gland development, urogenital development, and responses to oxygen levels (see Figure 7).

[0302] Table 4 summarizes the performance of the predictive model construction dataset.

[0303] [Table 4]

[0304] Table 5 summarizes the performance validation of ORA.

[0305] [Table 5]

[0306] Table 6 summarizes the performance of the clinical validation dataset.

[0307] [Table 6]

[0308] Table 7 summarizes ORA miRNAs.

[0309] [Table 7] JPEG2026521129000011.jpg238160JPEG2026521129000012.jpg238160JPEG2026521129000013.jpg238160JPEG2026521129000014.jpg218160

[0310] The ORA method accurately predicts a subject's optimal implantation window. Known methods require invasive and painful tissue sampling, necessitating sample collection in the same operating room as the embryo transfer. Furthermore, these methods are costly and require mock cycles to determine the implantation window. The ORA method utilizes non-invasive blood sampling and eliminates the need for testing during mock cycles, thus reducing costs and minimizing the time required from testing to embryo transfer. For example, blood is collected before embryo transfer during the first IVF cycle for patients undergoing hormone therapy or in natural cycles. If the transfer is unsuccessful, the blood sample is processed and quantified using the ORA predictive model. Based on ORA's recommendations, personalized embryo transfer (pET) is performed during the next cycle. Thus, the time and cost associated with conventional mock cycles are eliminated (see Figure 8).

[0311] Analysis was performed to identify differentially expressed miRNAs in blood across different endometrial receptivity states. Expression patterns varied across different receptivity states (see Tables 8 and 9). Several miRNAs were identified that showed significant expression differences in patient blood across pre-receptive, receptive, and post-receptive groups. A set of miRNAs, including hsa-let-7b-5p, hsa-let-7g-5p, and hsa-miR-423-5p, showed decreasing expression levels from pre-receptive to receptive and post-receptive endometrial states (see Figure 9A). Furthermore, there are specific stages where certain miRNAs exhibit higher or lower expression levels. For example, in the post-receptive group, miRNAs such as hsa-miR-5585-5p, hsa-miR-629-5p, hsa-miR-3960, hsa-miR-191-5p, and hsa-let-7d-5p showed significantly lower expression levels compared to the pre-receptive and receptive groups. On the other hand, hsa-miR-122-5p showed significantly higher expression levels in the post-receptive group compared to the pre-receptive and receptive groups (see Figure 9B). Furthermore, miRNAs in the pre-receptive group, including hsa-miR-375-3p, hsa-miR-143-3p, and hsa-miR-12116, showed significantly higher expression levels than in the receptive group (see Figure 9C). Overall, the changes in miRNA expression levels indicate a complex regulation of gene expression during endometrial preparation for implantation.

[0312] Table 8 shows the results of the statistical analysis (miRNA expression levels) of miRNAs included in the predictive model construction dataset.

[0313] [Table 8] JPEG2026521129000016.jpg227160

[0314] Table 9 summarizes the results of the statistical analysis (Tukey multiple comparison test (Post Hoc Tukey HSD)) of miRNAs included in the predictive model construction dataset.

[0315] [Table 9] JPEG2026521129000018.jpg231160

[0316] In summary, a set of extracellular miRNAs was found to be stably expressed in the bloodstream and correlate with endometrial receptivity. By combining the expression of these miRNAs with patient clinical characteristics such as age, BMI, and history of implantation failure, a predictive model was constructed to accurately identify the implantation window. High predictive accuracy was observed compared to tissue-based endometrial receptivity tests. This non-invasive test method and miRNA-based predictive model may be used as an alternative to invasive tissue-based endometrial receptivity tests in the future.

[0317] In the past, endometrial receptivity has been analyzed through invasive tissue biopsy. This study provides a novel, non-invasive method for testing endometrial receptivity by analyzing extracellular miRNAs, which can be used as an alternative to tissue biopsy in patients undergoing assisted reproductive technology.

[0318] Example 5: Clinical trial for miRNA identification A clinical trial will be conducted to identify changes in miRNA expression profiles in healthy subjects receiving hormone replacement therapy. This clinical trial will include subjects aged 21–38 years with consistent menstrual cycles. During hormone replacement therapy, uterine biopsies and blood samples will be taken on days 3, 4, 5, 6, or 7 after progesterone injection. The samples will be processed for miRNA extraction and sequencing as described in Example 3. The resulting sequencing data will be analyzed using ORA to identify novel miRNA expression profiles to determine the implantation window in the subjects (see Figures 10A–10B).

Claims

1. A method for determining the endometrial condition of a subject whose endometrial condition needs to be assessed, (a) Obtaining a sample from a subject, or using a sample obtained from a subject, (b) Determining the microRNA (miRNA) expression profile in the sample, (c) Input the miRNA expression profile from step (b) into the trained machine learning model to generate a report, (d) Determining the endometrial condition of the subject based on the report, A method that includes [a certain feature].

2. The method according to claim 1, wherein the state of the endometrium is pre-receptive, receptive, or post-receptive.

3. The method according to claim 1 or 2, wherein, if the endometrial state is in the receptive stage, the subject is receptive to embryo transfer.

4. The method according to claim 1 or 2, wherein, if the state of the endometrium is pre-receptive, the subject becomes receptive to embryo transfer approximately 24 hours after the sample is obtained from the subject.

5. The method according to claim 1 or 2, wherein if the state of the endometrium is post-receptive, the subject has passed the receptive stage.

6. The method according to claim 5, wherein the subject is receptive to embryo transfer approximately 24 hours before the sample is obtained from the subject.

7. The method according to claim 5, wherein the subject is receptive to embryo transfer approximately 12 hours before the sample is obtained from the subject.

8. The method according to any one of claims 1 to 7, wherein the subject is infertile.

9. The method according to any one of claims 1 to 8, wherein the subject has a history of implantation failure, has few remaining high-quality embryos, has a BMI lower or higher than the normal range, is overweight, and / or underweight.

10. The method according to any one of claims 1 to 9, wherein the subject is undergoing in vitro fertilization.

11. The method according to any one of claims 1 to 9, wherein the subject is undergoing infertility treatment.

12. The method according to any one of claims 1 to 9, wherein the subject is undergoing assisted reproductive technology.

13. A method for determining when a subject is receptive to embryo transfer, (a) obtaining samples from subjects or using samples obtained from subjects, (b) Determining the microRNA (miRNA) expression profile in the sample, (c) Input the miRNA expression profile from step (b) into the trained machine learning model to generate a report, (d) To identify the endometrial condition of the subject based on the report, (e) A method comprising determining when a subject is receptive to embryo transfer based on the condition of the subject's endometrium.

14. The method according to claim 13, wherein the state of the endometrium is pre-receptive, receptive, or post-receptive.

15. The method according to claim 13 or 14, wherein, if the endometrial state is in the receptive stage, the subject is receptive to embryo transfer.

16. The method according to claim 15, further comprising implanting an embryo into the subject.

17. The method according to claim 13 or 14, wherein, if the state of the endometrium is pre-receptive, the subject becomes receptive to embryo transfer approximately 24 hours after the sample is obtained from the subject.

18. The method according to claim 17, further comprising implanting an embryo in the subject approximately 24 hours after a blood sample is obtained from the subject.

19. The method according to claim 13 or 14, wherein if the state of the endometrium is post-receptive, the subject has passed the receptive stage.

20. The method according to claim 19, wherein the subject is receptive to embryo transfer approximately 24 hours before the sample is obtained from the subject.

21. The method according to claim 19, wherein the subject is receptive to embryo transfer approximately 12 hours before the sample is obtained from the subject.

22. The method according to any one of claims 13 to 21, wherein each step is performed during a first menstrual cycle.

23. The method according to any one of claims 13 to 21, wherein the method is repeated during the subsequent menstrual cycle of the subject.

24. The method according to any one of claims 13 to 21, comprising transferring an embryo to the subject during a subsequent menstrual cycle, wherein the embryo is transferred at a time identified in the first menstrual cycle as a time when the subject is receptive to embryo transfer.

25. The method according to any one of claims 1 to 24, wherein the sample is obtained approximately 5 days after the start of progesterone administration during an assisted reproductive technology cycle.

26. The method according to any one of claims 1 to 24, wherein the sample is obtained about seven days after a surge in LH is detected in the subject.

27. The method according to any one of claims 1 to 24, wherein the sample is obtained approximately 7 days after the administration of hCG to the subject.

28. The method according to any one of claims 1 to 24, wherein the sample is obtained approximately 4 days and approximately 5 days after the start of progesterone administration during the assisted reproductive technology cycle.

29. The method according to any one of claims 1 to 24, wherein the sample is obtained approximately 6 days and approximately 7 days after a surge in LH is detected in the subject.

30. The method according to any one of claims 1 to 24, wherein the sample is obtained about 6 days and about 7 days after the administration of hCG to the subject.

31. The method according to any one of claims 1 to 30, wherein the sample is a blood sample.

32. The method according to any one of claims 1 to 30, wherein the sample is a plasma sample.

33. The method according to any one of claims 1 to 32, wherein the subject is a human.

34. The method according to any one of claims 1 to 33, wherein the subject is a human female.

35. The method according to any one of claims 1 to 34, wherein the subject is aged 21 to 45 years.

36. The method according to any one of claims 1 to 34, wherein the subject is 35 years of age or older.

37. The method according to any one of claims 13 to 36, wherein the subject is undergoing assisted reproductive technology.

38. A kit for determining the condition of a subject's endometrium.

39. Obtaining a blood sample from a patient during an in vitro fertilization cycle, wherein the blood sample is collected from the patient on the day of embryo transfer during the in vitro fertilization cycle. Based on a machine learning model trained to determine the state of the endometrium based on miRNA expression profile data derived from the blood sample, the predicted state of the endometrium of the patient at the time of embryo transfer based on the miRNA expression profile from the blood sample is determined. Based on the predicted state of the endometrium, a predicted embryo transfer date is generated, A method comprising performing embryo transfer on the predicted embryo transfer date to carry out a subsequent in vitro fertilization cycle for the patient.

40. The method according to claim 39, wherein the blood sample is collected before embryo transfer.

41. The blood sample is further preserved, Preserving the blood sample allows for subsequent extraction and sequencing of miRNA. The method according to claim 40.

42. If a pregnancy test is performed on the aforementioned patient and the pregnancy test is negative, Obtaining the blood sample from storage, The aforementioned blood sample is sequenced for miRNA to provide a miRNA expression profile. The method according to claim 41, further comprising:

43. The method according to claim 40, further comprising performing a pregnancy test on the patient, and if the pregnancy test is negative, providing a miRNA expression profile by sequencing the blood sample for miRNA.

44. Determining endometrial state data for multiple patients, wherein the endometrial state data is obtained from at least one of the endometrial samples and pregnancy outcomes. The process involves associating the miRNA expression profiles from the aforementioned multiple patients with endometrial state data, such that each patient's individual miRNA expression profile is associated with the endometrial state of each patient. Training a machine learning model based on associated miRNA expression profiles and associated endometrial state data, wherein the trained machine learning model is trained to output a predicted endometrial state based on the input miRNA expression profile. A method that includes this.

45. The method according to claim 44, wherein the state of the endometrium is at least one of pre-receptive, receptive, and post-receptive.

46. The method according to claim 44, wherein the machine learning model is a plurality of machine learning models, and each of the machine learning models generates one or more predictions from the pre-receptive period, the receptive period, and the post-receptive period.

47. The method according to claim 44, wherein the model further associates one or more of the patient's age, body mass index (BMI), pregnancy history, and implantation failure with the patient's endometrial condition, so that input data regarding each patient's age, body mass index (BMI), pregnancy history, and implantation failure may be input to the machine learning model in addition to the miRNA expression profile.

48. The method according to claim 44, wherein the machine learning is trained on a server in the network, and the server is accessible to a patient or clinician for uploading the miRNA expression data.

49. In a machine learning model within a networked server, receiving a digital representation of a patient's miRNA expression profile, wherein the miRNA expression profile is determined by sequencing of the patient's blood sample. The process involves processing the miRNA expression profile in the machine learning model, wherein the machine learning model is trained to output a predicted endometrial state based on the input miRNA expression profile, and the process is such that the patient's predicted endometrial state can be obtained based on the digital representation of the provided miRNA expression profile. Methods that include...

50. The machine learning model is further trained to receive input data about each patient's age, body mass index (BMI), pregnancy history, and implantation failure, and to predict the state of the patient's endometrium based on the received input data. The method described above is The machine learning model receives input data on each patient's age, body mass index (BMI), pregnancy history, and implantation failure, so that it outputs the predicted endometrial state based on the input data in addition to the miRNA expression profile. The method according to claim 49, further comprising:

51. The method according to any one of claims 1 to 37, wherein the trained machine learning model is trained on a plurality of miRNA expression profiles, each miRNA expression profile is determined from a sample of a predetermined patient among a plurality of patients, each miRNA expression profile is associated with a known endometrial state of the predetermined patient, and thereby the trained machine learning model is trained to output the endometrial state of a patient other than the plurality of patients based on a miRNA expression profile determined from a sample of a patient other than the plurality of patients.

52. The method according to any one of claims 1 to 37 or 51, wherein the machine learning model is a classifier.

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