Methods for detecting multiple snare-related proteins in adhd patients
By screening and matching capture antibodies, identifying spectral overflow interference and compensating for response values, and combining dynamic reference weights and neural network models, the shortcomings of single SNARE protein detection have been overcome, achieving efficient and accurate detection of SNARE protein complex in children with ADHD.
Patent Information
- Application Number
- CN202610329295.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-18
- Publication Date
- 2026-07-10
AI Technical Summary
In existing technologies, single SNARE protein detection cannot fully reflect the overall functional status of the SNARE complex, and multi-index detection is cumbersome and time-consuming, resulting in excessive consumption of trace samples.
By screening capture antibodies that best match various SNARE proteins, the initial response value was obtained by detecting mixed blood samples. The correlation of the response value was calculated to discriminate spectral spillover interference. The response value of the healthy control group was used for compensation. Combined with dynamic reference weights and a neural network model, the expression characteristics of the SNARE complex were obtained.
It achieves a comprehensive and accurate mapping of SNARE protein complex expression, simplifies the detection process, improves detection efficiency and accuracy, and eliminates signal crosstalk caused by homologous protein structures.
Smart Images
Figure CN122361820A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of chemical or physical testing technology, and specifically to a method for detecting multiple SNARE-related proteins in children with ADHD. Background Technology
[0002] ADHD (Attention-Deficit / Hyperactivity Disorder) is one of the most common neurodevelopmental disorders in childhood, and the SNARE protein complex plays a central role in the release of neurotransmitters.
[0003] Current methods rely on detecting the expression levels of single SNARE proteins for auxiliary analysis. However, ADHD involves multiple processes, including synaptic vesicle docking, initiation, and fusion. A single protein indicator cannot comprehensively reflect the overall functional state of the SNARE complex. Furthermore, simply stacking the detection of multiple related proteins requires multiple independent detection operations on the same sample, which not only excessively consumes small sample volumes but is also time-consuming and has low throughput. Therefore, existing methods suffer from drawbacks such as limited information from single SNARE protein detection, cumbersome traditional multi-indicator stacking operations, and a lack of effective information integration models. Summary of the Invention
[0004] To address the limitations of current SNARE protein complex detection methods in comprehensively and accurately reflecting the overall functional status of the SNARE complex, and their cumbersome operation, this invention aims to provide a method for detecting multiple SNARE-related proteins in children with ADHD. The specific technical solution adopted is as follows: This invention provides a method for detecting multiple SNARE-related proteins in children with ADHD, the method comprising: In the process of detecting multiple target proteins in blood samples of target children, the reaction combination formed by the target protein and any empirical capture antibody is determined, and the reaction combination is tested to determine the matching capture antibody that best matches the target protein among the empirical capture antibodies; The matching capture antibodies corresponding to various target proteins are mixed with blood samples, and the initial response value of the target protein is obtained by detecting the mixed blood sample. By utilizing the correlation between the initial response values of any two target proteins, the combination of target proteins with spectral spillover interference can be determined; The compensation response value and reference weight of the interfered protein are determined by using the initial response value of the interfered protein in the target protein combination and the corresponding mean response value in the healthy control group. Based on the compensated response value and reference weight, the weighted total response value of all target proteins and the expression characteristics of the SNARE complex were obtained.
[0005] Further, the testing of the reaction combination to determine the best-matching capture antibody among the empirical capture antibodies for the target protein includes: The actual performance parameters of the empirical capture antibodies were obtained by performing immunoassay tests on the reaction combination using a liquid-phase multiplex immunoassay platform. The performance difference between the actual performance parameter combination and the corresponding theoretical performance parameter combination is compared to determine the matching degree of the reaction combination. The empirical capture antibody with the highest matching degree is taken as the matching capture antibody that best matches the target protein.
[0006] Furthermore, determining the matching degree of the reaction combination by comparing the performance differences between the actual performance parameter combinations and the corresponding theoretical performance parameter combinations includes: Determine the Euclidean distance between the actual performance parameter combinations and the corresponding theoretical performance parameter combinations for each immunization test; The cumulative value of the Euclidean distance under multiple immunoassay is used to obtain the matching degree between the target protein and the empirical capture antibody in the reaction combination.
[0007] Further, the step of mixing the matching capture antibodies corresponding to various target proteins with blood samples and detecting the initial response value of the target protein in the mixed blood sample includes: Matching capture antibodies corresponding to various target proteins are covalently coupled to the corresponding encoded fluorescent microspheres and mixed to obtain the working solution of microspheres; The working solution of the microspheres is mixed with the blood sample of the target child to form a mixed blood sample. The initial response value of the target protein is obtained by detecting the mixed blood sample through a dual-laser liquid phase chip system.
[0008] Furthermore, the step of determining the combination of target proteins with spectral spillover interference by utilizing the correlation between the initial response values of any two target proteins includes: Determine the Pearson correlation coefficient between the initial response values of any two target proteins in the target patient; If the Pearson correlation coefficient is greater than the preset correlation threshold, it is determined that there is spectral spillover interference between the two target proteins in the target patient. Based on the distribution of spectral spillover interference among multiple children in the same subtype of the two target proteins, the combination of target proteins with spectral spillover interference was determined.
[0009] Furthermore, determining the combination of target proteins exhibiting spectral spillover interference based on the distribution of spectral spillover interference among multiple children of the same subtype for the two corresponding target proteins includes: Determine the percentage of patients with spectral overflow interference corresponding to the two target proteins in multiple patients of the same subtype relative to the total number of patients of the same subtype; If the proportion of the number is greater than a preset abnormal proportion constant, the combination of the two target proteins is identified as a target protein combination with spectral overflow interference.
[0010] Further, the determination of the compensated response value and reference weight of the interfered protein using the initial response value of the interfered protein in the target protein combination and the corresponding mean response value in the healthy control group includes: The predicted interference value of the interfered protein in the target protein combination is determined by using the initial response value of the interfered protein in the target protein combination and the mean response value of the interfered protein in the corresponding protein combination of the healthy control group. Using the predicted interference values of the interfered proteins in the target protein combination, the compensation response values and reference weights of the interfered proteins are determined respectively.
[0011] Furthermore, using the predicted interference values of the interfered proteins in the target protein combination, the compensation response values of the interfered proteins are determined, including: The predicted interference value of the interfered protein in the target protein combination is combined with the initial response value to obtain the compensated response value of the interfered protein in the target protein combination.
[0012] Furthermore, using the predicted interference values of the interfered proteins in the target protein combination, the reference weights of the interfered proteins are determined, including: Determine the percentage of predicted interference values of the affected proteins in the target protein combination for the target child relative to the sum of predicted interference values of the affected proteins in all protein combinations. The predicted interference percentage is used to determine the reference weight of the interfered protein in the target protein combination, wherein the predicted interference percentage is negatively correlated with the reference weight.
[0013] Furthermore, the weighted total response value of all target proteins and the expression characteristics of the SNARE complex, obtained based on the compensated response value and reference weights, include: Based on the compensated response value of the interfered protein and the reference weight, the first weighted response value of the interfered protein is obtained; The total weighted response value of all target proteins is obtained by combining the first weighted response value of the interfered protein in the target child with the second weighted response value of other uninterrupted proteins; The weighted total response value is input into a pre-trained neural network model to obtain the expression feature value of the SNARE complex.
[0014] The present invention has the following beneficial effects: This invention addresses the challenge of cross-reactivity caused by the structural similarity of SNARE homologous proteins. It screens for the most compatible capture antibodies for each SNARE protein, then detects the initial response values of SNARE proteins in blood samples mixed with these antibodies, aiming to ensure the accuracy and sensitivity of the detection results. By calculating the correlation between the response values of SNARE proteins, it identifies the presence of non-biological interference factors such as spectral spillover crosstalk and the combination of interfered proteins. Then, based on the corresponding response values in the healthy control group, it compensates for the response values of the interfered proteins. The corrected and compensated response values of the interfered proteins are combined with reference weights characterizing their numerical accuracy to calculate their weighted response values. This weighted total response value is then combined with the weighted response values of unaffected proteins to obtain the total weighted response value, which is then output as a digital feature value of ADHD-related SNARE protein expression via a neural network model.
[0015] Compared to existing methods, this invention overcomes the limitations of single-protein indicator characterization and breaks through the constraints of relying solely on monomeric protein concentration. By detecting the SNARE core complex and accessory proteome, it comprehensively maps the overall expression status of ADHD-related SNARE proteins. To address signal crosstalk caused by the high structural similarity of homologous proteins, pseudocollinearity is eliminated through methods such as epitope alignment screening, ensuring the specificity and accuracy of parallel multi-indicator detection. The introduction of dynamic reference weights and subtype-specific weighting algorithms transforms discrete biochemical data into unified "ADHD-related SNARE protein expression digital characteristic values," overcoming the bias caused by simple data aggregation. This not only comprehensively and accurately reflects the overall functional status of SNARE protein complex expression but also eliminates the need for independent detection operations for various SNARE protein aggregations, greatly simplifying the multi-indicator detection process and improving the detection efficiency of SNARE protein complexes. Attached Figure Description
[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 The flowchart below shows the steps of a method for detecting multiple SNARE-related proteins in children with ADHD, provided in one embodiment of the present invention. Figure 2 This is a detailed flowchart of step S1 in a method for detecting multiple SNARE-related proteins in children with ADHD, provided in an embodiment of the present invention. Figure 3This is a detailed flowchart of step S3 in a method for detecting multiple SNARE-related proteins in children with ADHD, provided in an embodiment of the present invention. Figure 4 This is a detailed flowchart of step S4 in a method for detecting multiple SNARE-related proteins in children with ADHD, provided in an embodiment of the present invention. Figure 5 This is a detailed flowchart of step S5 in a method for detecting multiple SNARE-related proteins in children with ADHD, provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of the hardware operating environment of various SNARE-related protein detection devices for children with ADHD involved in the embodiments of the present invention. Detailed Implementation
[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for detecting multiple SNARE-related proteins in children with ADHD according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0020] The following describes in detail, with reference to the accompanying drawings, a specific scheme for detecting multiple SNARE-related proteins in children with ADHD provided by the present invention.
[0021] Example 1: Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a method for detecting multiple SNARE-related proteins in children with ADHD, provided by an embodiment of the present invention.
[0022] The method for detecting multiple SNARE-related proteins in children with ADHD includes the following steps: Step S1: During the SNARE detection of multiple target proteins in the blood sample of the target child, determine the reaction combination formed by the target protein and any empirical capture antibody, and test the reaction combination to determine the matching capture antibody that best matches the target protein among the empirical capture antibodies. In this embodiment, two independent clinical cohorts can be constructed: a discovery cohort and a validation cohort.
[0023] The discovery cohort consisted of N newly diagnosed, medication-naïve children with ADHD and N age-, sex-, and IQ-matched healthy control children. ADHD subtypes were classified into three groups: attention-deficient predominant, hyperactive-impulsive predominantly, and mixed. This discovery cohort is primarily used in all embodiments of the present invention, including this one.
[0024] Validation cohort: Independent of the discovery cohort, another group of H children with ADHD and healthy control children are recruited for independent validation of the model.
[0025] Peripheral blood sample data of all subjects and children stored in the corresponding database were obtained, along with detailed clinical data of all subjects in the database, including data on multiple dimensions such as age, gender, ADHD diagnosis information (severity, SNAP-IV scale score).
[0026] The target protein refers to any SNARE protein, including at least the three core associated proteins SNAP-25, Syntaxin-1A and VAMP-2, as well as highly associated accessory proteins such as Munc18-1 and Munc13-1.
[0027] This embodiment selects a liquid-phase multiplex immunoassay platform based on fluorescently encoded microspheres (such as Luminex). This technology uses microspheres internally stained with two different fluorescent dyes, and by adjusting the dye ratio, a variety of unique fluorescent codes can be generated.
[0028] Suppose there are M target proteins in total, and during testing, it is necessary to select the corresponding fluorescently encoded microspheres for each target protein.
[0029] Specifically, please refer to Figure 2 Step S1, which involves testing the reaction combination to determine the most closely matched capture antibody among the empirical capture antibodies, includes: Step S11: The reaction combination is subjected to immunoassay using a liquid-phase multiplex immunoassay platform to obtain the actual performance parameter combination of the empirical capture antibodies. Step S12: Compare the performance differences between the actual performance parameter combination and the corresponding theoretical performance parameter combination to determine the matching degree of the reaction combination, and take the empirical capture antibody with the largest matching degree as the matching capture antibody that best matches the target protein.
[0030] More specifically, step S12, which compares the performance differences between the actual performance parameter combinations and the corresponding theoretical performance parameter combinations to determine the matching degree of the reaction combinations, includes: Determine the Euclidean distance between the actual performance parameter combinations and the corresponding theoretical performance parameter combinations for each immunization test; The cumulative value of the Euclidean distance under multiple immunoassay is used to obtain the matching degree between the target protein and the empirical capture antibody in the reaction combination.
[0031] In this embodiment, since cross-reactivity is the most common interference factor in the immunoassay process, the simultaneous detection of M functionally related and structurally homologous target proteins will face severe fluorescence spectral overflow crosstalk of the multisphere system and abnormal collinearity under pathological conditions.
[0032] First, based on experience, multiple (empirical) capture antibodies are selected for each target protein (where each target protein may have multiple monoclonal antibodies). Then, based on bioinformatics tools, the epitope sequences of M functionally related and homologous target proteins are compared to select the theoretical regions of the capture antibody for each target protein, such as the C-terminal region of SNAP-25 and the H3 domain of Syntaxin-1A.
[0033] Then, for each target protein, it is combined with each empirical capture antibody and detection antibody (a pre-defined antibody for labeling) in the same reaction system (assuming a total of F reaction combinations), and multiplex assays are performed using a "sandwich" immunoassay pattern. It should be noted that the goal here is to find the capture antibody that best matches the target protein, so various capture antibodies can be treated as a single variable in the reaction combination.
[0034] S tests were performed. Each test yielded the sensitivity, linear range, and detection limit of the capture antibody. Combining these three parameters yields a characteristic combination, i.e., a combination of actual performance parameters. For the F-th combination, the matching degree is determined by analyzing the differences between its antibody sensitivity, linear range, detection limit, and theoretical values under the S-tests. Specifically, the mathematical formula for the matching degree of the f-th combination can be expressed as: In the formula, This represents the matching degree of the f-th combination. This represents the total number of tests. This represents the Euclidean distance between the feature combination and the theoretical feature combination under the s-th test, which is also the Euclidean distance between the actual performance parameter combination and the corresponding theoretical performance parameter combination. To prevent the denominator from being a very small positive number, for example, 0.001 is used.
[0035] The matching degree of each reaction combination can be calculated using the above method. Combinations with a matching degree greater than a preset empirical threshold (empirical value, such as 0.5, set according to the actual detection scenario) are recorded as matchable combinations. Then, the capture antibody corresponding to the maximum matching degree of each target protein in the matchable combination is recorded as the matching capture antibody for the corresponding target protein. Furthermore, the most matching capture antibody for each target protein can be obtained using the above method (the "capture antibody" mentioned in subsequent embodiments refers to the "matching capture antibody").
[0036] The optimal coating concentration for each matching capture antibody and the optimal working concentration for each detection antibody were then determined using checkerboard titration.
[0037] Furthermore, based on the above implementation process, the matching capture antibody for each target protein, the optimal coating concentration of the matching capture antibody, and the optimal working concentration of each detection antibody can be obtained.
[0038] Step S2: Mix the matching capture antibodies corresponding to various target proteins with the blood sample, and detect the mixed blood sample to obtain the initial response value of the target protein; Specifically, step S2 includes: Matching capture antibodies corresponding to various target proteins are covalently coupled to the corresponding encoded fluorescent microspheres and mixed to obtain the working solution of microspheres; The working solution of the microspheres is mixed with the blood sample of the target child to form a mixed blood sample. The initial response value of the target protein is obtained by detecting the mixed blood sample through a dual-laser liquid phase chip system.
[0039] In this embodiment, for children with the same subtype of ADHD, assuming there are Y children in this type of ADHD, M types of capture antibodies are first covalently coupled to the corresponding encoded fluorescent microspheres via a carbodiimide chemical reaction. The antibody loading per milligram of microspheres is confirmed by flow cytometry to ensure that the batch-to-batch coefficient of variation is less than 5%.
[0040] Then, the M types of coupled microspheres are mixed in a preset ratio to prepare a "multi-capture working solution", i.e., a microsphere working solution.
[0041] Blood samples from Y children were mixed with the microsphere working solution in a 96-well plate. A sandwich structure of "microsphere-capture antibody-target protein-detection antibody" was formed using the optimal working concentration of the detection antibody. Then, a dual-laser liquid chromatography-array system was used to obtain the initial response value of each target protein for each child.
[0042] Step S3: Utilize the correlation between the initial response values of any two target proteins to determine the combination of target proteins that are subject to spectral spillover interference. Specifically, please refer to Figure 3Step S3 includes: Step S31: Determine the Pearson correlation coefficient between the initial response values of any two target proteins in the target patient; Step S32: If the Pearson correlation coefficient is greater than the preset correlation threshold, it is determined that there is spectral overflow interference between the two target proteins in the target patient. Step S33: Based on the distribution of spectral overflow interference of the two target proteins in multiple children of the same subtype, determine the combination of target proteins with spectral overflow interference.
[0043] More specifically, step S33 includes: Determine the percentage of patients with spectral overflow interference corresponding to the two target proteins in multiple patients of the same subtype relative to the total number of patients of the same subtype; If the proportion of the number is greater than a preset abnormal proportion constant, the combination of the two target proteins is identified as a target protein combination with spectral overflow interference.
[0044] In this embodiment, due to the homology of SNARE family proteins, even if the best-matching capture antibody is selected, abnormal collinearity may still exist in the pathological state of the multiple microsphere system at the physical level, i.e., spectral spillover interference may occur. Therefore, it is necessary to correct the initial response value for each child. The specific correction process is as follows: First, the initial response values of the M target proteins were summarized for all ADHD children (covering three subtypes) in the cohort.
[0045] Then, the Pearson correlation coefficient between the raw response values of the i-th and j-th target proteins for each patient is calculated, denoted as . Furthermore, when multiple detection systems are combined, if there is fluorescence spectral overflow crosstalk between target protein i and target protein j in the multiple microsphere system, then in a large number of samples, the initial signal value (response value) of target protein i will exhibit a non-biological characteristic of "forced collinearity" as the intensity of the initial signal value of target protein j changes.
[0046] Therefore if If the value is greater than 0.92 (preset relevant threshold, which can be adjusted), it indicates that the i-th target protein and the j-th target protein of the M-th patient are interfering with each other.
[0047] Similarly, the above method can be used to calculate the cost for each patient. Values. Then calculate the values for each subtype of children. Patients with a value greater than 0.92. If the number of patients with a value greater than 0.92 in any of the three subtypes exceeds the preset value. If the condition is as described above, it indicates that the i-th target protein and the j-th target protein interfere to some extent. (Preset value) Y is the product of the total number of samples of this subtype (all children) and a preset abnormality proportion constant (e.g., 10%). The above calculation process is the same as "when the proportion of the number is greater than the preset abnormality proportion constant, the combination of the two target proteins is determined as the target protein combination with spectral overflow interference".
[0048] It should be noted that both the i-th and j-th target proteins can be considered as interfered proteins. If the concentrations of the two target proteins can be distinguished, the protein with the lower concentration is considered the interfered protein. Alternatively, accessory proteins highly associated with the pathogenesis of ADHD can be considered as interfered proteins.
[0049] Based on the above implementation process, all target protein groups z with interference can be calculated, assuming there are Z groups in total.
[0050] Step S4: Using the initial response values of the interfered proteins in the target protein combination and the corresponding mean response values in the healthy control group, determine the compensation response value and reference weight of the interfered proteins. Specifically, please refer to Figure 4 Step S4 includes: Step S41: Using the initial response value of the interfered protein in the target protein combination and the mean response value of the interfered protein in the corresponding protein combination of the healthy control group, determine the predicted interference value of the interfered protein in the target protein combination. In this embodiment, for the target protein group with interference in group z, the response value of the interfered protein in the target protein group with interference in group z of the healthy control group is first used as a benchmark to obtain the predicted interference value of the interfered protein in group z of each child, denoted as . The mathematical formula can be expressed as: In the formula, This represents the predicted interference value of the target protein (interfered protein) in group z. This represents the initial response value of the target protein in group z where interference exists. This represents the statistical mean of the response values of the target protein in the target protein group within the healthy control group. This represents the absolute value of the difference between the two.
[0051] Step S42: Using the predicted interference values of the interfered proteins in the target protein combination, determine the compensation response values and reference weights of the interfered proteins respectively.
[0052] In one embodiment, step S42, which uses the predicted interference value of the interfered protein in the target protein combination to determine the compensation response value of the interfered protein, includes: The predicted interference value of the interfered protein in the target protein combination is combined with the initial response value to obtain the compensated response value of the interfered protein in the target protein combination.
[0053] In this embodiment, for a certain subtype of the patient group, the compensated value of the interfering target protein in the z-th group can be expressed as: In the formula, This represents the compensation response value of the target protein in group z that is subject to interference. This represents the initial response value of the target protein in group z where interference exists. This represents the predicted interference value of the target protein in group z where interference exists.
[0054] In real-world scenarios, the strong fluorescence signals emitted by high-concentration proteins can cause cross-channel spectral overflow crosstalk, leading to increased background noise in low-concentration protein channels and thus "suppressing" the response values of low-concentration proteins. Therefore, by analyzing the signal ratio of high-concentration proteins (such as SNAP-25) to low-concentration accessory proteins (such as Munc13-1) in children of the same subtype, if the signal ratio is greater than a preset ratio threshold (generally determined through control experiments) and the total signal intensity is found to be close to the preset detection upper limit, the above compensation scheme can be automatically activated for compensation.
[0055] In another embodiment, step S42, which determines the reference weight of the interfered protein using the predicted interference value of the interfered protein in the target protein combination, includes: Determine the percentage of predicted interference values of the affected proteins in the target protein combination for the target child relative to the sum of predicted interference values of the affected proteins in all protein combinations. The predicted interference percentage is used to determine the reference weight of the interfered protein in the target protein combination, wherein the predicted interference percentage is negatively correlated with the reference weight.
[0056] Based on the above embodiments, in this embodiment, the final response value of each target protein for each child is obtained, i.e., the weighted response value. Based on the above analysis, it can be determined which target protein combinations have received response value compensation, indicating that these target protein combinations may have biases. Therefore, these target protein combinations are assigned higher fault weights. Thus, the fault weight of the interfering target proteins in group z can be expressed as: In the formula, This represents the fault weight of the target protein in group z that is subject to interference. This represents the predicted interference value of the target protein in group z where interference exists. This represents the sum of predicted interference values for all target proteins in group Z that exhibit interference; the ratio of these two values represents the predicted interference percentage. It should be noted that in practical applications, d is generally not 0. When d is 0, it indicates no interference; in this case, it is directly... Set to 0.
[0057] A higher fault weight for the interfering target protein in group z indicates a potentially larger numerical deviation, suggesting a lower reference weight in subsequent analyses. The overall weight of the interfering target protein is set to 0.3. Therefore, the reference weight value for the interfering target protein in group z can be set to... The weight of the target protein that does not interfere (the unelected protein) is set to 0.7. Both 0.3 and 0.7 can be adjusted according to the specific circumstances.
[0058] Step S5: Based on the compensated response value and reference weight, obtain the weighted total response value of all target proteins and the expression characteristics of the SNARE complex.
[0059] Specifically, please refer to Figure 5 Step S5 includes: Step S51: Based on the compensated response value of the interfered protein and the reference weight, obtain the first weighted response value of the interfered protein; Step S52: Combine the first weighted response value of the interfered protein of the target child with the second weighted response value of other uninterrupted proteins to obtain the total weighted response value of all target proteins; Step S53: Input the weighted total response value into the pre-trained neural network model to obtain the expression feature value of the SNARE complex.
[0060] Based on the above embodiments, in this embodiment, for a certain subtype of children, the compensated response value and reference weight of the interfering target protein in group z for each patient can be obtained according to the above implementation process. Then, for each patient in a certain subtype, the weighted response values of all their target proteins are input into a pre-trained neural network model (such as a convolutional neural network) to output the expression numerical feature value of the ADHD-related SNARE protein complex for each patient. Specifically, for the interfering protein, its compensated response value is combined with the reference weight to obtain its first weighted response value; for the uninterfered protein, its original response value is combined with its reference weight (such as 0.7 above) to obtain a second weighted response value, and then the first weighted response values and the second weighted response values are superimposed to obtain the total weighted response value.
[0061] This invention overcomes the limitations of single-protein indicator characterization and breaks through the constraints of relying solely on monomeric protein concentration. By detecting the SNARE core complex and accessory proteome, it comprehensively maps the overall expression status of ADHD-related SNARE proteins. To address signal crosstalk caused by the high structural similarity of homologous proteins, methods such as "epitope alignment screening" are used to eliminate pseudocollinearity, ensuring the specificity and accuracy of parallel multi-indicator detection. The introduction of dynamic reference weights and subtype-specific weighting algorithms transforms discrete biochemical data into unified "ADHD-related SNARE protein expression digital characteristic values," overcoming the bias caused by simple data aggregation. This not only comprehensively and accurately reflects the overall functional status of SNARE protein complex expression but also eliminates the need for independent detection operations for various SNARE protein aggregations, greatly simplifying the multi-indicator detection process and improving the detection efficiency of SNARE protein complexes.
[0062] Example 2: This invention also proposes a device for detecting multiple SNARE-related proteins in children with ADHD. The device can be a data processing device such as a computer or server, or a combination of multiple devices.
[0063] like Figure 6 As shown, Figure 6 This is a schematic diagram of the hardware operating environment of various SNARE-related protein detection devices for children with ADHD, as described in the embodiments of the present invention.
[0064] like Figure 6 As shown, the device for detecting multiple SNARE-related proteins in children with ADHD may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to establish communication between these components. The user interface 1003 may include a display or an input unit such as a control panel; the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001. The memory 1005, as a computer storage medium, may include a program for detecting multiple SNARE-related proteins in children with ADHD (hereinafter referred to as the "multiple SNARE-related protein detection program").
[0065] Those skilled in the art will understand that Figure 6The hardware structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0066] Continue to refer to Figure 6 , Figure 6 The memory 1005, which is a computer-readable storage medium, may include an operating device, a user interface module, a network communication module, and various SNARE-related protein detection programs for children with ADHD.
[0067] exist Figure 6 In this embodiment, the network communication module is mainly used to connect to the server and can communicate with the server for data; while the processor 1001 can call the various SNARE-related protein detection programs for ADHD children stored in the memory 1005 and execute the steps in the above embodiments.
[0068] Based on the hardware structure of the above-mentioned SNARE-related protein detection device for children with ADHD, various embodiments of the present invention for the detection of multiple SNARE-related proteins for children with ADHD are implemented.
[0069] Furthermore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a detection program for multiple SNARE-related proteins in children with ADHD, wherein when the detection program for multiple SNARE-related proteins in children with ADHD is executed by a processor, it implements the steps of the method corresponding to the above-described detection method for multiple SNARE-related proteins in children with ADHD.
[0070] The method implemented when performing the multiple SNARE-related protein detection procedure for children with ADHD can be referred to in the various embodiments of the present invention for the multiple SNARE-related protein detection method for children with ADHD, and will not be repeated here.
[0071] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0072] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0073] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0074] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural / method transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.
Claims
1. A method for detecting multiple SNARE-related proteins in children with ADHD, characterized in that, The method includes: In the process of detecting multiple target proteins in blood samples of target children, the reaction combination formed by the target protein and any empirical capture antibody is determined, and the reaction combination is tested to determine the matching capture antibody that best matches the target protein among the empirical capture antibodies; The matching capture antibodies corresponding to various target proteins are mixed with blood samples, and the initial response value of the target protein is obtained by detecting the mixed blood sample. By utilizing the correlation between the initial response values of any two target proteins, the combination of target proteins with spectral spillover interference can be determined; The compensation response value and reference weight of the interfered protein are determined by using the initial response value of the interfered protein in the target protein combination and the corresponding mean response value in the healthy control group. Based on the compensated response value and reference weight, the weighted total response value of all target proteins and the expression characteristics of the SNARE complex were obtained.
2. The method for detecting multiple SNARE-related proteins in children with ADHD according to claim 1, characterized in that, The testing of the reaction combination to determine the best-matching capture antibody among the empirical capture antibodies for the target protein includes: The actual performance parameters of the empirical capture antibodies were obtained by performing immunoassay tests on the reaction combination using a liquid-phase multiplex immunoassay platform. The performance difference between the actual performance parameter combination and the corresponding theoretical performance parameter combination is compared to determine the matching degree of the reaction combination. The empirical capture antibody with the highest matching degree is taken as the matching capture antibody that best matches the target protein.
3. The method for detecting multiple SNARE-related proteins in children with ADHD according to claim 2, characterized in that, The determination of the matching degree of the reaction combination by comparing the performance differences between the actual performance parameter combination and the corresponding theoretical performance parameter combination includes: Determine the Euclidean distance between the actual performance parameter combinations and the corresponding theoretical performance parameter combinations for each immunization test; The cumulative value of the Euclidean distance under multiple immunoassay is used to obtain the matching degree between the target protein and the empirical capture antibody in the reaction combination.
4. The method for detecting multiple SNARE-related proteins in children with ADHD according to claim 1, characterized in that, The step of mixing various target proteins with matching capture antibodies and blood samples, and detecting the initial response value of the target protein in the mixed blood sample, includes: Matching capture antibodies corresponding to various target proteins are covalently coupled to the corresponding encoded fluorescent microspheres and mixed to obtain the working solution of microspheres; The working solution of the microspheres is mixed with the blood sample of the target child to form a mixed blood sample. The initial response value of the target protein is obtained by detecting the mixed blood sample through a dual-laser liquid phase chip system.
5. The method for detecting multiple SNARE-related proteins in children with ADHD according to claim 1, characterized in that, The method of determining the combination of target proteins with spectral spillover interference by utilizing the correlation between the initial response values of any two target proteins includes: Determine the Pearson correlation coefficient between the initial response values of any two target proteins in the target patient; If the Pearson correlation coefficient is greater than the preset correlation threshold, it is determined that there is spectral spillover interference between the two target proteins in the target patient. Based on the distribution of spectral spillover interference among multiple children in the same subtype of the two target proteins, the combination of target proteins with spectral spillover interference was determined.
6. The method for detecting multiple SNARE-related proteins in children with ADHD according to claim 5, characterized in that, The determination of the target protein combination with spectral spillover interference based on the distribution of spectral spillover interference among multiple children of the same subtype of the two target proteins includes: Determine the percentage of patients with spectral overflow interference corresponding to the two target proteins in multiple patients of the same subtype relative to the total number of patients of the same subtype; If the proportion of the number is greater than a preset abnormal proportion constant, the combination of the two target proteins is identified as a target protein combination with spectral overflow interference.
7. The method for detecting multiple SNARE-related proteins in children with ADHD according to claim 1, characterized in that, The determination of the compensated response value and reference weight of the interfered protein using the initial response value of the interfered protein in the target protein combination and the corresponding mean response value in the healthy control group includes: The predicted interference value of the interfered protein in the target protein combination is determined by using the initial response value of the interfered protein in the target protein combination and the mean response value of the interfered protein in the corresponding protein combination of the healthy control group. Using the predicted interference values of the interfered proteins in the target protein combination, the compensation response values and reference weights of the interfered proteins are determined respectively.
8. The method for detecting multiple SNARE-related proteins in children with ADHD according to claim 7, characterized in that, Using the predicted interference values of the interfered proteins in the target protein combination, the compensation response values of the interfered proteins are determined, including: The predicted interference value of the interfered protein in the target protein combination is combined with the initial response value to obtain the compensated response value of the interfered protein in the target protein combination.
9. The method for detecting multiple SNARE-related proteins in children with ADHD according to claim 1, characterized in that, Using the predicted interference values of the interfered proteins in the target protein combination, the reference weights of the interfered proteins are determined, including: Determine the percentage of predicted interference values of the affected proteins in the target protein combination for the target child relative to the sum of predicted interference values of the affected proteins in all protein combinations. The predicted interference percentage is used to determine the reference weight of the interfered protein in the target protein combination, wherein the predicted interference percentage is negatively correlated with the reference weight.
10. The method for detecting multiple SNARE-related proteins in children with ADHD according to claim 1, characterized in that, The weighted total response value of all target proteins and the expression characteristics of the SNARE complex, based on the compensated response value and reference weights, are obtained, including: Based on the compensated response value of the interfered protein and the reference weight, the first weighted response value of the interfered protein is obtained; The total weighted response value of all target proteins is obtained by combining the first weighted response value of the interfered protein in the target child with the second weighted response value of other uninterrupted proteins; The weighted total response value is input into a pre-trained neural network model to obtain the expression feature value of the SNARE complex.