138 results about "Preeclampsia" patented technology

The invention relates to the field of noninvasive prenatal diagnosis, and particularly discloses a preeclampsia noninvasive screening method based on deep sequencing 8bp oligonucleotide double-fragment characteristics, which comprises the following steps: collecting preeclampsia and healthy pregnant woman peripheral blood samples, and extracting free DNA for high-throughput sequencing; the method comprises the following steps: extracting core 8-mer sequences' GTGCGCCC 'and' GATGGGGT 'in a long fragment of 150-200bp through bioinformatics analysis; an integrated support vector machine, K-nearest neighbor, extreme gradient lifting, a random forest and a multi-layer perceptron are combined with a logistic regression element classifier to construct a stacking model, the frequency of a core sequence is normalized, machine learning analysis is carried out, and the preeclampsia risk is predicted. According to the invention, two 8bp oligonucleotide characteristic fragments are specifically screened, and a deep learning architecture of multi-model fusion is combined, so that the limitations of low specificity and invasive detection of a traditional screening method are effectively broken through.

A method for predicting health status of a subject, including: receiving at least one subject-related dynamic property data, receiving at least one subject-related covariate, processing the at least one subject-related dynamic property data and the at least one subject-related covariate data to generate a subject-related processed dataset, generating at least one health status hypothesis based on the subject-related processed dataset, and predicting at least one health status based on the at least one health status hypothesis. Also described is a system that can execute the method for predicting health status of a subject, including: at least one processing component; and at least one analyzing component, wherein the system is configured to predict at least one health status based on the at least one health status hypothesis.

The invention discloses a pre-eclampsia prediction method and system based on machine learning and early pregnancy indexes. The pre-eclampsia prediction method comprises the following steps: acquiring early pregnancy laboratory index data and FMF model evaluation parameters of a to-be-predicted pregnant woman; based on a preset preeclampsia type, performing feature screening on the early pregnancy laboratory index data by using a Boruta algorithm to construct corresponding original features; inputting the original features into a trained first-level machine learning prediction model corresponding to the pre-eclampsia type to obtain a risk score; according to the risk score and an FMF model evaluation parameter, obtaining a joint feature; and inputting the joint features into a trained second-stage machine learning prediction model corresponding to the pre-eclampsia type to obtain a corresponding pre-eclampsia risk prediction result. According to the method, information of different sources and different physiological dimensions is deeply fused, analyzed and judged, and high-precision and low-cost early prediction of different pre-eclampsia types is realized.

The invention provides a construction method and application of a gene Idh1 conditional knockout mouse model and a preeclampsia heart injury animal model, and relates to the technical field of biology. According to the preeclampsia heart injury animal model, flox mice with conditional gene Idh1 knockout and mice with placental trophoblast specific expression Cre recombinase comate, PE-related phenotypes and typical symptoms of heart injury spontaneously appear in the double-positive pregnant mouse model, and the double-positive pregnant mouse model can be used for research of disease causes, diagnosis methods and drug research and development.

A system comprising an implantable medical device and processing circuitry. The implantable medical device comprises one or more sensors configured to continuously sense one or more physiological characteristics of a body of a maternal patient, and sensing circuitry operably connected to the one or more sensors and configured to issue one or more output signals indicative of the one or more physiological characteristics. The processing circuitry is configured to define one or more patient attributes using the one or more output signals; and generate an output based on the one or more patient attributes, the output configured to cause a computing device to provide an indication of preeclampsia of the maternal patient to a user.

The invention belongs to the technical field of biological medicines, and particularly relates to placenta targeting peptide plCSA-BP modified tetrahedral framework nucleic acid as well as a preparation method and application thereof. The placenta targeting peptide plCSA-BP is creatively combined with tFNAs to form a tFNAs / plCSA-BP compound, the compound has excellent placenta targeting property and can effectively improve the function of placenta trophoblasts, so that the treatment effect of preeclampsia is remarkably improved, a new treatment strategy is provided for the placenta diseases in the gestation period, and the placenta targeting peptide plCSA-BP has a good application prospect. The application prospect of the nano material in placental disease treatment is expanded.

The disclosure describes changes in cfRNA gene expression that are associated with risk for preeclampsia. Accordingly, the disclosure provides methods and kits for preeclampsia risk assessment.

The invention provides a computer-implemented method for predictive assessment of a health risk parameter, such as a risk for diabetes, in particular gestational diabetes mellitus, and / or preeclampsia, for a woman based on at least one pregnancy-related parameter, comprising the following steps: providing an assessment of the health risk parameter using a first machine-learning model (1) if the pregnancy-related parameter fulfils a first condition; providing an assessment of the health risk parameter using a second machine-learning model (2) if the pregnancy-related parameter fulfils a second condition; and making available the health risk parameter.

The invention discloses a wearable monitoring bracelet for preeclampsia early warning, and relates to the technical field of wearable monitoring bracelets.The wearable monitoring bracelet comprises a monitoring bracelet body, a touch control circular screen is arranged in the middle of the top face of the monitoring bracelet body, and splicing end blocks are fixedly connected to the two ends of the monitoring bracelet body; the end parts of the two splicing end blocks are both provided with expansion splicing boxes, the interior of the expansion splicing box at the tail end of the monitoring bracelet main body is filled with an installation elastic rubber box, and one side of the interior of the installation elastic rubber box is provided with a gas pressure intensity sensor. Meanwhile, a universal splicing structure is utilized at the splicing part of the expansion splicing box and the splicing end block, so that a sensor in the expansion splicing box can be replaced as required, and the monitoring bracelet can be used for specially and accurately detecting other vital signs while detecting eclampsia symptoms; and the use safety and smoothness of the monitoring bracelet are further improved.

The invention provides a computer-implemented method for predictive assessment of a health risk parameter, such as a risk for diabetes, in particular gestational diabetes mellitus, and / or pre-eclampsia, for a woman based on at least one pregnancy-related parameter, comprising the following steps: providing an assessment of the health risk parameter using a first machine-learning model (1) if the pregnancy-related parameter fulfils a first condition; providing an assessment of the health risk parameter using a second machine-learning model (2) if the pregnancy-related parameter fulfils a second condition; and making available the health risk parameter.

The invention discloses a method and a system for predicting preeclampsia risk of a pregnant woman based on computer implementation. The method integrates the concentration level of vitamin D (especially 25-hydroxyvitamin D) in serum of pregnant women, angiogenesis biomarkers (such as sFlt-1 / PlGF ratio), conventional clinical indexes (such as mean arterial pressure and body mass index) and personal medical history data. A machine learning algorithm (such as a random forest or a gradient lifting model) is adopted to carry out weighted analysis on the multi-dimensional data, and a dynamic and high-precision prediction model is constructed. The system comprises a data acquisition module, an intelligent prediction module and a clinical decision support module, and can provide real-time risk assessment and personalized intervention suggestions (such as a vitamin D supplement scheme) for medical staff, so that early warning and effective management of preeclampsia are realized, and the risk of pregnancy outcome caused by maternal and infant poor pregnancy is reduced.

The invention discloses a urine glycosylation protein marker for early auxiliary diagnosis of preeclampsia and application of the urine glycosylation protein marker. The invention discloses a urine glycosylation protein marker for early auxiliary diagnosis of preeclampsia. The urine glycosylation protein marker is selected from any one or more of glycosylation modified SERPINC1 and SERPING1. The invention also discloses application of the reagent for detecting the urine glycosylated protein marker in preparation of a kit for early auxiliary diagnosis of preeclampsia. According to the present invention, the high-throughput screening method is adopted to analyze the glycosylated proteins in the urine of the normal pregnancy pregnant woman and the preeclampsia pregnant woman, such that the glycosylated proteins capable of indicating the preeclampsia disease are screened, and the research basis is provided for the development and the clinical application of the glycosylated protein marker in the urine; besides, in the research method, urine is relatively simple and non-invasive to collect and is easier to obtain, so that the urine is easier to accept by a patient, continuous dynamic monitoring can be performed, changes of markers can be tracked, clinical transformation is easy, and the method is suitable for popularization and application.

The invention provides a computer-implemented method for predicting and assessing a health risk parameter, such as the risk of diabetes, in particular gestational diabetes and / or preeclampsia, of a woman based on at least one pregnancy-related parameter, the method comprising the steps of: if the pregnancy-related parameter satisfies a first condition; if so, providing an assessment of the health risk parameter using a first machine learning model (1); providing an assessment of the health risk parameter using a second machine learning model (2) if the pregnancy-related parameter satisfies a second condition; and the health risk parameters are made available.

Provided are diagnostic and / or screening tests, assays and devices for preeclampsia (PE). The diagnostic and / or screening tests, assays and devices include test antibodies for activin A and / or inhibin A. The PE diagnostic and / or screening tests, assays and devices are capable of detecting activin A and / or inhibin A in a sample at a concentration of less than about 60 pg / mL. Corresponding methods of diagnosing and / or screening for PE are provided.

The invention provides an application of a pair of immune costimulatory factors TNFSF9 / TNFRSF9 as markers in preparation of a product for early prediction of preeclampsia, and the immune costimulatory factors comprise a tumor necrosis factor superfamily member 9 (TNFSF9) and a tumor necrosis factor receptor superfamily member 9 (TNFRSF9). The product is used for carrying out early warning on the preeclampsia occurrence risk 20 weeks before pregnancy. The product is used for detecting the immune costimulatory factor through at least one of serum, plasma, whole blood and placental tissue. The invention aims to improve the early prediction efficiency of preeclampsia by detecting the abnormal expression level of the pair of markers.

Provided are methods and compositions for using tissue kallikrein-1 (KLK1) to treat or prevent pregnancy disorders such as fetal growth restriction (FGR) and preeclampsia.

The invention discloses a machine learning-based pregnant woman preeclampsia subtype classification probability prediction method and system. According to the method, the one-carbon metabolic cycle core metabolite is combined with multi-source clinical indexes for the first time, and cross-modal deep feature fusion is realized by constructing derivative composite features with clear biological significance; in the model level, a double-layer self-adaptive model integration strategy is adopted, firstly, multiple base classifiers are subjected to self-adaptive weighted fusion based on cross validation performance, then further optimization is carried out through stacking generalization, and a final prediction model with high precision and high robustness is formed; besides, the system integrates SHAP interpretability analysis and risk positioning visualization based on historical distribution, not only outputs the classification probability, but also provides visual clinical decision-making assistance. According to the method, the problems of insufficient subtype identification ability, weak model generalization ability and poor result interpretability in the prior art are effectively solved, and reliable technical support is provided for health management and clinical decision-making in the gestation period.

The invention discloses a preeclampsia biomarker and an application of the preeclampsia biomarker. The preeclampsia biomarker comprises at least one of a fusion gene PSPC1-MRPS31P2 and a fusion gene LINC00630-AL035494. The preeclampsia biomarker can be used for detecting preeclampsia. The preeclampsia biomarker provided by the invention can provide a non-invasive diagnosis scheme for clinical prenatal preeclampsia, and preeclampsia specific high-expression fusion genes (PSPC1-MRPS31P2 and LINC00630-AL035494) in peripheral blood of a pregnant woman are detected through real-time quantitative PCR (Polymerase Chain Reaction), so that preeclampsia early prediction, disease diagnosis and monitoring of the state of a patient in the illness period are realized; and in combination with the existing clinical diagnosis scheme, the accuracy of pre-eclampsia diagnosis is improved.

The invention belongs to the technical field of biology, and particularly relates to application of a reagent for inhibiting expression of circular RNA hsacirc0001360 in preparation of a medicine for treating preeclampsia, the medicine inhibits inflammation, by conducting CircRNAs sequencing and subsequent verification on placenta tissue, hsacirc0001360 is determined as a research object, the relation between hsacirc0001360 and PE is disclosed, and the action mechanism of ncRNAs in PE is perfected.

The application provides use of zanamivir in preparation of a medicine for treating or preventing preeclampsia, and a corresponding medicine and treatment method; zanamivir can effectively improve high blood pressure, proteinuria, creatinine, neuraminidase and sialic acid elevation symptoms of preeclampsia, increase NO level, and improve pregnancy outcome. Zanamivir and hydroxyprogesterone caproate in combination show better effects.

The invention is suitable for the technical field of biology, and provides an L-NAME induced trophoblast organ injury model and a construction method and application thereof, and the construction method of the trophoblast organ injury model comprises the following steps: constructing a trophoblast organ by using human villus tissue; the method comprises the following steps: carrying out culture and passage treatment on a trophoblast organ, and then adding L-NAME with the final concentration of 15-60mM to carry out induced injury, so as to obtain the trophoblast organ injury model. The basic model used in the invention is a trophoblast-like organ which can be subcultured for a long time, so that the problems of difficult acquisition of placenta samples and ethics limitation in the clinical early stage are reduced. Meanwhile, L-NAME is used for performing drug-induced injury, a trophoblast organ injury model for simulating the placental pathological state of preeclampsia can be constructed, and compared with traditional hypoxia-induced injury, the method is more convenient, and the limitation of instruments and equipment is reduced.

The invention discloses application of C1INH in preparation of a product for prediction or auxiliary diagnosis of preeclampsia and the product, belongs to the technical field of biological medicines, and aims to detect the content of C1INH in a blood sample on a protein level by taking the blood sample of a subject as a detection object. According to the invention, C1INH molecules in blood are taken as a detection index, prediction and auxiliary diagnosis of preeclampsia of pregnant women can be realized, high sensitivity and specificity are realized, rapid detection can be realized, and a new way is provided for early screening and diagnosis of preeclampsia in the prior art.

This invention discloses a method, prediction method, and prediction device for constructing a preeclampsia prediction model based on temporal multimodal data, relating to the field of preeclampsia risk prediction technology. The construction method includes: acquiring multimodal temporal feature data of pregnant women with known pregnancy outcomes to construct a time-series dataset; the pregnant women with known pregnancy outcomes include those with and without preeclampsia; training a graph attention network and a fully connected network using the time-series dataset; embedding the resulting missing value fitting model as an embedding layer into a gated recurrent unit; and obtaining the preeclampsia prediction model after training with the time-series dataset. In this invention, the prediction model processes missing data through a missing value fitting model, effectively recovering missing information and ensuring the model can more accurately handle incomplete medical data. Simultaneously, the combination of a gated recurrent unit for capturing and predicting temporal features effectively improves the prediction accuracy of preeclampsia.

The invention relates to a computer-implemented method for classification of an occurrence of a complication caused by preeclampsia within a predetermined time period, comprising applying (S3) a second machine learning algorithm (A2) to the second dataset (DS2) of medical parameters of the patient to classify the occurrence of the complication caused by preeclampsia within the predetermined time period, and outputting (S4) a class representing the occurrence of the complication caused by preeclampsia within the predetermined time period. The invention further relates to a system (1) for classification of an occurrence of a complication caused by preeclampsia within a predetermined time period.

The invention belongs to the technical field of molecular diagnosis, and particularly relates to application of a biomarker in prediction or auxiliary diagnosis of preeclampsia. The invention provides an application of a reagent for detecting expression levels of Caspase3, BAX and IL-8 proteins in preparation of a product for predicting or assisting in diagnosing preeclampsia. Through micro-plasma Olink proteomics and clinical test verification, it is found that the risk of preeclampsia of a pregnant woman can be noninvasively, accurately and effectively predicted by detecting the protein expression levels of Caspase3, BAX and IL-8 in plasma; by using the kit provided by the invention, the occurrence of preeclampsia can be predicted or diagnosed in an auxiliary manner before clinical symptoms appear, so that the early discovery rate of diseases is greatly improved, an earlier intervention and treatment opportunity is provided for doctors, and the clinical application prospect is good.

The present invention relates to a disease-specific biomarker for early prediction and diagnosis of preeclampsia, and use thereof. A composition for predicting or diagnosing preeclampsia or a method for providing information for prediction or diagnosis of preeclampsia, according to one aspect, can simply and effectively predict or diagnose a disease by measuring and comparing the level of mRNA of a disease-specific protein that changes in a patient, or an mRNA of a gene encoding same.

The present application relates to the technical field of medical data fusion processing, and discloses a method and system for predicting adverse pregnancy outcomes of preeclampsia by fusing placental microenvironment image features and serum biomarkers, wherein the method comprises: obtaining digital whole field image of placental tissue section and serum biomarker detection data at the same period; constructing a multi-modal sample data set; extracting multi-scale structure features of placental microenvironment; constructing serum biomarker discriminant features; dynamically fusing placental image features and serum marker features; constructing a prediction model for adverse pregnancy outcomes of preeclampsia and outputting a prediction probability. Compared with the prior art which only relies on single image information or single serum biomarker for risk assessment, it is difficult to realize stable prediction of the coordinated changes of placental microenvironment abnormalities and biochemical abnormalities. Due to the multi-scale image structure analysis, the model generalization ability for predicting adverse pregnancy outcomes of preeclampsia is improved.

The present invention includes methods and computer programs for use in the detection preeclampsia and / or determining an increased risk for preeclampsia in a pregnant female, the methods including identifying in a biosample obtained from the pregnant female cell-free DNA signals, including concentration, fetal fraction, and fragment size distribution. These methods provide for the identification of patients at risk of preeclampsia in the first trimester of pregnancy.

Provided are methods and compositions for using tissue kallikrein-1 (KLK1) to treat or prevent pregnancy disorders such as fetal growth restriction (FGR) and preeclampsia.

Provided is a method for diagnosing or predicting preeclampsia in a pregnant woman or determining if a pregnant woman has a risk of preeclampsia, comprising: a) obtaining at least one (such as two or more) maternal or fetal parameter or factor of the pregnant woman, selected from a group comprising: (i) one or more of mean artery pressure (MAP), BMIsampling, ΔBMI, Gestational age and ΔBMI / GA; (ii) Maternal age; (iii) one or more of Urinary protein level and the ratio of Urinary protein level (such as Urine Total Protein (UTP) ) / Urinary Creatinine level (Cre) (UTP / Cre); (iv) one or more of preeclampsia in previous pregnancy (PEhis) and having received antihypertensive medication (Antihyper); and (v) any combination of (i) - (iv), b) determining if the urine of said pregnant woman contains misfolded proteins or misfolded protein aggregates, and c) combining the at least one maternal or fetal parameter or factor obtained in step a) and the content level of misfolded proteins or misfolded protein aggregates in the urine of said pregnant woman to diagnose or predict preeclampsia or determine the risk of preeclampsia.