A sample fractionation method, apparatus and storage medium for improving uniformity of host removal

Abstract

The application discloses a sample grading method, device and storage medium for improving host removal efficiency uniformity. It is found that different sample types and states have corresponding sample host removal conditions, and appropriate host removal reaction formula and reaction conditions can ensure high host removal efficiency and improve the uniformity of the final host removal effect of different samples. Based on this, the application provides a sample grading method, system, device, medium or program product for improving host removal efficiency uniformity and a host removal method, system, device, medium or program product for improving host removal efficiency uniformity through sample grading, thereby assisting detection workers in optimizing host removal conditions according to sample levels, effectively improving the uniformity of sample host removal effects, and avoiding the missed detection or damage of microorganisms in human samples with high or low levels.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics, specifically relating to a sample grading method, system, device, medium or program product for improving the uniformity of host removal efficiency, and a host removal method, system, device, medium or program product for improving the uniformity of host removal efficiency through sample grading. Background Technology

[0002] A significant challenge in detecting pathogens in clinical samples using metagenomic next-generation sequencing (mNGS) is the often high human content in these samples. This human-derived signal suppresses or completely eliminates the microbial signal in the sequencing data, severely impacting the reliability and sensitivity of the results. Therefore, with the widespread application of mNGS technology, various host DNA removal methods have emerged. Based on different host removal principles, these methods mainly include filtration / differential centrifugation, differential lysis, and nucleic acid enrichment. Among these, differential lysis is currently the most widely used host removal method, and several commercially available kits are available.

[0003] The host removal efficiency of differential lysis is influenced by various factors, including lysis buffer formulation, sample type, state, and microbial composition. Under fixed lysis buffer formulation and experimental procedures, host removal efficiency is primarily affected by sample type and the human content in the sample. Samples with high human content exhibit relatively low host removal efficiency, leading to poor detection sensitivity of pathogenic microorganisms and even the risk of missed detection of low-abundance microorganisms. Therefore, to obtain more stable host removal efficiency, samples need to be graded according to sample type and human content, with each grade corresponding to specific sample pretreatment and host removal conditions. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, the present invention aims to provide a sample grading method, system, device, medium or program product for improving the uniformity of host removal efficiency, and a host removal method, system, device, medium or program product for improving the uniformity of host removal efficiency through sample grading.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A first aspect of the present invention provides a sample grading method for improving the uniformity of host removal efficiency, the method being performed by a computer, the method comprising the following steps: Data Acquisition: Acquire the state data of the sample to be tested. The state of the sample to be tested includes: the sample is clear and similar to water; the sample is slightly turbid and semi-transparent; the sample is highly turbid or milky white; the sample is slightly viscous and contains a small amount of insoluble matter; the sample is highly viscous.

[0006] Data processing: The state data of the sample to be tested is input into the constructed grading model, and the grading model performs sample grading based on the state data of the sample to be tested.

[0007] Output results.

[0008] Furthermore, the sample to be tested is a human-derived sample.

[0009] Furthermore, the construction steps of the hierarchical model are as follows: Acquire the state data of the sample to be tested, the state of the sample to be tested includes: the sample is clear, similar to water; the sample is slightly turbid and semi-transparent; the sample is highly turbid or milky white; the sample is slightly viscous with a small amount of insoluble matter; the sample is highly viscous; input the state data of the sample to be tested into a machine learning algorithm to construct a hierarchical model.

[0010] Furthermore, the state of the sample to be tested may also include: sample pH, color, transparency, turbidity, viscosity, clot formation, pH, hemolysis index, protein concentration, cell number, and activity.

[0011] Furthermore, the samples include blood samples, bile or bile drainage fluid samples, tissue samples, and swab samples.

[0012] Furthermore, the sample exhibited hemolysis.

[0013] Furthermore, the hierarchical model obtains results using the following criteria: When the sample is clear and similar to water, it is classified as a Level 1 sample; when it is slightly turbid and semi-transparent, it is classified as a Level 2 sample; when it is highly turbid or milky white, it is classified as a Level 3 sample; when it is slightly viscous with a small amount of insoluble matter, it is classified as a Level 4 sample; and when it is highly viscous, it is classified as a Level 5 sample.

[0014] Furthermore, the grading model can expand the number of grades based on sample pH, sample color, transparency, turbidity, viscosity, clot formation, pH, hemolysis index, protein concentration, cell number, and activity data.

[0015] A second aspect of the present invention provides a host removal method for improving host removal efficiency and uniformity through sample grading, the method being performed by a computer, and the method comprising the following steps: Data Acquisition: Acquire the graded data of the samples to be tested, which includes: Level 1 samples, Level 2 samples, Level 3 samples, Level 4 samples, and Level 5 samples.

[0016] Data processing: The graded data of the sample to be tested is input into the constructed selection model, which selects host removal conditions based on the graded data of the sample to be tested.

[0017] Output results.

[0018] Furthermore, the sample to be tested is a human-derived sample.

[0019] Furthermore, the construction steps of the selection model are as follows: Obtain the graded data of the test samples, which includes: Level 1 samples, Level 2 samples, Level 3 samples, Level 4 samples, and Level 5 samples; input the graded data of the test samples into a machine learning algorithm to construct a selection model.

[0020] Furthermore, the selection model obtains results using the following criteria: When the sample to be tested is a Level 1 sample, the corresponding host removal conditions are: 1000 µl of original sample, 6 µl of lysis buffer, and 10 µl of nuclease; when the sample to be tested is a Level 2 sample, the corresponding host removal conditions are: 1000 µl of original sample, 20 µl of lysis buffer, and 10 µl of nuclease; when the sample to be tested is a Level 3 sample, the corresponding host removal conditions are: 200 µl of original sample, 25 µl of lysis buffer, and 15 µl of nuclease; when the sample to be tested is a Level 4 sample, the corresponding host removal conditions are: 250 µl of original sample, liquefaction with 0.5 times the volume of 4% NaOH, 5 µl of lysis buffer, and 12 µl of nuclease; when the sample to be tested is a Level 5 sample, the corresponding host removal conditions are: 150 µl of original sample, liquefaction with 2-3 times the volume of 4% NaOH, and 5 µl of lysis buffer. µl, nuclease dosage 12 µl.

[0021] In some implementations, the test samples include 20 or more samples, such as 30, 50, 80, 100, 150, 200, 300, 500 or more.

[0022] Furthermore, the machine learning algorithm includes algorithmic models developed using various development tools.

[0023] Furthermore, the development tools include, but are not limited to, TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell.

[0024] Furthermore, the algorithm models include, but are not limited to, linear regression models, logistic regression models, Lasso regression models, Ridge regression models, linear discriminant analysis models, nearest neighbor models, decision tree models, perceptron models, neural network models, support vector machine models, Naive Bayes models, AdaBoost models, GBDT models, XGBoost models, LightGBM models, CatBoost models, and random forest models.

[0025] A third aspect of the present invention provides a sample grading system for improving the uniformity of host removal efficiency, the system comprising: Data acquisition unit: used to acquire the state data of the sample to be tested, the state of the sample to be tested includes: the sample is clear, similar to water; the sample is slightly turbid and semi-transparent; the sample is highly turbid or milky white; the sample is slightly viscous with a small amount of insoluble matter; the sample is highly viscous.

[0026] Data grading unit: used to perform grading monitoring on the data obtained by the data acquisition unit through the grading model obtained by the construction steps provided in the first aspect of the present invention, so as to obtain the specific grading result of the sample to be tested.

[0027] Result output unit: Used to output the hierarchical results.

[0028] Furthermore, the sample to be tested is a human-derived sample.

[0029] A fourth aspect of the present invention provides a host removal system that improves the uniformity of host removal efficiency through sample grading, the system comprising: Data acquisition unit: used to acquire the graded data of the sample to be tested, wherein the graded data of the sample to be tested includes: first-level sample, second-level sample, third-level sample, fourth-level sample, and fifth-level sample.

[0030] Data classification unit: used to select host removal conditions for the data obtained by the data acquisition unit through the selection model obtained by the construction steps provided in the second aspect of the present invention, so as to obtain the specific host removal conditions for the sample to be tested.

[0031] Result output unit: Used to output the selection result.

[0032] Furthermore, the sample to be tested is a human-derived sample.

[0033] The fifth aspect of the present invention provides a computer device, a computer-readable storage medium, or a computer program product, including a computer program. The device includes a memory and a processor. The memory is used to store program instructions. The processor is used to invoke the program instructions, which, when executed, implement the steps of the sample grading method for improving host removal efficiency uniformity provided in the first aspect of the present invention or the host removal method for improving host removal efficiency uniformity through sample grading provided in the second aspect of the present invention.

[0034] Furthermore, the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the sample classification method for improving host removal efficiency uniformity provided in the first aspect of the present invention or the host removal method for improving host removal efficiency uniformity through sample classification provided in the second aspect of the present invention.

[0035] Furthermore, when the computer program is executed by the processor, it implements the steps of the sample classification method for improving the uniformity of host removal efficiency provided in the first aspect of the present invention or the host removal method for improving the uniformity of host removal efficiency through sample classification provided in the second aspect of the present invention.

[0036] Advantages and beneficial effects of the present invention: This invention proposes a sample grading method, system, device, medium, or program product to improve the uniformity of host removal efficiency. The method is simple and effective, capable of distinguishing between low, medium, and high human-origin samples, and allows for optimization of experimental conditions for different samples. It provides a host removal method, system, device, medium, or program product that improves the uniformity of host removal efficiency through sample grading. The sample grading method proposed in this invention, combined with optimized host removal conditions, improves the uniformity of host removal efficiency in samples with different human-origin content, avoiding missed detection of microorganisms in high- or low-human-origin samples. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the sample grading method and host removal process of the present invention.

[0038] Figure 2 This is a schematic diagram of the sample grading method for improving host removal efficiency uniformity provided by the present invention.

[0039] Figure 3 This is a schematic diagram of the host removal method for improving host removal efficiency and uniformity through sample grading, as provided by the present invention.

[0040] Figure 4 This is a schematic diagram of the sample grading system structure provided by the present invention to improve the uniformity of host removal efficiency.

[0041] Figure 5 This is a schematic diagram of the host removal system structure provided by the present invention, which improves the uniformity of host removal efficiency through sample grading.

[0042] Figure 6 A schematic diagram of the structure of the computer device provided by the present invention.

[0043] Figure 7 The results of human qRT-PCR detection of nucleic acids obtained from different human samples under different host removal conditions are shown; where A represents high-human samples and B represents low-human samples.

[0044] Figure 8 The effectiveness of the sample grading method in distinguishing human samples.

[0045] Figure 9 The results of human qRT-PCR detection of nucleic acids obtained from human samples of each grade under their respective optimal host removal conditions. Detailed Implementation

[0046] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0047] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Figure 1 This is a schematic diagram of the sample grading method and host removal process of the present invention.

[0050] Figure 2This is a schematic flowchart of the sample grading method for improving host removal efficiency uniformity provided by the present invention. Specifically, the method includes: 201: Data Acquisition: Acquire the state data of the sample to be tested, wherein the state of the sample to be tested includes: the sample is clear and similar to water; the sample is slightly turbid and semi-transparent; the sample is highly turbid or milky white; the sample is slightly viscous and contains a small amount of insoluble matter; the sample is highly viscous.

[0051] In the context of this invention, the term "sample" as used refers to a composition obtained from or derived from a patient / subject that contains cells and / or other molecular entities to be characterized and / or identified based on, for example, physical, biochemical, chemical, and / or physiological characteristics. For example, a sample refers to any sample derived from a patient / subject that is expected or known to contain cells and / or molecular entities to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell cultures, cell supernatants, cell lysates, platelets, serum, plasma, vitreous fluid, lymph, synovial fluid, follicular fluid, semen, pancreatic juice, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tissue culture fluid, tissue extracts, homogenized tissue, cell extracts, and combinations thereof.

[0052] In some embodiments, the patient / subject may be human or non-human and may include, for example, animal strains or species used as a "model system" for research purposes. Similarly, the patient / subject may include adults or adolescents (e.g., children). Furthermore, the patient / subject may refer to any living organism, preferably a mammal (e.g., human or non-human). Examples of mammals include, but are not limited to, any member of the mammalian class: humans, non-human primates (e.g., chimpanzees) and other apes and monkeys; livestock, such as cattle, horses, sheep, goats, pigs; domestic animals, such as rabbits, dogs, and cats; laboratory animals include rodents, such as rats, mice, and guinea pigs. Examples of non-mammals include, but are not limited to, birds, fish, etc.

[0053] In a specific embodiment of the present invention, the samples include human 293T cells, blood samples, body fluids (including cerebrospinal fluid, tissue fluid, ocular fluid, amniotic fluid, bile, pleural / peritoneal effusion, drainage fluid, etc.), and lavage fluid clinical samples. Human 293T cells were purchased from Beina Biotechnology and cultured in DMEM-H complete medium. The clinical samples were retained samples after clinical testing, totaling 100 cases.

[0054] In some embodiments of the present invention, experimental verification has shown that the appropriate host removal methods are not the same for test samples with different human origin content. Using the same host removal conditions can lead to missed detection or damage to microorganisms. This suggests that samples can be classified into different grades according to their human origin content to facilitate the optimization of corresponding host removal conditions.

[0055] In one embodiment of the present invention, we used sample standards with different human content to analyze the effect of the same host removal method on the microbial detection efficiency of samples with different human content.

[0056] The specific experimental method is as follows: 1. Experimental materials: Nucleic acid extraction kit, Novizan; Library preparation kit, Novizan; Self-developed differential lysis buffer; nuclease; Nuclease buffer; Nuclease-free water, Thermo Fisher Scientific; Microbial standards, self-prepared, preparation method as shown in Table 1: Table 1. Microbial Standard Formulations

[0057] 2. Preparation of standard products High-human-derived standard: Add 400 µl of a 1×10⁻⁶ concentration to a 15 ml centrifuge tube. 7 Human 293T cells / ml, 400 µl of 10× microbial standard, and 3200 µl of PBS were mixed by pipetting with a 1 ml pipette to obtain a human-derived content of 1×10⁻⁶ cells / ml. 6 Standards per cells / ml; Human-derived standard: Add 400 µl of a 1×10⁻⁶ concentration to a 15 ml centrifuge tube. 6 Human 293T cells / ml, 400 µl of 10× microbial standard, and 3200 µl of PBS were mixed by pipetting with a 1 ml pipette to obtain a human-derived content of 1×10⁻⁶ cells / ml. 5 Standards per cells / ml; Low-human-derived standard: Add 400 µl of a 1×10⁻⁶ concentration to a 15 ml centrifuge tube. 5 Human 293T cells / ml, 400 µl of 10× microbial standard, and 3200 µl of PBS were mixed by pipetting with a 1 ml pipette to obtain a human-derived content of 1×10⁻⁶ cells / ml. 4 Standards per cells / ml; 3. Host removal and nucleic acid extraction Take 1 ml of high-, medium-, and low-human samples respectively into three 1.5 ml centrifuge tubes. After centrifugation, discard a portion of the supernatant. Add 6 µl of differential lysis buffer, 2 µl of nuclease, and nuclease buffer to the remaining solution. Mix well using a 1 ml pipette. Place the centrifuge tubes in a metal bath and incubate at 1000 rpm and 37ºC for 5–15 min. After incubation, transfer the centrifuge tubes to a 55ºC metal bath and incubate for 5–10 min. Extract nucleic acids from the samples after host removal.

[0058] 4. Use the extracted nucleic acids to construct libraries and perform sequencing.

[0059] Experimental results: Table 2 shows the RPM (Reads Per Million) of microbial detection in samples with different human origin content. High-human origin samples had the lowest RPM, followed by medium-human origin samples, while low-human origin samples had the highest RPM. This result indicates that host removal conditions suitable for low-human origin samples are not optimal for high- and medium-human origin samples, leading to limited microbial detection.

[0060] Table 2. Microbial RPM Detection in High, Medium, and Low Human Samples

[0061] In another embodiment of the invention, we use two types of high-human-source samples to explore their optimal host removal conditions, and use the host removal conditions applicable to high-human-source samples to verify whether they are applicable to low-human-source samples.

[0062] The specific experimental method is as follows: 1. High-human-derived samples: white blood cell count was 6 × 10⁻⁶. 6 cells / ml, 8×10 6 Two blood samples with cells / ml; Low-human-sourced samples: as mentioned above, low-human-sourced standard products.

[0063] 2. Take three 1 ml portions from each of the two blood samples and place them into three 1.5 ml centrifuge tubes. After centrifugation, discard a portion of the supernatant. Add the following solutions to the remaining solution in each sample: Condition 1: 50 µl differential lysis buffer, 2 µl nuclease, and nuclease buffer; Condition 2: 60 µl differential lysis buffer, 2 µl nuclease, and nuclease buffer; Condition 3: 70 µl differential lysis buffer, 2 µl nuclease, and nuclease buffer. Mix thoroughly using a 1 ml pipette. Place the centrifuge tubes in a metal bath and incubate at 1000 rpm and 37°C for 5–15 min. After incubation, transfer the centrifuge tubes to a 55°C metal bath and incubate for 5–10 min.

[0064] 3. Nucleic acid extraction was performed on the samples after host removal was completed. qRT-PCR was used to detect host removal under three different host removal conditions. The optimal host removal conditions were determined based on the results of nucleic acid library construction, sequencing, and detection of RPM in the samples.

[0065] The qRT-PCR primer sequences used are as follows: Forward primer: AGATTTGGACCTGCGAGCG (SEQ ID NO: 1); Reverse primer: GAGCGGCTGTCTCCACAAGT (SEQ ID NO: 2).

[0066] 4. Apply the optimal host removal conditions for high-human samples determined in the above steps and the host removal conditions applicable to low-human samples in the previous embodiment to low-human samples respectively. qRT-PCR is used to detect the host removal under the two host removal conditions. By detecting RPM in the samples through nucleic acid library construction and sequencing, it is determined whether the optimal host removal conditions for high-human samples are applicable to low-human samples based on the results.

[0067] Experimental results: The human removal efficiency of two highly human blood samples under three host de-hosting conditions is as follows: Figure 7 As shown in Figure A, condition 1 has the highest host removal efficiency. The microbial detection RPM is shown in Table 3. The results show that condition 1 has the highest microbial detection RPM value for host removal, therefore condition 1 is determined to be the optimal host removal condition for blood samples.

[0068] Table 3. RPM detection in two blood samples under three host de-hosting conditions

[0069] For low-human samples, the human removal efficiency under two host removal conditions (50 µl differential lysis buffer; 6 µl differential lysis buffer) is as follows: Figure 7As shown in B, the host removal efficiency using 6 µl of differential lysis buffer was higher than that using 50 µl of differential lysis buffer (a difference of 6 Ct values). The RPM for microbial detection is shown in Table 4. The results indicate that the RPM for microbial detection using 6 µl of differential lysis buffer was higher than that using 50 µl of differential lysis buffer. Therefore, for low-human samples, 6 µl of differential lysis buffer is a more suitable host removal condition, while the optimal host removal condition for high-human samples is not applicable to low-human samples.

[0070] Table 4. RPM detection in low-human samples under two host-de-host conditions

[0071] The above results indicate that highly human and low-human samples have their own optimal host removal conditions. If the same host removal conditions are used for both types of samples, it will affect the host removal efficiency and microbial detection sensitivity of one of the samples.

[0072] Based on observations of clinical sample states, liquid clinical samples (excluding blood) can be broadly categorized into five levels: clear, slightly turbid, highly turbid, slightly viscous, and highly viscous. To test whether this grading method can distinguish samples with different human origin content, qRT-PCR quantification was performed on samples at each of the five levels. The results showed that the human origin content in highly turbid samples was essentially the same as that in highly viscous samples, with the highest human origin content. The human origin content in other samples, from highest to lowest, was: slightly turbid, slightly viscous, and clear. This indicates that samples with different human origin contents can be distinguished simply by observing the sample state without a complex quantification process. Although the human origin content in highly turbid and highly viscous samples was essentially the same, highly viscous samples require liquefaction before host removal. The liquefaction process causes some damage to human cells and microorganisms; therefore, the host removal conditions are different from those for highly turbid samples, necessitating the classification of these two types of samples into two different levels. Figure 8 ).

[0073] 202: Data Processing: Input the state data of the sample to be tested into the constructed grading model, and the grading model performs sample grading based on the state data of the sample to be tested.

[0074] 203: Output result.

[0075] Figure 3 This is a schematic flowchart of the host removal method for improving host removal efficiency and uniformity through sample grading provided by the present invention. Specifically, the method includes: 301: Data Acquisition: Acquire the graded data of the sample to be tested and input it into the constructed selection model. The selection model selects host removal conditions based on the graded data of the sample to be tested.

[0076] In some embodiments of the present invention, it has been found that using corresponding host removal conditions according to different sample grades can effectively improve the uniformity of host removal efficiency. This suggests that corresponding host removal conditions can be obtained according to sample grades to improve the uniformity of host removal efficiency.

[0077] In one embodiment of the present invention, we optimize the host removal conditions using the sample grading method described in the present invention.

[0078] The specific experimental method is as follows: Clinical samples of bodily fluids and lavage fluids were collected and graded according to their condition: Grade 1 samples were clear; Grade 2 samples were slightly turbid; Grade 3 samples were highly turbid; Grade 4 samples were slightly viscous; and Grade 5 samples were highly viscous.

[0079] For primary samples, host removal and microbial detection were tested under the following conditions: sample input volume of 1000 µl to 2000 µl, lysis buffer volume of 6 µl to 20 µl, and nuclease volume of 6 µl to 10 µl. The optimal host removal conditions were determined based on the results.

[0080] For secondary samples, host removal and microbial detection were tested under the following conditions: sample input volume of 500 µl to 1000 µl, lysis buffer volume of 15 µl to 20 µl, and nuclease volume of 6 µl to 10 µl. The optimal host removal conditions were determined based on the results.

[0081] For three levels of samples, host removal and microbial detection were tested under the conditions of sample input volume of 200 µl to 1000 µl, lysis buffer volume of 20 µl to 25 µl, and nuclease volume of 10 µl to 15 µl, respectively. The optimal host removal conditions were determined based on the results.

[0082] For Level 4 samples, host removal and microbial detection were tested under the following conditions: sample liquefaction (0.5–2 times the sample volume of 4% NaOH), sample input volume of 500 µl–1000 µl, lysis buffer volume of 5 µl–10 µl, and nuclease volume of 10 µl–15 µl. The optimal host removal conditions were determined based on the results.

[0083] For Level 5 samples, host removal and microbial detection were tested under the following conditions: liquefaction (1-3 times the sample volume of 4% NaOH), liquefaction sample input of 300 µl-1000 µl, lysis buffer volume of 5 µl-10 µl, and nuclease volume of 10 µl-15 µl. The optimal host removal conditions were determined based on the results.

[0084] Host removal was performed on samples from level 1 to level 5 using the optimal host removal conditions for each level of samples, and nucleic acid was extracted from the samples after host removal was completed.

[0085] Host removal was detected in five levels of samples using human qRT-PCR.

[0086] Experimental results: Optimal host removal conditions for each level of sample: The host removal effect was best when the sample input was 1000 µl, the lysis buffer was 6 µl, and the nuclease was 10 µl.

[0087] The optimal host removal efficiency was achieved for secondary samples with a sample input of 1000 µl, a lysis buffer volume of 20 µl, and a nuclease volume of 10 µl.

[0088] The optimal host removal efficiency for Level 3 samples was achieved with a sample input of 200 µl, a lysis buffer volume of 25 µl, and a nuclease volume of 15 µl.

[0089] The optimal host removal efficiency for Level IV samples was achieved with a sample input of 250 µl, liquefaction with 0.5 times the volume of 4% NaOH, a lysis buffer volume of 5 µl, and a nuclease volume of 12 µl.

[0090] The optimal host removal efficiency for Level 5 samples was achieved when the sample input was 150 µl, liquefied with 2–3 times the volume of 4% NaOH, the lysis buffer was 5 µl, and the nuclease was 12 µl.

[0091] The results showed that when host removal was performed on samples at each of the five levels using the optimal host removal conditions, the host removal effect was basically the same (the difference in Ct values ​​between samples was <2). Figure 9 This result indicates that using the most suitable host removal conditions for each level of sample can improve the uniformity of host removal efficiency among samples, effectively avoiding the problems of low host removal efficiency and low microbial detection sensitivity in some samples under a single host removal condition.

[0092] 302: Data Processing: Input the graded data of the sample to be tested into the constructed selection model, and the selection model selects host removal conditions based on the graded data of the sample to be tested.

[0093] 303: Output result.

[0094] In some embodiments of the present invention, the methods for constructing the hierarchical model and the selection model are known to those skilled in the art, and can be implemented and realized in different ways to associate sample state data and sample hierarchical data with a certain probability or risk.

[0095] In the context of this invention, the term "machine learning" refers to the use of computers to simulate or implement human learning activities, and technicians typically use various development tools to build algorithmic models for machine learning.

[0096] The development tools include, but are not limited to, TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell.

[0097] The algorithm models include, but are not limited to, linear regression models, logistic regression models, Lasso regression models, Ridge regression models, linear discriminant analysis models, nearest neighbor models, decision tree models, perceptron models, neural network models, support vector machine models, Naive Bayes models, AdaBoost models, GBDT models, XGBoost models, LightGBM models, CatBoost models, and random forest models.

[0098] Figure 4 This is a schematic diagram of the sample grading system structure provided by the present invention to improve the uniformity of host removal efficiency.

[0099] The system is programmed or otherwise configured to include a data acquisition unit 401, a data classification unit 402, and a result output unit 403. Data acquisition unit: used to acquire the state data of the sample to be tested, the state of the sample to be tested includes: the sample is clear, similar to water; the sample is slightly turbid and semi-transparent; the sample is highly turbid or milky white; the sample is slightly viscous with a small amount of insoluble matter; the sample is highly viscous.

[0100] Data grading unit: used to perform grading monitoring on the data obtained by the data acquisition unit through the grading model obtained by the construction steps provided in the first aspect of the present invention, so as to obtain the specific grading result of the sample to be tested.

[0101] Result output unit: Used to output the hierarchical results.

[0102] Figure 5 This is a schematic diagram of the host removal system structure provided by the present invention, which improves the uniformity of host removal efficiency through sample grading.

[0103] The system is programmed or otherwise configured to include a data acquisition unit 501, a data classification unit 502, and a result output unit 503. Data acquisition unit: used to acquire the graded data of the sample to be tested, wherein the graded data of the sample to be tested includes: first-level sample, second-level sample, third-level sample, fourth-level sample, and fifth-level sample; Data classification unit: used to select host removal conditions for the data obtained by the data acquisition unit through the selection model obtained by the construction steps provided in the second aspect of the present invention, so as to obtain the specific host removal conditions for the sample to be tested; Result output unit: Used to output the selection result.

[0104] The system may be a user's electronic device or a computer system remotely located relative to that electronic device.

[0105] Figure 6 A schematic diagram of the structure of the computer device provided by the present invention.

[0106] The computer device 600 includes a processor 601 and a memory 602 coupled to the processor 601. The memory 602 stores program instructions. When the program instructions are executed by the processor 601, the processor 601 performs the steps of the sample classification method for improving host removal efficiency uniformity provided in the first aspect of the present invention or the host removal method for improving host removal efficiency uniformity through sample classification provided in the second aspect of the present invention.

[0107] The processor 601 can also be referred to as a CPU (Central Processing Unit). The processor 601 may be an integrated circuit chip with signal processing capabilities. The processor 601 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0108] Computer device 600 can be a mobile electronic device.

[0109] It should be understood that the systems, apparatuses, and methods described in this invention can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or modules, and may be electrical, mechanical, or other forms.

[0110] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0112] The above are merely embodiments of this application and do not limit the scope of this patent application. Any equivalent structural or procedural changes made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of this application.

Claims

1. A sample grading method for improving the uniformity of host removal efficiency, characterized in that, The method is performed by a computer and includes the following steps: Data Acquisition: Acquire the state data of the sample to be tested. The state of the sample to be tested includes: the sample is clear, similar to water; the sample is slightly turbid and semi-transparent; the sample is highly turbid or milky white; the sample is slightly viscous with a small amount of insoluble matter; the sample is highly viscous. Data processing: The state data of the sample to be tested is input into the constructed grading model, which performs sample grading based on the state data of the sample to be tested; Output results; Preferably, the sample to be tested is a human sample.

2. The method according to claim 1, characterized in that, The construction steps of the hierarchical model are as follows: Acquire the state data of the sample to be tested, wherein the state of the sample to be tested includes: the sample is clear, similar to water; the sample is slightly turbid and semi-transparent; the sample is highly turbid or milky white; the sample is slightly viscous with a small amount of insoluble matter; the sample is highly viscous; input the state data of the sample to be tested into a machine learning algorithm to construct a grading model; Preferably, the state of the sample to be tested may further include: sample pH, color, transparency, turbidity, viscosity, clot condition, pH, hemolysis index, protein concentration, cell number and activity; Preferably, the samples include blood samples, bile or bile drainage fluid samples, tissue samples, and swab samples; Preferably, the sample exhibits hemolysis.

3. The method according to claim 1, characterized in that, The hierarchical model can obtain four to ten levels of classification samples, with the optimal result achieved by obtaining five levels of sample classification using the following criteria: When the sample is clear and similar to water, it is classified as a Level 1 sample; when it is slightly turbid and semi-transparent, it is classified as a Level 2 sample; when it is highly turbid or milky white, it is classified as a Level 3 sample; when it is slightly viscous with a small amount of insoluble matter, it is classified as a Level 4 sample; and when it is highly viscous, it is classified as a Level 5 sample. Preferably, the grading model can further expand the number of grades based on sample pH, sample color, transparency, turbidity, viscosity, clot condition, pH, hemolysis index, protein concentration, cell number, and activity data.

4. A host removal method that improves host removal efficiency and uniformity through sample grading, characterized in that, The method is performed by a computer and includes the following steps: Data Acquisition: Acquire the graded data of the samples to be tested, wherein the graded data of the samples to be tested includes: Level 1 samples, Level 2 samples, Level 3 samples, Level 4 samples, and Level 5 samples; Data processing: The graded data of the sample to be tested is input into the constructed selection model, and the selection model selects host removal conditions based on the graded data of the sample to be tested; Output results; Preferably, the sample to be tested is a human sample.

5. The method according to claim 4, characterized in that, The steps for constructing the selection model are as follows: Obtain the graded data of the test samples, which includes: Level 1 samples, Level 2 samples, Level 3 samples, Level 4 samples, and Level 5 samples; input the graded data of the test samples into a machine learning algorithm to construct a selection model.

6. The method according to claim 4, characterized in that, The selection model obtains results based on the following criteria: When the sample to be tested is a Level 1 sample, the corresponding host removal conditions are: 1000 µl of original sample, 6 µl of lysis buffer, and 10 µl of nuclease; when the sample to be tested is a Level 2 sample, the corresponding host removal conditions are: 1000 µl of original sample, 20 µl of lysis buffer, and 10 µl of nuclease; when the sample to be tested is a Level 3 sample, the corresponding host removal conditions are: 200 µl of original sample, 25 µl of lysis buffer, and 15 µl of nuclease; when the sample to be tested is a Level 4 sample, the corresponding host removal conditions are: 250 µl of original sample, liquefaction with 0.5 times the volume of 4% NaOH, 5 µl of lysis buffer, and 12 µl of nuclease; when the sample to be tested is a Level 5 sample, the corresponding host removal conditions are: 150 µl of original sample, liquefaction with 2-3 times the volume of 4% NaOH, 5 µl of lysis buffer, and 12 µl of nuclease.

7. The method according to claim 2 or 5, characterized in that, The machine learning algorithms include algorithm models developed using various development tools; The development tools include TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell. The algorithm models include linear regression, logistic regression, Lasso regression, Ridge regression, linear discriminant analysis, nearest neighbor, decision tree, perceptron, neural network, support vector machine, naive Bayes, AdaBoost, GBDT, XGBoost, LightGBM, CatBoost, and random forest.

8. A sample grading system for improving the uniformity of host removal efficiency, characterized in that, The system includes: Data acquisition unit: used to acquire the state data of the sample to be tested, the state of the sample to be tested includes: the sample is clear, similar to water; the sample is slightly turbid and semi-transparent; the sample is highly turbid or milky white; the sample is slightly viscous with a small amount of insoluble matter; the sample is highly viscous. Data grading unit: used to perform grading monitoring on the data obtained by the data acquisition unit through the grading model obtained by the construction steps described in claim 2, so as to obtain the specific grading result of the sample to be tested; Result output unit: Used to output the hierarchical results; Preferably, the sample to be tested is a human sample.

9. A host removal system that improves host removal efficiency uniformity through sample grading, characterized in that, The system includes: Data acquisition unit: used to acquire the graded data of the sample to be tested, wherein the graded data of the sample to be tested includes: first-level sample, second-level sample, third-level sample, fourth-level sample, and fifth-level sample; Data classification unit: used to select host removal conditions for the data obtained by the data acquisition unit through the selection model obtained by the construction steps described in claim 5, so as to obtain the specific host removal conditions for the sample to be tested; Result output unit: Used to output the selection result; Preferably, the sample to be tested is a human sample.

10. A computer device, computer-readable storage medium, or computer program product, comprising a computer program, characterized in that, The device includes: a memory and a processor, the memory being used to store program instructions; the processor being used to invoke the program instructions, which, when executed, implement the steps of the sample grading method for improving host removal efficiency uniformity as described in any one of claims 1-3 or the steps of the host removal method for improving host removal efficiency uniformity through sample grading as described in any one of claims 4-6; The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the sample grading method for improving host removal efficiency uniformity as described in any one of claims 1-3, or the steps of the host removal method for improving host removal efficiency uniformity through sample grading as described in any one of claims 4-6. When the computer program is executed by the processor, it implements the steps of the sample classification method for improving the uniformity of host removal efficiency as described in any one of claims 1-3, or the steps of the host removal method for improving the uniformity of host removal efficiency through sample classification as described in any one of claims 4-6.

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