A multi-modal combined screening system for rare diseases of highland newborns and a storage medium

Abstract

The application discloses a highland newborn rare disease multi-mode combined screening system and a storage medium, and belongs to the technical field of medical software development. The application constructs a highland adaptive newborn rare disease multi-mode combined screening system. The screening system has highland adaptability, a significantly reduced cost, and a good application prospect.

Description

Technical Field

[0001] This invention belongs to the field of medical software development technology, specifically relating to a multimodal joint screening system for rare diseases in newborns in high-altitude areas and its storage medium. Background Technology

[0002] Newborn screening (NBS) is a crucial measure for preventing birth defects. The widespread application of NBS has effectively screened newborns for certain serious genetic and metabolic diseases. This facilitates early diagnosis and treatment of some serious genetic disorders, thus preventing severe consequences, including neonatal death. In the 1990s, the application of tandem mass spectrometry (MS / MS) significantly improved screening efficiency and was considered a major innovation. Currently, tandem mass spectrometry, biochemical immunoassay, and other methods are widely used to detect common neonatal inherited metabolic diseases, such as congenital hypothyroidism (CH), congenital adrenal hyperplasia (CAH), amino acid metabolism disorders, organic acid metabolism disorders, and fatty acid oxidation metabolism disorders. This method is known as traditional biochemical screening (TBS). Clinical practice has shown that this screening method is an efficient and cost-effective public health program. However, neonatal TBS has certain limitations. For example, due to the high sensitivity of this technology, MS / MS screening has a high false positive and false negative rate. This leads to a large number of newborns being recalled for re-examination. Simultaneously, there is also a certain rate of missed diagnoses. Due to their nonspecificity, disease diagnosis cannot be based solely on changes in small molecule metabolites.

[0003] Therefore, it is necessary to use next-generation sequencing (NGS) technology for pathogenic variant detection after MS / MS screening. More importantly, the types of diseases screened by TBS remain limited. In recent years, with the rapid development of NGS, genetic testing has played an important role in the characterization of major birth defects, providing new opportunities for further expanding the screening and diagnosis of neonatal genetic diseases.

[0004] Biochemical screening is susceptible to environmental factors (such as high altitude hypoxia and low temperature) that can interfere with metabolite stability, leading to false positives (such as fluctuations in phenylalanine concentration); gene screening (especially short-read NGS) struggles to detect complex structural variations, resulting in a high rate of missed diagnoses. Sample transport conditions in high-altitude environments (such as the freezing and thawing of blood spots) reduce the accuracy of mass spectrometry detection.

[0005] Currently, there is no newborn screening program that integrates high-altitude environment adaptation technology with an integrated screening, diagnosis, and treatment platform. Summary of the Invention

[0006] In order to solve the above-mentioned problems in the existing technology, the purpose of this invention is to provide a multimodal joint screening system for rare diseases in newborns in high-altitude areas and a storage medium.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A multimodal joint screening system for rare diseases in newborns at high altitudes, comprising:

[0009] The plateau sample collection module is configured to collect samples using a blood spot collection card containing antifreeze and record plateau environmental parameters.

[0010] The multimodal detection module is configured as follows:

[0011] Gene screening unit: Dynamically target and sequence genes of diseases prevalent in high-altitude areas using samples collected from the plateau sample collection module; dynamically adjust target points based on the variation frequency of the population in the plateau area to obtain gene variation data;

[0012] Biochemical screening unit: Mass spectrometry analysis is performed on samples collected in the plateau sample collection module to obtain metabolite concentration data;

[0013] The intelligent joint analysis module is configured as follows:

[0014] Input the plateau environmental parameters obtained from the plateau sample collection module, the gene variation data obtained from the gene screening unit in the multimodal detection module, and the metabolite concentration data obtained from the biochemical screening unit;

[0015] The plateau genetic risk index is calculated based on the input gene variation data, metabolite concentration data, and plateau environmental parameters.

[0016] Diagnostic results are output based on the plateau genetic risk index, gene variation data, and metabolite concentration data.

[0017] A report is generated based on the diagnostic results, providing pathogenicity classification and intervention recommendations;

[0018] The formula for calculating the plateau genetic risk index is: Plateau genetic risk index = (gene variation pathogenicity weight × allele frequency in plateau population) + (metabolite concentration shift × environmental stability coefficient);

[0019] Metabolite concentration shift = (metabolite concentration - reference median) / reference median × 100%, where the reference median is the healthy median concentration in the same age group and sex.

[0020] Environmental stability coefficient = blood oxygen saturation compensation term × temperature fluctuation compensation term;

[0021] The formula for calculating the blood oxygen saturation compensation term is as follows:

[0022] ;

[0023] The formula for calculating the temperature fluctuation compensation term is:

[0024] ;

[0025] k 1 To compensate for the negative correlation between metabolites and temperature, k 2 This is the fluctuation sensitivity coefficient. k 3 To compensate for long-term exposure to low temperatures, T avg For average transport temperature, ΔT For the temperature fluctuation range, t low Duration of exposure to low temperatures.

[0026] Preferably, the steps for outputting diagnostic results based on the high-altitude genetic risk index, gene variation data, and metabolite concentration data include:

[0027] The genetic risk index for high altitude was compared with the dynamic threshold, and the judgment rules are as follows:

[0028] (1) If the high-altitude genetic risk index is higher than the dynamic threshold:

[0029] 1) If a pathogenic variant exists and the metabolite concentration exceeds the variant adjustment threshold, then return "positive diagnosis" and the high-altitude genetic risk index;

[0030] 2) If the condition of "pathogenic variants exist and metabolite concentrations exceed the variant adjustment threshold" is not met, further third-generation sequencing verification is required, and a "negative" result and high-altitude genetic risk index should be returned.

[0031] (2) If the high-altitude genetic risk index is not higher than the dynamic threshold, then return "negative" and the high-altitude genetic risk index;

[0032] The formula for calculating the dynamic threshold is as follows:

[0033] .

[0034] Preferably, the metabolite concentration data has been calibrated for altitude adaptation based on blood oxygen saturation, a parameter relevant to high-altitude environments.

[0035] Preferred options also include:

[0036] The regional database support module is configured to store a database of plateau-specific diseases with localized allele frequencies; this database is iteratively optimized based on continuously updated clinical data from the hospital.

[0037] Preferably, the rare disease is non-syndromic deafness, congenital hypothyroidism, phenylketonuria, or spinal muscular atrophy.

[0038] The present invention also provides a computer-readable storage medium having stored thereon a computer program for implementing the above-mentioned multimodal joint screening system for rare diseases in newborns in high-altitude areas.

[0039] The present invention has achieved the following beneficial effects:

[0040] 1. Improved screening accuracy:

[0041] This invention proposes a novel method for calculating the genetic risk index of high altitude. By calculating this index, the system can reduce the false positive rate through gene-biochemical cross-validation.

[0042] 2. Efficiency and cost optimization:

[0043] (1) This invention reduces unnecessary sequencing by using dynamic gene panels, thus lowering costs;

[0044] (2) Improved efficiency of automated report generation.

[0045] In summary, this invention constructs a high-altitude-adaptive multimodal joint screening system for rare neonatal diseases, which enhances high-altitude adaptability, significantly reduces costs, and has good application prospects.

[0046] Obviously, based on the above description of the present invention, and according to common technical knowledge and conventional methods in the field, various other modifications, substitutions or alterations can be made without departing from the basic technical concept of the present invention.

[0047] The following detailed embodiments further illustrate the above-described content of the present invention. However, this should not be construed as limiting the scope of the present invention to the following examples. All technologies implemented based on the above-described content of the present invention fall within the scope of the present invention. Attached Figure Description

[0048] Figure 1 This is the core module and workflow of the multimodal joint screening system for rare diseases in newborns adapted to high altitudes. Detailed Implementation

[0049] It should be noted that the algorithms for data acquisition, transmission, storage and processing steps not specifically described in the embodiments, as well as the hardware structures and circuit connections not specifically described, can all be implemented using content already disclosed in the prior art.

[0050] Example 1: Multimodal Joint Screening System for Rare Diseases in Newborns Adapted to High Altitude

[0051] The core modules and workflow of the high-altitude adaptive newborn rare disease multimodal joint screening system are as follows: Figure 1 As shown.

[0052] Specifically:

[0053] 1. High-altitude sample collection module: Uses blood spot collection cards containing antifreeze to collect samples and record high-altitude environmental parameters.

[0054] Among them, the special blood spot collection card is a filter paper card containing antifreeze (such as glycerin) to ensure that blood cells do not rupture during transportation at -20°C.

[0055] 2. Multimodal detection module:

[0056] (1) Gene screening unit:

[0057] Using a third-generation sequencer, genes for diseases prevalent in high-altitude areas are targeted and amplified (e.g., GJB2, PAH, and SMN1 can be selected in this embodiment).

[0058] (2) Biochemical screening unit:

[0059] Mass spectrometry was used to analyze samples collected in the high-altitude sample acquisition module to obtain metabolite concentration data. Stabilizers were added during sample pretreatment to inhibit metabolite degradation caused by the low temperature at high altitudes.

[0060] 3. Intelligent Joint Analysis Module:

[0061] (1) Data input: Gene variation data obtained from the gene screening unit (VCF format) + metabolite concentration data obtained from the biochemical screening unit (CSV format);

[0062] (2) Cross-validation algorithm

[0063] Input data:

[0064] 1) Gene variation data (VCF format);

[0065] 2) Metabolite concentration data (CSV format);

[0066] 3) Plateau environmental parameters (JSON format, including key indicators such as SpO2).

[0067] Step 1, Environmental Compensation Calibration:

[0068] Based on the blood oxygen saturation (SpO2) value in the plateau environment parameters, the metabolite concentration data is calibrated for altitude adaptation to generate calibrated metabolite data.

[0069] Step 2, Genetic-Metabolic Association Analysis:

[0070] By combining genetic variation data (VCF) and calibrated metabolite data, the High Altitude Genetic Risk Index (HGRI, or risk score) is calculated. This index comprehensively assesses the association risk between genetic variation and metabolite concentration in a high-altitude environment.

[0071] Step 3, Dynamic Decision Tree (Threshold Adaptive):

[0072] Based on the comparison between the plateau genetic risk score and the dynamic threshold (calculated from the SpO2 value in the environmental parameters), the following judgments are made:

[0073] (1) If the high-altitude genetic risk index is higher than the dynamic threshold:

[0074] 1) Check for the presence of pathogenic variants (in conjunction with VCF) and ensure that the calibrated metabolite concentration exceeds the variant adjustment threshold: if the condition is met, return "positive diagnosis" and the high-altitude genetic risk index.

[0075] 2) If the above conditions are not met, but there are variants of unknown significance and abnormal metabolite concentrations: trigger third-generation sequencing verification (trigger_WES), return "negative" and the high-altitude genetic risk index (further verification required).

[0076] (2) If the risk score is not higher than the dynamic threshold, the default value will be "negative" and the high altitude genetic risk index.

[0077] The environmental compensation function is obtained by integrating blood oxygen saturation and temperature fluctuation parameters and correcting for metabolite concentrations (e.g., compensation for phenylalanine degradation rate under high-altitude low temperatures). The high-altitude environmental parameter (i.e., the environmental stability coefficient) is a composite parameter composed of blood oxygen saturation (SpO2) and temperature fluctuation parameters.

[0078] Environmental stability coefficient = [blood oxygen saturation compensation term] × [temperature fluctuation compensation term].

[0079] In the formula, the SpO2 Compensation Term is:

[0080]

[0081] The temperature fluctuation compensation term is as follows:

[0082]

[0083] The complete formula for the environmental stability coefficient is:

[0084]

[0085] Parameter description:

[0086] =0.005 (phenylalanine), 0.003 (creatine kinase): Temperature negative correlation compensation (low temperature slows down metabolite degradation, so the measured values ​​need to be corrected upwards).

[0087] =0.002: Fluctuation sensitivity coefficient (sudden increase due to damage to blood cells caused by drastic temperature changes);

[0088] =0.0015 / hour: Long-term low temperature exposure compensation.

[0089] The definitions and sources of each temperature parameter in the corrected formula are shown in Table 3.

[0090] Dynamic threshold mechanism: The judgment threshold is automatically adjusted based on the neonatal oxygen saturation (SpO2) (the threshold is lowered by 20% in high-altitude hypoxic environments). The HGRI threshold definition and reference range are shown in Table 1, and the threshold reference values ​​and adjustment rules are shown in Table 2.

[0091] Table 1 HGRI Threshold Definitions and Reference Ranges

[0092]

[0093] Table 2 Threshold Reference Values ​​and Adjustment Rules

[0094]

[0095] The formula for calculating the dynamic threshold is as follows:

[0096]

[0097] For example, when the newborn's SpO2 is 85%, the phenylalanine threshold is downregulated by:

[0098]

[0099] Dynamic threshold = 120 μmol / L × (1 - 10%) = 108 μmol / L

[0100] The formula for calculating the genetic risk index of high altitude is:

[0101] HGRI = (Pathogenicity weight of gene variant × Allele frequency in high-altitude populations) + (Metabolite concentration shift × Environmental stability coefficient)

[0102] In the environmental compensation function, the temperature fluctuation parameter is used to correct for metabolite concentrations, and the correction formula is as follows:

[0103]

[0104] Parameter description:

[0105] =0.005 (phenylalanine), 0.003 (creatine kinase): Temperature negative correlation compensation (low temperature slows down metabolite degradation, so the measured values ​​need to be corrected upwards).

[0106] =0.002: Fluctuation sensitivity coefficient (sudden increase due to damage to blood cells caused by drastic temperature changes);

[0107] =0.0015 / hour: Long-term low temperature exposure compensation.

[0108] The definitions and sources of each temperature parameter in the corrected formula are shown in Table 3.

[0109] Table 3 Definitions and sources of each temperature parameter

[0110]

[0111] (3) Output a report based on the results obtained in step (2), and provide pathogenicity classification and intervention recommendations.

[0112] 4. Regional database support module:

[0113] (1) Database of diseases specific to the population in plateau regions: storing localized allele frequencies;

[0114] (2) Real-time updates: Clinical data is captured from the hospital's HIS / LIS system using ETL tools, and then structured and stored in the database after NLP parsing.

[0115] The following experimental examples demonstrate the beneficial effects of the present invention. The experimental examples were performed according to the system and method described in Example 1.

[0116] Experimental Example 1: Multimodal Screening for Non-Syndromic Hearing Loss in Newborns at High Altitudes

[0117] Taking non-syndromic deafness as an example, a multimodal joint screening database for rare diseases in newborns is constructed. If other types of rare diseases are to be screened, the type of rare disease to be analyzed in step 2 and the high-frequency pathogenic genes of that type of rare disease in step 3 can be changed.

[0118] step:

[0119] 1. Data Acquisition and Preprocessing:

[0120] Blood spots were collected from 100 newborns (at an altitude of over 3000 meters):

[0121] Use antifreeze blood spot cards;

[0122] Environmental parameters: average SpO2 = 85%, average transport temperature Tavg = -19.3 ℃;

[0123] Parallel detection, detection metrics and methods are shown in Table 4:

[0124] Table 4. Indicators and methods for screening non-syndromic hearing loss in newborns at high altitudes.

[0125]

[0126] 2. HGRI dynamic analysis:

[0127] (1) Input data

[0128] Genetic variation data: Loaded from the "GJB2.vcf" file;

[0129] Metabolite concentration data: ATP concentration, glutamate concentration.

[0130] Plateau environmental parameters:

[0131] Blood oxygen saturation (SpO2): 85%;

[0132] Average temperature: -19.3℃.

[0133] (2) Analysis steps

[0134] Step 1, calculate the High Altitude Genetic Risk Index (HGRI):

[0135] The parameters include:

[0136] Gene weight: 1.2 (reflecting the pathogenicity of the GJB2 gene in high-altitude populations).

[0137] Allele frequency: 0.28 (frequency of the c.235delC variant in high-altitude populations);

[0138] Metabolite concentration data;

[0139] Environmental parameters.

[0140] Step 2, Dynamic Decision Making:

[0141] Set a risk threshold of 65. If the calculated risk score is higher than 65, check if there are known pathogenic variants in the VCF data. If there are pathogenic variants, return the diagnosis result of "positive for deafness".

[0142] Key parameters:

[0143] Metabolite reference values ​​(healthy newborns at an altitude of 4000m): ATP = 12.3 μmol / L, glutamate = 95 μmol / L;

[0144] Metabolite shift:

[0145]

[0146] 3. Regional database linkage:

[0147] Confirmed cases of deafness were extracted from the HIS system via ETL;

[0148] NLP analysis keywords: "congenital hearing loss", "GJB2 mutation" → ICD-10 H90.5;

[0149] Dynamically updated gene panel: 3 new high-frequency mutation sites in plateau regions (c.299_300delAT, c.176_191del16).

[0150] The results showed that the high-altitude adaptive neonatal rare disease multimodal joint screening system of the present invention has good high-altitude adaptability.

[0151] In summary, this invention constructs a multimodal joint screening system for rare diseases in newborns with high altitude adaptability. The screening system of this invention has enhanced high altitude adaptability, significantly reduced cost, and has good application prospects.

Claims

1. A multimodal joint screening system for rare diseases in newborns at high altitudes, characterized in that, The high-altitude newborn rare disease multimodal joint screening system includes: The plateau sample collection module is configured to collect samples using a blood spot collection card containing antifreeze and record plateau environmental parameters. The multimodal detection module is configured as follows: Gene screening unit: Dynamically target and sequence genes of diseases prevalent in high-altitude areas using samples collected from the plateau sample collection module; dynamically adjust target points based on the variation frequency of the population in the plateau area to obtain gene variation data; Biochemical screening unit: Mass spectrometry analysis is performed on samples collected in the plateau sample collection module to obtain metabolite concentration data; The intelligent joint analysis module is configured as follows: Input the plateau environmental parameters obtained from the plateau sample collection module, the gene variation data obtained from the gene screening unit in the multimodal detection module, and the metabolite concentration data obtained from the biochemical screening unit; The plateau genetic risk index is calculated based on the input gene variation data, metabolite concentration data, and plateau environmental parameters. Diagnostic results are output based on the plateau genetic risk index, gene variation data, and metabolite concentration data. A report is generated based on the diagnostic results, providing pathogenicity classification and intervention recommendations; The formula for calculating the plateau genetic risk index is: Plateau genetic risk index = (gene variation pathogenicity weight × allele frequency in plateau population) + (metabolite concentration shift × environmental stability coefficient); Metabolite concentration shift = (metabolite concentration - reference median) / reference median × 100%, where the reference median is the healthy median concentration in the same age group and sex. Environmental stability coefficient = blood oxygen saturation compensation term × temperature fluctuation compensation term; The formula for calculating the blood oxygen saturation compensation term is as follows: ； The formula for calculating the temperature fluctuation compensation term is: ； k 1 To compensate for the negative correlation between metabolites and temperature, k 2 This is the fluctuation sensitivity coefficient. k 3 To compensate for long-term exposure to low temperatures, T avg For average transport temperature, ΔT For the temperature fluctuation range, t low Duration of exposure to low temperatures.

2. The high-altitude newborn rare disease multimodal joint screening system according to claim 1, characterized in that, The steps for generating diagnostic results based on the high-altitude genetic risk index, gene variation data, and metabolite concentration data include: The genetic risk index for high altitude was compared with the dynamic threshold, and the judgment rules are as follows: (1) If the high-altitude genetic risk index is higher than the dynamic threshold: 1) If a pathogenic variant exists and the metabolite concentration exceeds the variant adjustment threshold, then return "positive diagnosis" and the high-altitude genetic risk index; 2) If the condition of "the presence of pathogenic variants and metabolite concentrations exceeding the variant adjustment threshold" is not met, further third-generation sequencing verification is required, and a "negative" result and high-altitude genetic risk index should be obtained. (2) If the high-altitude genetic risk index is not higher than the dynamic threshold, then return "negative" and the high-altitude genetic risk index; The formula for calculating the dynamic threshold is as follows: 。 3. The high-altitude newborn rare disease multimodal joint screening system according to claim 1, characterized in that, The metabolite concentration data have been calibrated for altitude adaptation based on blood oxygen saturation, a parameter relevant to high-altitude environments.

4. The high-altitude newborn rare disease multimodal joint screening system according to claim 1, characterized in that, Also includes: The regional database support module is configured to store a database of plateau-specific diseases with localized allele frequencies. The database is iteratively optimized based on continuously updated clinical data from the hospital.

5. The high-altitude newborn rare disease multimodal joint screening system according to claim 1, characterized in that, The rare diseases mentioned are non-syndromic deafness, congenital hypothyroidism, phenylketonuria, or spinal muscular atrophy.

6. A computer-readable storage medium, characterized in that, It stores a computer program for implementing the plateau newborn rare disease multimodal joint screening system as described in any one of claims 1 to 5.

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