HIV drug resistance analysis management system based on high-throughput sequencing

Abstract

The invention relates to the technical field of medical treatment, in particular to an HIV (Human Immunodeficiency Virus) drug resistance analysis management system based on high-throughput sequencing, which comprises a data storage module used for storing gene data, drug data and drug resistance mutation data; the preprocessing module is used for quality control and filtering of the original sequencing data, and comparison is carried out through the reference genome of the data storage module so as to generate variation site information capable of being analyzed; the detection module is used for analyzing the variation site information so as to obtain drug resistance related mutation data in the HIV gene; the prediction module is used for predicting sensitivity data of the HIV virus to various antiviral drugs; and the visualization module is used for carrying out visualization expression on the drug resistance related mutation data and the sensitivity data through images. According to the HIV drug resistance analysis management system based on high-throughput sequencing disclosed by the invention, a rapid and accurate HIV drug resistance analysis result is provided for clinicians.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to an HIV drug resistance analysis and management system based on high-throughput sequencing. Background Art

[0002] HIV drug resistance occurs when the virus, through genetic mutations, becomes less sensitive or insensitive to one or more antiviral drugs. This resistance can develop due to continued viral replication and genetic mutations under drug pressure, weakening or even rendering the drug's inhibitory effect ineffective. The core mechanism of drug resistance is closely related to HIV's high mutation rate—its reverse transcriptase lacks proofreading capabilities, producing billions of viral particles daily. Some of these mutants survive drug selection and become dominant strains.

[0003] Currently, sequencing can be used to analyze drug-resistance-related mutations in the HIV genome, such as mutations in the reverse transcriptase, protease, and integrase genes, to determine the virus's sensitivity to drugs. However, this method is highly complex to operate, which reduces the efficiency of obtaining analysis results. In addition, the detection sensitivity is low, making it difficult to accurately analyze HIV drug resistance. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides an HIV drug resistance analysis and management system based on high-throughput sequencing, which provides clinicians with fast and accurate HIV drug resistance analysis results.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] An HIV drug resistance analysis and management system based on high-throughput sequencing, including a data storage module, a preprocessing module, a detection module, a prediction module and a visualization module.

[0007] The data storage module is used for storing gene data, drug data, and drug-resistant mutation data;

[0008] The preprocessing module is used for quality control and filtering of raw sequencing data, and compares it with the reference genome of the data storage module to generate variant site information for analysis;

[0009] The detection module is used to acquire data from the pre-processing module, and the detection module analyzes the mutation site information to obtain drug resistance-related mutation data in the HIV gene;

[0010] The prediction module is used to obtain data from the data storage module and the detection module, and the prediction module compares the drug resistance-related mutation data with the drug resistance mutation data to predict the sensitivity data of the HIV virus to various antiviral drugs;

[0011] The visualization module is used to acquire data from the detection module and the prediction module, and to visualize the drug resistance-related mutation data and sensitivity data through images.

[0012] Furthermore, the preprocessing module removes connectors and deletes low-quality data to obtain first preprocessed data. The preprocessing module compares the first preprocessed data with the reference genome to obtain position information of each sequence, and obtains the variant site information based on the position information of each sequence.

[0013] Furthermore, the preprocessing module performs variant site detection on the position information of each sequence to obtain the variant site information.

[0014] Furthermore, the detection module performs mutation site detection after comparing the mutation site information with the gene data to obtain the mutation point.

[0015] Furthermore, the detection module matches the mutation point with the drug data to obtain drug resistance-related mutation data, and divides the drug resistance-related mutation data into major mutation data and minor mutation data.

[0016] Furthermore, the prediction module compares the drug resistance-related mutation data with the drug resistance mutation data through HIVdb to predict the sensitivity data of the HIV virus to various antiviral drugs, and generates treatment recommendations based on the sensitivity data to assist doctors in formulating treatment plans.

[0017] Furthermore, it also includes a report generation module, which is used for data acquisition of the detection module and the prediction module to generate a report on patient-related prediction data treatment, drug-resistant mutation data and prediction results.

[0018] Furthermore, it also includes a patient platform, and the data of the patient platform is communicated with the patient's terminal. The patient platform includes a drug resistance information module, a medication recommendation module and an inspection and monitoring module.

[0019] The drug resistance information module is used for data acquisition of the detection module and the prediction module to generate drug resistance information of the patient.

[0020] The medication suggestion module is used to obtain data from the prediction module. The medication suggestion module obtains medication data such as medication dosage and medication interval according to the treatment plan, and the medication suggestion module sends the medication data to the terminal.

[0021] The inspection and monitoring module is used for acquiring data from the prediction module, and the inspection and monitoring module obtains detection time information of drug resistance detection according to the treatment plan, and the inspection and monitoring module sends inspection reminder information to the terminal according to the detection time information.

[0022] Furthermore, the inspection and monitoring module compares the scheduled drug resistance detection time of the treatment plan with the current time. When the difference between the scheduled drug resistance detection time and the current time is equal to the reminder threshold, the inspection and monitoring module dials the terminal to remind the patient to undergo drug resistance detection through the intelligent customer service and determines the final detection time with the patient;

[0023] The inspection monitoring module compares the final inspection time with the current time, and when the current time is greater than the timeout threshold, the inspection monitoring module sends a timeout signal to the terminal and the hospital platform;

[0024] Furthermore, the patient platform also includes a psychological counseling module, which includes an assessment submodule and a counseling suggestion submodule.

[0025] The evaluation submodule is used to set up an evaluation questionnaire, and the evaluation submodule sends the evaluation questionnaire to the terminal, and the patient obtains a questionnaire score by filling out the evaluation questionnaire; the evaluation submodule obtains data from the detection module and the prediction module to obtain an influencing factor based on the drug resistance-related mutation data and sensitivity data;

[0026] The assessment submodule is provided with different risk levels, and each risk level corresponds to a different assessment score range; the assessment submodule calculates the assessment score corresponding to the patient based on the questionnaire score and the influencing factor, and the assessment submodule obtains the patient's risk level based on the assessment score; when the risk level is greater than the risk threshold, the assessment submodule sends the patient's information to the hospital platform;

[0027] The data storage module is also used for storing data of psychotherapy cases and corresponding patient conditions;

[0028] The counseling suggestion submodule is used to obtain data from the data storage module and the evaluation submodule. The counseling suggestion submodule determines the type of psychological problem of the patient based on the patient's risk level and the patient's answers to the evaluation questionnaire, and the counseling suggestion submodule obtains the corresponding counseling plan in the data storage module according to the type of psychological problem.

[0029] The beneficial effects of the present invention are:

[0030] 1. The preprocessing module removes low-quality sequences, eliminates contaminants and adapter sequences, processes duplicates and outliers, and reduces redundant data dimensions, saving storage space, accelerating downstream analysis, and improving downstream alignment accuracy. It also facilitates the detection module's detection of low-frequency variants. Leveraging data from the data storage module, the detection module analyzes variant site information, locating pathogenic mutations to improve sequencing data reliability and support large-scale genomic research, enabling the prediction module to accurately predict HIV susceptibility to various antiviral drugs. The visualization module also graphically displays predicted results for drug-resistant gene mutations and drug-resistant phenotypes, enabling clinicians to quickly understand a patient's condition.

[0031] 2. Through the patient platform, patients can keep abreast of their own conditions through the terminal. The medication recommendation module can provide targeted reminders for patients' medication. Patients who miss doses, have irregular doses, or interrupt treatment will suffer from insufficient drug concentrations, which will not be able to completely inhibit the virus and will lead to the selection and amplification of drug-resistant mutant strains. Under the action of the inspection and monitoring module, drug resistance testing is performed regularly through the terminal to avoid the failure to identify mutation sites through genotype resistance testing in a timely manner, resulting in delayed adjustment of treatment plans and inducing the accumulation of adaptive mutations in the virus. The psychological counseling module is used to provide psychological counseling to patients, improve treatment compliance, strengthen patients' understanding of drug compliance, reduce missed doses or interruptions in treatment, and at the same time warn of the risk of suicidal tendencies or worsening depression. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a structural block diagram of an HIV drug resistance analysis and management system based on high-throughput sequencing in a preferred embodiment of the present invention.

[0033] In the figure, 1-data storage module, 2-preprocessing module, 3-detection module, 4-prediction module, 5-visualization module, 6-report generation module, 7-patient platform, 71-drug resistance information module, 72-medication recommendation module, 73-inspection and monitoring module, 74-psychological counseling module, 741-assessment sub-module, 742-counseling suggestion sub-module. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0036] Please attend Figure 1 A preferred embodiment of the present invention is an HIV drug resistance analysis and management system based on high-throughput sequencing, which includes a data storage module 1, a preprocessing module 2, a detection module 3, a prediction module 4, a visualization module 5, a report generation module 6 and a patient platform 7.

[0037] The data storage module 1 is used for storing gene data, drug data, and drug-resistant mutation data.

[0038] The preprocessing module 2 is used for quality control and filtering of the original sequencing data, and compares it with the reference genome of the data storage module 1 to generate variant site information for analysis.

[0039] The preprocessing module 2 removes the connectors and deletes low-quality data to obtain first preprocessed data. The preprocessing module 2 compares the first preprocessed data with the reference genome to obtain the position information of each sequence, and obtains the variation site information based on the position information of each sequence.

[0040] The preprocessing module 2 performs mutation site detection on the position information of each sequence to obtain mutation site information.

[0041] The detection module 3 is used to obtain data from the pre-processing module 2, and the detection module 3 analyzes the mutation site information to obtain drug resistance-related mutation data in the HIV gene.

[0042] The detection module 3 matches the mutation points with the drug data to obtain drug resistance-related mutation data, and divides the drug resistance-related mutation data into major mutation data and minor mutation data.

[0043] The prediction module 4 is used to obtain data from the data storage module 1 and the detection module 3, and the prediction module 4 compares the drug resistance-related mutation data with the drug resistance mutation data to predict the sensitivity data of the HIV virus to various antiviral drugs.

[0044] Prediction module 4 compares drug resistance-related mutation data with drug resistance mutation data through HIVdb to predict the sensitivity data of HIV virus to various antiviral drugs, and generates treatment recommendations based on the sensitivity data to help doctors develop treatment plans.

[0045] The visualization module 5 is used to acquire data from the detection module 3 and the prediction module 4, and to visualize the drug resistance-related mutation data and sensitivity data through images.

[0046] The report generation module 6 is used to acquire data from the detection module 3 and the prediction module 4 to generate a report on the patient-related prediction data treatment, drug-resistant mutation data and prediction results.

[0047] Preprocessing module 2 removes low-quality sequences, eliminates contaminants and adapter sequences, processes duplicates and outliers, and reduces redundant data dimensions, saving storage space, accelerating downstream analysis, and improving downstream alignment accuracy. This also facilitates low-frequency variant detection in detection module 3. Leveraging data from data storage module 1, detection module 3 analyzes variant site information, locating pathogenic mutations to improve sequencing data reliability and support large-scale genomic research, enabling prediction module 4 to accurately predict HIV susceptibility to various antiviral drugs. Visualization module 5 also graphically displays predicted results for drug-resistant gene mutations and drug-resistant phenotypes, enabling clinicians to quickly understand a patient's condition.

[0048] The data of the patient platform 7 is communicated with the patient's terminal 8, and the patient platform includes a drug resistance information module 71, a medication suggestion module 72, and an inspection and monitoring module 73. The terminal 8 in this embodiment is a smart phone.

[0049] The drug resistance information module 71 is used to acquire data from the detection module 3 and the prediction module 4 to generate drug resistance information of the patient.

[0050] The medication recommendation module 72 is used to obtain data from the prediction module 4. The medication recommendation module 72 obtains medication data such as medication dosage and medication interval based on the treatment plan, and sends the medication data to the terminal 8. In this embodiment, the medication recommendation module 72 pushes medication reminders to the terminal 8 at the medication time, thereby providing targeted medication reminders for patients. Patients who miss doses, have irregular dosages, or interrupt treatment may experience insufficient drug concentrations, fail to fully inhibit the virus, and promote the selection and amplification of drug-resistant mutants.

[0051] The inspection monitoring module 73 is used to obtain data from the prediction module 4. The inspection monitoring module 73 obtains the detection time information of the drug resistance detection according to the treatment plan, and sends an inspection reminder message to the terminal 8 according to the detection time information.

[0052] In this embodiment, the inspection and monitoring module 73 compares the scheduled drug resistance detection time of the treatment plan with the current time. When the difference between the scheduled drug resistance detection time and the current time is equal to the reminder threshold, the inspection and monitoring module 73 dials the terminal 8 to remind the patient to undergo drug resistance detection through the intelligent customer service and confirm the final detection time with the patient;

[0053] The inspection monitoring module 73 compares the final inspection time with the current time. When the current time is greater than the timeout threshold, the inspection monitoring module 73 sends a timeout signal to the terminal 1 and the hospital platform.

[0054] The inspection and monitoring module 73 reminds the patient before the drug resistance test and after the scheduled drug resistance test time expires. The reminder is made through the robot customer service before the drug resistance test, which can reduce the workload of medical staff. The reminder is made manually after the scheduled drug resistance test time expires, so that the patient can go for drug resistance testing as soon as possible, avoiding the patient missing the test time and avoiding the failure to identify the mutation site in time through the genotype resistance test, resulting in delayed adjustment of the treatment plan and inducing the accumulation of adaptive mutations in the virus.

[0055] The patient platform 7 also includes a psychological counseling module 74, which includes an assessment submodule 741 and a counseling suggestion submodule 742.

[0056] The evaluation submodule 741 is used to set up the evaluation questionnaire, and the evaluation submodule 741 sends the evaluation questionnaire to the terminal 8. The patient obtains the questionnaire score by filling out the evaluation questionnaire. The evaluation submodule 741 obtains the data of the detection module 3 and the prediction module 4 to obtain the influencing factor based on the drug resistance-related mutation data and sensitivity data.

[0057] The assessment submodule 741 is configured with different risk levels, and each risk level corresponds to a different assessment score range. The assessment submodule 741 calculates the patient's corresponding assessment score based on the questionnaire score and the influencing factors, and the assessment submodule 741 determines the patient's risk level based on the assessment score. When the risk level is greater than the risk threshold, the assessment submodule 741 sends the patient's information to the hospital platform.

[0058] The evaluation submodule 741 of this embodiment conducts a comprehensive evaluation of comprehensive psychological problems through the patient's drug resistance and evaluation questionnaire, thereby improving the accuracy and effectiveness of the evaluation, thereby facilitating medical personnel to customize psychological counseling plans for patients and improve the patient's mental health level and treatment quality.

[0059] The data storage module 1 is also used for storing data of psychotherapy cases and corresponding patient conditions;

[0060] The counseling suggestion submodule 742 is used to obtain data from the data storage module 1 and the evaluation submodule 741. The counseling suggestion submodule 742 determines the type of psychological problem of the patient based on the patient's risk level and the patient's answers to the evaluation questionnaire, and the counseling suggestion submodule 742 obtains the corresponding counseling plan in the data storage module 1 according to the type of psychological problem.

[0061] The counseling suggestion submodule 742 uses psychological treatment cases and corresponding patient conditions to provide data support for the patient's psychological treatment, reducing the difficulty for doctors to formulate psychological counseling plans, and at the same time formulating effective psychological counseling plans for patients.

[0062] In this embodiment, the psychological counseling module 74 is used to provide psychological counseling to the patient, improve treatment compliance, strengthen the patient's understanding of medication compliance, reduce missed doses or treatment interruptions, and at the same time warn of the risk of suicidal tendencies or worsening depression.

Claims

1. An HIV drug resistance analysis and management system based on high-throughput sequencing, characterized in that: It includes a data storage module (1), a preprocessing module (2), a detection module (3), a prediction module (4) and a visualization module (5), The data storage module (1) is used for storing gene data, drug data, and drug-resistant mutation data; The preprocessing module (2) is used for quality control and filtering of the original sequencing data, and performs comparison with the reference genome of the data storage module (1) to generate variant site information for analysis; The detection module (3) is used to acquire data from the pre-processing module (2), and the detection module (3) analyzes the mutation site information to obtain drug resistance-related mutation data in the HIV gene; The prediction module (4) is used to obtain data from the data storage module (1) and the detection module (3), and the prediction module (4) compares the drug resistance-related mutation data with the drug resistance mutation data to predict the sensitivity data of the HIV virus to various antiviral drugs; The visualization module (5) is used to acquire data from the detection module (3) and the prediction module (4), and to visualize the drug resistance-related mutation data and sensitivity data through images.

2. The HIV drug resistance analysis and management system based on high-throughput sequencing according to claim 1, characterized in that: The preprocessing module (2) removes the connectors and deletes low-quality data to obtain first preprocessed data. The preprocessing module (2) compares the first preprocessed data with the reference genome to obtain position information of each sequence, and obtains the variant site information based on the position information of each sequence.

3. The HIV drug resistance analysis and management system based on high-throughput sequencing according to claim 2, characterized in that: The preprocessing module (2) performs variation site detection on the position information of each sequence to obtain the variation site information.

4. The HIV drug resistance analysis and management system based on high-throughput sequencing according to claim 1, characterized in that: The detection module (3) performs mutation site detection after comparing the mutation site information with the gene data to obtain the mutation point.

5. The HIV drug resistance analysis and management system based on high-throughput sequencing according to claim 4, characterized in that: The detection module (3) matches the mutation point with the drug data to obtain drug resistance-related mutation data, and divides the drug resistance-related mutation data into primary mutation data and secondary mutation data.

6. The HIV drug resistance analysis and management system based on high-throughput sequencing according to claim 1, characterized in that: The prediction module (4) compares the drug resistance-related mutation data with the drug resistance mutation data through HIVdb to predict the sensitivity data of HIV virus to various antiviral drugs, and generates a treatment recommendation based on the sensitivity data to assist doctors in formulating a treatment plan.

7. The HIV drug resistance analysis and management system based on high-throughput sequencing according to claim 6, characterized in that: The system also includes a report generation module (6), which is used for acquiring data from the detection module (3) and the prediction module (4) to generate a report on the predicted data treatment, drug-resistant mutation data and predicted results related to the patient.

8. The HIV drug resistance analysis and management system based on high-throughput sequencing according to claim 7, characterized in that: It also includes a patient platform (7), and the data of the patient platform (7) is communicated with the patient's terminal (8), and the patient platform includes a drug resistance information module (71), a medication suggestion module (72) and an inspection and monitoring module (73). The drug resistance information module (71) is used to acquire data from the detection module (3) and the prediction module (4) to generate drug resistance information of the patient; The medication suggestion module (72) is used for acquiring data from the prediction module (4), and the medication suggestion module (72) obtains medication data including medication dosage and medication interval according to the treatment plan, and the medication suggestion module (72) sends the medication data to the terminal (8); The inspection and monitoring module (73) is used for data acquisition of the prediction module (4), the inspection and monitoring module (73) obtains detection time information of drug resistance detection according to the treatment plan, and the inspection and monitoring module (73) sends inspection reminder information to the terminal (8) according to the detection time information.

9. The HIV drug resistance analysis and management system based on high-throughput sequencing according to claim 8, characterized in that: The inspection and monitoring module (73) compares the scheduled drug resistance detection time of the treatment plan with the current time. When the difference between the scheduled drug resistance detection time and the current time is equal to the reminder threshold, the inspection and monitoring module (73) dials the terminal (8) to remind the patient to undergo drug resistance detection through the intelligent customer service and determines the final detection time with the patient; The inspection monitoring module (73) compares the final inspection time with the current time, and when the current time is greater than a timeout threshold, the inspection monitoring module (73) sends a timeout signal to the terminal (1) and the hospital platform.

10. The HIV drug resistance analysis and management system based on high-throughput sequencing according to claim 8, characterized in that: The patient platform (7) further includes a psychological counseling module (74), which includes an assessment submodule (741) and a counseling suggestion submodule (742). The evaluation submodule (741) is used for setting the evaluation questionnaire, and the evaluation submodule (741) sends the evaluation questionnaire to the terminal (8), and the patient obtains the questionnaire score by filling in the evaluation questionnaire; the evaluation submodule (741) obtains the data of the detection module (3) and the prediction module (4) to obtain the influence factor according to the drug resistance-related mutation data and the sensitivity data; The assessment submodule (741) is provided with different risk levels, and each risk level corresponds to a different assessment score range; the assessment submodule (741) calculates the assessment score corresponding to the patient based on the questionnaire score and the influencing factor, and the assessment submodule (741) obtains the risk level of the patient based on the assessment score; When the risk level is greater than the risk threshold, the assessment submodule (741) sends the patient's information to the hospital platform; The data storage module (1) is also used for storing data of psychotherapy cases and corresponding patient conditions; The counseling suggestion submodule (742) is used for acquiring data from the data storage module (1) and the evaluation submodule (741). The counseling suggestion submodule (742) determines the type of psychological problem of the patient based on the risk level of the patient and the patient's answers to the evaluation questionnaire. The counseling suggestion submodule (742) obtains a corresponding counseling plan in the data storage module (1) based on the type of psychological problem.

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