A method for evaluating the effectiveness of autism interventions and its application.

Abstract

This application relates to the field of biomedical technology, and in particular to a method for evaluating the effectiveness of autism intervention and its application. The method includes the following steps: collecting a test sample and a control sample; the test sample is a urine sample from an autism patient, and the control sample is a urine sample from a healthy volunteer; processing the test sample and the control sample separately to obtain NMR samples; testing the NMR samples using an NMR spectrometer to obtain NMR spectra; performing multivariate statistical analysis on the NMR spectra to construct a urinary metabolite model; and using the urinary metabolite model to screen and evaluate autism patients. This application provides a method for evaluating the effectiveness of autism intervention and its application, constructing a urinary metabolite model through NMR metabolomics technology, providing practical evidence for establishing methods for early screening and treatment process evaluation of autism.

Description

Technical Field

[0001] This application relates to the field of biomedical technology, and in particular to an identification method for evaluating the effectiveness of autism intervention and its application. Background Technology

[0002] Autistic Spectrum Disorder (ASD) is a highly heterogeneous mental developmental disorder characterized by impairments in social and emotional interaction, including verbal and nonverbal communication difficulties in developing, maintaining, and understanding interpersonal relationships, and sometimes repetitive, stereotyped behaviors and narrow interests. The alarming increase in the prevalence of autism in recent years has become a significant public health issue, drawing considerable attention. The pathogenesis of ASD is complex, and currently there is no specific treatment; only medications are used to alleviate some symptoms. However, numerous studies have shown that early diagnosis and intervention have advantages in improving the prognosis of ASD patients. However, early diagnosis of ASD often relies on clinicians' experience and assessments using autism behavior scales, which can lead to subjective biases and delayed early intervention in some cases. Furthermore, the evaluation of the effectiveness of early intervention is time-consuming, reducing treatment adherence in some patients. Therefore, establishing a simple and rapid screening and analysis method based on metabolic biomarkers is of significant scientific importance for improving the prognosis of autism patients in my country and reducing the social burden. Summary of the Invention

[0003] This application provides an identification method and its application for evaluating the effectiveness of autism intervention, enabling early screening and evaluation of the efficacy of autism treatment.

[0004] To address the aforementioned technical problems, in a first aspect, embodiments of this application provide a method for evaluating the effectiveness of autism intervention and its application, comprising: collecting a test sample and a control sample; the test sample being a urine sample from an autism patient, and the control sample being a urine sample from a healthy volunteer; processing the test sample and the control sample separately to obtain nuclear magnetic resonance (NMR) samples; testing the NMR samples using an NMR spectrometer to obtain NMR spectra; performing multivariate statistical analysis on the NMR spectra to construct a urine metabolite model; and using the constructed urine metabolite model to screen autism patients and evaluate the effectiveness of autism intervention.

[0005] In some exemplary embodiments, multivariate statistical analysis is performed on nuclear magnetic resonance spectra to construct a urine metabolite model, including: performing cluster analysis on nuclear magnetic resonance spectra; comparing the nuclear magnetic resonance spectra of the test sample and the control sample, performing orthogonal partial least squares discriminant analysis, and constructing a urine metabolite model.

[0006] In some exemplary embodiments, cluster analysis includes principal component analysis and orthogonal partial least squares discriminant analysis; principal component analysis is used to analyze nuclear magnetic resonance spectra, including the following steps: organizing the nuclear magnetic resonance spectrum data into a pattern that can be directly imported into SIMCA-P software for analysis, obtaining a dataset; importing the dataset into SIMCA-P software; selecting to build a PCA-X model in modeltype, setting data grouping according to the experimental grouping of the test sample and control sample, and selecting UV scaling mode; automatically fitting the model, viewing the score plot and loading plot; and using permutation analysis to verify whether the model is overfitted.

[0007] In some exemplary embodiments, the orthogonal partial least squares method is used to discriminate and analyze nuclear magnetic resonance spectra, including the following steps: organizing the nuclear magnetic resonance spectrum data into a format that can be directly imported into SIMCA-P software for analysis to obtain a dataset; importing the dataset into SIMCA-P software; selecting to build an OPLS-DA model in the modeltype, setting the data grouping according to the experimental grouping of the test sample and the control sample, and selecting UV as the scaling mode; automatically fitting the model and viewing the score plot and loading plot; and using permutation analysis to verify whether the model is overfitted.

[0008] In some exemplary embodiments, a urine metabolite model is used to screen patients with autism and evaluate the effectiveness of autism intervention, including: obtaining differentially expressed metabolic sites based on the urine metabolite model; determining whether the VIP value of the differentially expressed metabolic site is greater than 1; if so, identifying and quantifying the differentially expressed metabolic site using a targeted profile analysis quantitative module; if not, no processing is performed.

[0009] In some exemplary embodiments, a targeted profile analysis quantitative module is used to identify and quantify differentially metabolizing sites, including: the targeted profile analysis quantitative module performs deconvolution and multivariate linear fitting on the differentially metabolizing sites, identifies and quantifies the NMR curve of the target compound from the NMR spectrum, and calculates the content of the target compound component based on the CSI intensity and concentration.

[0010] In some exemplary embodiments, the test sample and the control sample are processed separately to obtain NMR samples, including: pre-treating the test sample and the control sample separately; preparing phosphate buffer; adding the phosphate buffer to the pre-treated test sample and the pre-treated control sample respectively, and mixing them evenly to obtain a mixture of the test sample and a mixture of the control sample; and transferring the mixture of the test sample and the mixture of the control sample to a standard NMR tube respectively.

[0011] In some exemplary embodiments, the method further includes preprocessing the nuclear magnetic resonance spectrum before performing multivariate statistical analysis on the nuclear magnetic resonance spectrum.

[0012] Secondly, embodiments of this application also provide a diagnostic biomarker for evaluating the effectiveness of autism intervention, obtained by implementing the above-described identification method for evaluating the effectiveness of autism intervention.

[0013] Thirdly, embodiments of this application also provide the application of the above-mentioned diagnostic biomarkers for evaluating the effectiveness of autism interventions in the preparation of an assessment kit for evaluating the effectiveness of autism interventions.

[0014] The technical solution provided in this application has at least the following advantages:

[0015] This application provides a method for evaluating the effectiveness of autism intervention and its application. The method includes: collecting a test sample and a control sample; the test sample is a urine sample from an autism patient, and the control sample is a urine sample from a healthy volunteer; processing the test sample and the control sample separately to obtain NMR samples; testing the NMR samples using an NMR spectrometer to obtain NMR spectra; performing multivariate statistical analysis on the NMR spectra to construct a urinary metabolite model; using the urinary metabolite model to screen autism patients and evaluate the effectiveness of autism intervention. This application provides a method for evaluating the effectiveness of autism intervention by constructing a urinary metabolite model using NMR metabolomics technology to obtain differentially expressed metabolic sites, providing practical evidence for establishing methods for early screening and treatment process evaluation of autism.

[0016] The purpose of this application is to provide a time-efficient and highly sensitive non-invasive method for screening and intervening in ASD. By using nuclear magnetic resonance metabolomics technology to perform relative quantification of urinary metabolites in ASD patients before, during, and after drama therapy, as well as multivariate statistical analysis of urinary metabolites in healthy volunteers, differential metabolites can be quickly screened out, thereby achieving early screening of autism and evaluation of treatment efficacy. Attached Figure Description

[0017] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments, and unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0018] Figure 1 This is a flowchart illustrating an identification method for evaluating the effectiveness of autism interventions and its application, provided as an embodiment of this application.

[0019] Figure 2 This is a flowchart illustrating an identification method for evaluating the effectiveness of autism interventions and its application, provided as another embodiment of this application.

[0020] Figure 3The image shows the OPLS-DA analysis of the control sample and the test sample before treatment, as provided in an embodiment of this application.

[0021] Figure 4 The OPLS-DA replacement test diagrams of the control sample and the test sample before treatment are provided in an embodiment of this application.

[0022] Figure 5 This is an S-plot analysis of the control sample and the test sample before treatment, provided in an embodiment of this application.

[0023] Figure 6 An OPLS-DA analysis diagram of urine samples from an ASD patient before and after drama therapy, provided as an embodiment of this application.

[0024] Figure 7 OPLS-DA replacement test diagram of urine samples before and after drama therapy in the ASD group provided in an embodiment of this application.

[0025] Figure 8 An S-plot analysis of urine samples from the ASD group before and after drama therapy, provided in an embodiment of this application.

[0026] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] As the background information shows, current methods for assessing the effectiveness of autism interventions rely heavily on clinicians' experience and autism behavior scales for the early diagnosis of ASD patients. This often leads to subjective judgment biases and delays in early intervention in some cases. In addition, the evaluation methods for the effectiveness of early interventions are time-consuming, which reduces treatment adherence for some patients.

[0028] Numerous studies have confirmed that autism is not related to parenting style or economic status, but rather that biological factors play a crucial role in its development. Some metabolomics studies have shown elevated levels of biomarkers in the blood and urine samples of ASD patients, including pyruvate, serotonin, lactate, and mitochondrial-associated enzymes. Metabolomics is a research method that quantitatively analyzes all metabolites in an organism and seeks the relative relationships between metabolites and physiological and pathological changes. It has potential applications in identifying autism and its physiological and potential biochemical pathways during intervention. The main analytical techniques in metabolomics include nuclear magnetic resonance spectroscopy (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS). 1H-NMR offers advantages such as simple and non-destructive sample pretreatment, comprehensive detection of metabolites, and high reproducibility. Over the past few decades, humoral metabolomics based on nuclear magnetic resonance spectroscopy has significantly influenced the discovery of biomarkers for disease states. Urine, as a non-invasive collected bodily fluid, has advantages for long-term disease monitoring and provides a large number of recognized biomarkers for systemic diseases.

[0029] The term "drama therapy" has been coined in some technologies, and it has been presented as a new model of psychotherapy. Furthermore, the first theoretical system of drama therapy has been established. As an emerging intervention for children with ASD that combines performance, music, dance, and drama, drama therapy immerses participants in the storyline and characters, thereby promoting self-awareness. Research shows that the human brain has plasticity. During drama therapy, the mind and body of children with ASD can be fully mobilized, mirror neurons are activated, which helps improve their language communication, behavioral control, and social interaction abilities.

[0030] To address the problems existing in current methods for evaluating the effectiveness of autism interventions, this application provides an identification method for evaluating the effectiveness of autism interventions and its application. The method includes: collecting a test sample and a control sample; the test sample is a urine sample from an autism patient, and the control sample is a urine sample from a healthy volunteer; processing the test sample and the control sample respectively to obtain nuclear magnetic resonance (NMR) samples; testing the NMR samples using an NMR spectrometer to obtain NMR spectra; performing multivariate statistical analysis on the NMR spectra to construct a urinary metabolite model; and using the urinary metabolite model to screen autism patients and evaluate the effectiveness of autism interventions.

[0031] The test samples in this application are urine samples from children with autism, and the control samples are urine samples from healthy children. On one hand, this application's embodiments target children with ASD receiving drama therapy, collecting urine samples multiple times before, during, and after treatment. Based on nuclear magnetic resonance metabolomics technology, the metabolites in the urine are relatively quantified. By comparing the urine metabolites with those of healthy children, differential metabolites between children with ASD and healthy children are quickly identified, thus enabling early screening for ASD. On the other hand, this application's embodiments assess the treatment efficacy by observing the changing trends of differential metabolites in children with ASD before, during, and after treatment. The identification method provided in this application for evaluating the effectiveness of autism intervention is time-efficient, highly sensitive, and non-invasive, beneficial for early screening of autism and providing a scientific basis for evaluating treatment efficacy.

[0032] The embodiments of this application will now be described in detail with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the embodiments of this application to facilitate a better understanding of the application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0033] See Figure 1 This application provides an identification method for evaluating the effectiveness of autism intervention and its application, comprising the following steps:

[0034] Step S1: Collect the test sample and the control sample; the test sample is a urine sample from autistic patients, and the control sample is a urine sample from healthy volunteers.

[0035] Step S2: Process the test sample and the control sample respectively to obtain NMR samples.

[0036] Step S3: Use a nuclear magnetic resonance spectrometer to test the nuclear magnetic resonance sample and obtain the nuclear magnetic resonance spectrum.

[0037] Step S4: Perform multivariate statistical analysis on nuclear magnetic resonance spectra to construct a urine metabolite model.

[0038] Step S5: Use a urine metabolite model to screen patients with autism and evaluate the effectiveness of autism intervention.

[0039] Specifically, step S1 mainly involves sample collection. The samples include test samples and control samples; this application primarily uses children with autism and healthy children as comparison subjects to evaluate the effectiveness of autism intervention. Test samples are urine samples from children with autism (Urine samples from children with ASD), and control samples are urine samples from healthy children. Therefore, the samples can be divided into an ASD group and a healthy control group. Children with ASD are defined as having an Autism Scale (ABC) score ≥67 points and being diagnosed by a neurologist according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria; other neurological, cardiovascular, and inherited metabolic diseases have been excluded. Children with ASD receive drama therapy intervention from a professional drama therapy team, and urine samples are collected from children with autism before and after each drama therapy session. Samples for the healthy control group are obtained by randomly selecting urine samples from healthy children.

[0040] Step S2 mainly involves processing the test sample and the control sample separately to prepare NMR samples. Then, step S3 is performed, which mainly...1 H-NMR analysis was performed. Specifically, a Bruker 600MHz nuclear magnetic resonance spectrometer equipped with a 5 mm cryogenic probe (Cryo-Probe) was used to obtain nuclear magnetic resonance spectra using a noesypr1d sequence presaturated water signal.

[0041] In some embodiments, step S4 performs multivariate statistical analysis on the nuclear magnetic resonance spectra to construct a urine metabolite model, including: performing cluster analysis on the nuclear magnetic resonance spectra; comparing the nuclear magnetic resonance spectra of the test sample and the control sample, performing orthogonal partial least squares method for discriminant analysis, and constructing a urine metabolite model.

[0042] Specifically, before performing multivariate statistical analysis on the nuclear magnetic resonance spectrum in step S4, the identification method for evaluating the intervention effect of autism provided in this application embodiment further includes: nuclear magnetic resonance spectrum preprocessing.

[0043] The steps for preprocessing nuclear magnetic resonance spectra include: processing the nuclear magnetic resonance spectra (NMR spectra) 1 The NMR spectrum was uniformly corrected, phase adjusted, and Fourier transformed. Then, the DSS chemical shift was set to 0.00 ppm, and the signals at -2.210~-0.040 ppm, 4.60~5.30 ppm, and 10.00~11.80 ppm were set as shearing segments. The NMR spectrum was segmented and integrated in segments of 0.04 ppm. The spectrum was normalized with the total area as 1. Finally, the file was saved as an NMR CSV file (*.csv and *.txt).

[0044] After preprocessing the nuclear magnetic resonance spectra, step S4 is performed to conduct multivariate statistical analysis on the nuclear magnetic resonance spectra and construct a urine metabolite model.

[0045] Multivariate statistical analysis reduces the impact of noise or high variability of variables by dividing each variable by the square root of its standard deviation or by using the unit variance (UV) scale, Pareto scale (PAR), or logarithmic transformation. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to perform cluster analysis on the aforementioned NMR data. After comparing the metabolic profile data of the two groups (ASD group and healthy control group) using OPLS-DA, the VIP values ​​of differentially metabolizing sites were obtained to identify these sites. Chenomx was then used to identify the substances at these differentially metabolizing sites.

[0046] In some embodiments, cluster analysis includes principal component analysis and orthogonal partial least squares discriminant analysis; principal component analysis is used to analyze the nuclear magnetic resonance spectra, including the following steps: organizing the nuclear magnetic resonance spectrum data into a format that can be directly imported into SIMCA-P software for analysis, obtaining a dataset; importing the dataset into SIMCA-P software; selecting to build a PCA-X model in modeltype, setting data grouping according to the experimental grouping of the test samples and the control samples, and selecting UV scaling mode; automatically fitting the model, viewing the score plot and loading plot; and using permutation analysis to verify whether the model is overfitting.

[0047] In some embodiments, the orthogonal partial least squares method is used to discriminate and analyze the nuclear magnetic resonance spectra, including the following steps: organizing the nuclear magnetic resonance spectrum data into a format that can be directly imported into SIMCA-P software for analysis, obtaining a dataset; importing the dataset into SIMCA-P software; selecting to build an OPLS-DA model in the modeltype, setting data grouping according to the experimental grouping of the test sample and the control sample, and selecting UV as the scaling mode; automatically fitting the model, viewing the score plot and loading plot; and using permutation analysis to verify whether the model is overfitted.

[0048] It should be noted that the difference between using principal component analysis and orthogonal partial least squares discriminant analysis lies in the fact that the urine metabolite model constructed in the modeltype of principal component analysis is the PCA-X model; while the urine metabolite model constructed in the modeltype of orthogonal partial least squares discriminant analysis is the OPLS-DA model.

[0049] In some embodiments, step S5 uses a urine metabolite model to screen autism patients and evaluate the effectiveness of autism intervention, including: obtaining differentially expressed metabolic sites based on the urine metabolite model; determining whether the VIP value of the differentially expressed metabolic sites is greater than 1; if so, identifying and quantifying the differentially expressed metabolic sites using a targeted profile analysis quantitative module; if not, no processing is performed.

[0050] In some embodiments, the targeted profile analysis quantitative module is used to identify and quantify differentially metabolizing sites, including: the targeted profile analysis quantitative module performs deconvolution and multivariate linear fitting on the differentially metabolizing sites, identifies and quantifies the nuclear magnetic resonance curve of the target compound from the nuclear magnetic resonance spectrum, and calculates the content of the target compound component based on the CSI intensity and concentration.

[0051] Specifically, the 1H NMR spectrum undergoes standardized phase adjustment, baseline correction, and line shape matching using the processor module in Chenomx NMR Suite to ensure the accuracy and precision of the profiling analysis results. A chemical shape indicator (CSI) is used as the DSS to perform line shape matching on the spectrum, overcoming line shape variations caused by changes in acquisition conditions such as shimming files and temperature. The targeted profiling analysis and quantification Profiler module in Chenomx NMR Suite is then used to identify and quantify the differentially metabolized sites obtained in step S5. This module automatically performs deconvolution and multivariate linear fitting to identify and quantify the NMR curves of target compounds from the mixed 1H NMR spectra.

[0052] In some embodiments, step S2 involves processing the test sample and the control sample to obtain NMR samples, including: pre-treating the test sample and the control sample respectively; preparing phosphate buffer; adding the phosphate buffer to the pre-treated test sample and the pre-treated control sample respectively, and mixing them evenly to obtain a mixture of the test sample and a mixture of the control sample; and transferring the mixture of the test sample and the mixture of the control sample to a standard NMR tube respectively.

[0053] In addition, embodiments of this application also provide a diagnostic biomarker for evaluating the effectiveness of autism intervention obtained by screening the identification method for evaluating the effectiveness of autism intervention implemented in any of the above embodiments.

[0054] In addition, this application also provides the application of the above-mentioned diagnostic biomarkers for evaluating the effectiveness of autism intervention in the preparation of an assessment kit for evaluating the effectiveness of autism intervention.

[0055] See Figure 2 The following is a detailed explanation of the identification method for evaluating the effectiveness of autism intervention according to this application, using a specific embodiment as a comparison between autistic children and healthy children. The method includes the following steps:

[0056] Step S101: Collect the test sample and the control sample.

[0057] Urine samples were collected and analyzed from 18 children with ASD and 24 healthy children.

[0058] The ASD group consisted of 18 children aged 5-8 years with ASD, who scored ≥67 on the Autism Scale (ABC) and were diagnosed by a neurologist according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria; other neurological, cardiovascular, and inherited metabolic disorders were excluded. The 18 children received a 3-month drama therapy intervention from a professional drama therapy team, with an intervention frequency of 2 hours / session, 12 sessions / child. Urine samples were collected from the children before and after each drama therapy session.

[0059] Healthy control group: Urine samples were randomly selected from 24 healthy children (12 boys and 12 girls) aged 5 to 9 years.

[0060] Next, step S102 is performed to preprocess the test sample and the control sample respectively.

[0061] Specifically, the sample pretreatment includes the following steps: Quickly transfer 1.5 mL of the collected urine (test sample and control sample) into an EP tube using a pipette, incubate at 4°C for 10 min, then centrifuge at 4°C, 10000 rpm for 5 min; after centrifugation, take 800 µL of the supernatant and add 20 µL of sodium azide solution (0.02%, m / V), a microbial inhibitor; number the pretreated test samples and control samples respectively, and record the sampling information of the clinical samples; quickly place the pretreated test samples and control samples in a -80°C freezer for storage until the unified experiment.

[0062] It should be noted that the pretreatment steps for urine samples (test samples and control samples), mixing phosphate buffer, and preparing NMR samples are the same for both test samples and control samples.

[0063] After sample pretreatment, step S103 was performed to prepare phosphate buffer (working buffer). Specifically, Na₂HPO₄·H₂O was dissolved in D₂O to prepare 0.5M Na₂HPO₄ (solution A); NaH₂PO₄·H₂O was dissolved in D₂O to prepare 0.5M NaH₂PO₄ solution (solution B); DSS (218.32 g / mol) was dissolved in D₂O to prepare 25 mM DSS stock solution, which was stored at 4°C protected from light. For the experiment, 405 µL of solution A, 95 µL of solution B, and 500 µL of D₂O were mixed, and the pH was adjusted to 7.4 with solution B. Then, 250 µL of the DSS stock solution was added to prepare the working buffer (final concentration of phosphate buffer 0.2 M, final concentration of DSS 5 mM).

[0064] Next, proceed to step S104 to prepare the NMR sample. Take out the urine sample, let it stand for 20 min to thaw, then centrifuge at 4 ℃, 1500 rpm for 5 min and collect the supernatant; take a new EP tube, add 50 µL of working buffer and 450 µL of urine, and mix well; transfer the mixture to a 5 mm standard NMR tube.

[0065] Step S105: Test the NMR sample to obtain the NMR spectrum.

[0066] Step S105 mainly involves 1 H-NMR analysis was performed using a Bruker 600MHz NMR spectrometer equipped with a 5mm cryogenic probe (Cryo-Probe). The noesypr1d sequence was used to detect presaturated water signals. The observation frequency was 600.13 MHz, the spectral width (SW) was δ 14.0, the transmitter frequency offset position (O1P) was δ 4.710, the acquisition time (AQ) was 2.654 s, the relaxation time (D1) was 2.0 s, the time domain (TD) was 65536, the number of empty scans (DS) was 2, the number of scans (NS) was 64, the receiver gain (RG) was 32, the temperature (TE) was 298 K, and the linewidth factor (LB) was 0.3 Hz. The NMR spectra of the samples were obtained by testing the samples according to these parameters.

[0067] Step S106: Preprocess the nuclear magnetic resonance spectrum.

[0068] Specifically, using TOPSPIN 3.5 (Bruker Biospin, Germany) to... 1 The 1H NMR spectra underwent uniform baseline correction, phase adjustment, and Fourier transform, and were then imported into MestReNova (Mestrelab Research, Santiago de Compostella, Spain) for further processing to digitize the spectral data. The DSS chemical shift was set to 0.00 ppm, and the signals at -2.210 to -0.040 ppm, 4.60 to 5.30 ppm, and 10.00 to 11.80 ppm were defined as sheared regions. The NMR spectra were segmented and integrated in 0.04 ppm increments, and the total area was normalized to 1. Finally, the data was saved as NMR CSV files (*.csv and *.txt).

[0069] Step S107: Perform multivariate statistical analysis on the pretreated nuclear magnetic resonance spectra to construct a urine metabolite model.

[0070] Multivariate statistical analysis was performed using SIMCA (Umetrics, Umea, Sweden). The influence of noise or high variability was reduced by dividing each variable by the square root of its standard deviation or by logarithmic transformation using the unit variance (UV) scale, Pareto scale (PAR). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to perform cluster analysis on the NMR data. VIP values ​​of differentially metabolizing sites were obtained by comparing the metabolic profiles of two groups using OPLS-DA, and then Chenomx was used to identify the substances at these sites. The steps for exploratory principal component analysis (PCA) were as follows: the data was formatted for direct import into SIMCA-P software; the dataset was imported into SIMCA-P; a urinary metabolite model (PCA-X model) was selected in the modeltype, and data grouping was set according to experimental groups, with UV scaling mode selected; the model was automatically fitted, and the score plot and loading plot were viewed; permutation analysis was used to verify whether the model was overfitted.

[0071] The steps for performing Orthogonal Least Squares (OPLS-DA) are as follows: Organize the data into a format that can be directly imported into SIMCA-P software for analysis; import the dataset into SIMCA-P; select the urine metabolite model (OPLS-DA model) in the modeltype, and set the data grouping according to the experimental grouping, selecting UV as the scaling mode; automatically fit the model, and view the score plot and loading plot; use Permutation analysis to verify whether the model is overfitted.

[0072] Step S108: Based on the urine metabolite model, differential metabolic sites are obtained and identified and analyzed.

[0073] It should be noted that the aforementioned differential metabolic sites can also be understood as differential metabolites. Therefore, step S108 is based on a urinary metabolite model to obtain differential metabolites and to identify and analyze them.

[0074] Specifically, the 1H NMR spectrum undergoes standardized phase adjustment, baseline correction, and line shape matching via the processor module in Chenomx NMR Suite to ensure accurate and precise profiling analysis. A chemical shape indicator (CSI) is used as the DSS to match the spectrum, overcoming line shape variations caused by changes in acquisition conditions such as shimming and temperature. The targeted profiling analysis and quantification Profiler module in Chenomx NMR Suite identifies and quantifies differentially expressed metabolic sites with VIP values ​​>1 obtained in section 7). This module automatically performs deconvolution and multivariate linear fitting to identify and quantify the NMR curves of target compounds from the mixed 1H NMR spectrum, and calculates the content of these components based on CSI intensity and concentration.

[0075] The identification method for evaluating the effectiveness of autism intervention in the above embodiments first establishes a comparison model between the healthy control group and the ASD group using the orthogonal least squares (OPLS-DA) method. This model combines partial least squares discriminant analysis (PLS-DA) with orthogonal signal correction (OSC), using known grouping information to correlate X data (variables) with known information Y data (groups), maximizing the distinction between groups to identify differentially expressed metabolic sites. R²X represents the explanatory power along the X-axis. Typically, an R²X value > 0.5. The closer R²Y and Q² are to 1 and the smaller the difference, the better the model's predictive ability; conversely, a smaller difference indicates a poorer predictive ability.

[0076] Figures 3-8 The experimental results of the above embodiments are shown. Specifically, Figure 3 The OPLS-DA analysis plots of urine samples from the healthy control group and the ASD group before treatment are shown. Figure 4 The OPLS-DA replacement test plots of urine samples from the healthy control group and the ASD group before treatment are shown. Figure 5 The S-plot analysis of urine samples from the healthy control group and the ASD group before treatment is shown; Figure 6 The OPLS-DA analysis graphs of urine samples from the ASD group before and after drama therapy are shown. Figure 7 The OPLS-DA substitution test plots of urine samples before and after drama therapy in the ASD group are shown. Figure 8 The S-plot analysis of urine samples before and after drama therapy in the ASD group is shown.

[0077] like Figure 3 As shown, in Figure 3In the OPLS-DA model, the ASD group (left dot) and the Control group (right dot) show significant separation between groups and relatively small dispersion within groups. The score plot parameters R2X=0.681, R2Y=0.991, Q2=0.926, indicating that the model has good fitting and prediction capabilities.

[0078] Figure 4 The permutation test shown is used to evaluate the quality of the OPLS-DA model. The R2 intercept is 0.65 and the Q2 intercept is -0.908, which meets the criteria of R2<1 and Q2<0.5, indicating that the model is not overfitting and is statistically effective.

[0079] Next, S-plot analysis was performed on the above model. The score S-plot was used to identify the variables that significantly contributed to the classification. Each point in the plot represents... 1 A segment of the 1H-NMR spectrum (0.04 ppm) is used in this plot. The importance in projection (VIP) score (the weighted value of the variable in the response model) is used to determine whether the 1H NMR signal segment is a potential differential variable. Covariance (p) represents the contribution of the observed variance and is plotted on the x-axis of the S-Plot. Correlation (p(corr)) represents the correlation between samples and the reliability of the results and is plotted on the y-axis of the S-Plot. UV (VIP>1) is used to identify potentially differential metabolite variables using the software's built-in method. The results are shown below. Figure 5 As shown.

[0080] Figure 5 In the score plot S-plot shown, the black dots represent potentially differential variables located at the top of the intervals. These sites meet the screening criteria of FC > 1.5 or FC < 0.67, and p < 0.05.

[0081] Furthermore, by analyzing the NMR spectra corresponding to the aforementioned differential sites, and by comprehensively analyzing the peak shape, chemical shift, and coupling constant of all hydrogen proton signals of each compound, the differential metabolites in the pre-treatment urine samples of the healthy control (Control) group and the ASD group were identified, as shown in Table 1.

[0082] Table 1. Differential metabolites in urine samples from the healthy control group and the ASD group before treatment.

[0083]

[0084] Table 1 shows that the concentrations of the eight components differed significantly between the two sample groups, with fold changes (FC) all >1.5 or <0.67, and the differences were statistically significant (p<0.05). This indicates that this method can clearly distinguish children with ASD from healthy control children. Furthermore, an orthogonal least squares-DA (OPLS-DA) model was established to compare the ASD group before and after drama therapy.

[0085] Figure 6 In the OPLS-DA model shown, the ASD group before treatment (gray dot on the left) and the ASD group after treatment (black hexagonal dot on the right) show significant separation between the groups and relatively small dispersion within the groups. The score plot parameters R2X=0.641, R2Y=0.795, and Q2=0.534, indicating that the model has good fitting and predictive capabilities.

[0086] Figure 7 The permutation test shown is used to evaluate the quality of the OPLS-DA model. The R² intercept is 0.686 and the Q² intercept is -0.405, which meets the criteria of R² < 1 and Q² < 0.5, indicating that the model is not overfitting and is statistically effective.

[0087] Next, S-plot analysis was performed on the above model. The score S-plot was used to identify the variables that significantly contributed to the classification. Each point in the plot represents... 1 A segment of the 1H-NMR spectrum (0.04 ppm) is used in this plot. The Importance in Projection (VIP) score (the weight of variables in the response model) is used to determine whether the 1H NMR signal segment represents a potential differential variable. Covariance (p) represents the contribution of the observed variance and is plotted on the x-axis of the S-Plot. Correlation (p(corr)) represents the correlation between samples and the reliability of the results and is plotted on the y-axis of the S-Plot. UV (VIP>1) is used to identify potentially differential metabolite variables using the software's built-in method. The results are shown below. Figure 8 As shown.

[0088] Figure 8 In the score plot S-plot shown, the black dots represent potentially differential variables located at the top of the intervals. These sites meet the screening criteria of FC > 1.5 or FC < 0.67, and p < 0.05.

[0089] Furthermore, by analyzing the NMR spectra corresponding to the aforementioned differential sites, and by comprehensively analyzing the peak shape, chemical shift, and coupling constant of all hydrogen proton signals of each compound, the differential metabolites in the pre-treatment urine samples of the healthy control (Control) group and the ASD group were identified, as shown in Table 2.

[0090] Table 2. Differential metabolites obtained from urinary metabolomics studies before and after drama therapy for ASD.

[0091]

[0092] Table 2 shows that the concentrations of the nine components differed significantly between the two sample groups, with fold changes (FC) all <0.67 and p <0.05. This indicates that the identification method provided in this application for evaluating the effectiveness of autism intervention can clearly distinguish urine samples before and after ASD drama therapy.

[0093] This application embodiment is passed through 1 Using 1H-NMR metabolomics, urinary metabolite models were established between children with autism and healthy controls, yielding eight differentially expressed metabolites. Urinary metabolite models were also established before and after drama therapy in children with autism, yielding nine differentially expressed metabolites. This method is convenient, rapid, and non-invasive, and the biochemical reactions of these specific metabolites in vivo can be used to infer possible mechanisms of the disease and treatment process. This invention provides practical evidence for establishing methods for early screening and treatment process assessment of autism.

[0094] The purpose of this application is to provide a time-efficient and highly sensitive non-invasive method for screening and intervening in children with ASD. By using nuclear magnetic resonance metabolomics technology to perform relative quantification of urinary metabolites in children with ASD before, during, and after drama therapy, as well as multivariate statistical analysis of urinary metabolites with those of healthy children, differential metabolites can be quickly screened out, thereby achieving early screening for autism and evaluation of treatment efficacy.

[0095] Based on the above technical solutions, embodiments of this application provide a method for evaluating the effectiveness of autism intervention and its application. The method includes: collecting a test sample and a control sample; the test sample is a urine sample from an autism patient, and the control sample is a urine sample from a healthy volunteer; processing the test sample and the control sample separately to obtain NMR samples; testing the NMR samples using an NMR spectrometer to obtain NMR spectra; performing multivariate statistical analysis on the NMR spectra to construct a urine metabolite model; and using the urine metabolite model to screen autism patients and evaluate the effectiveness of autism intervention. Embodiments of this application provide a method for evaluating the effectiveness of autism intervention and its application. By using NMR metabolomics technology to construct a urine metabolite model and obtain differentially expressed metabolic sites, it provides practical evidence for establishing methods for early screening and evaluation of autism treatment.

[0096] refer to Figure 9Another embodiment of this application relates to an electronic device, which includes at least one processor 101; and a memory 102 communicatively connected to at least one processor 101; wherein the memory 102 stores instructions executable by at least one processor 101, the instructions being executed by at least one processor 101 to enable at least one processor 101 to perform any of the above-described method embodiments.

[0097] The memory 102 and processor 101 are connected via a bus, which may include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 101 and memory 102 together. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 101 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 101.

[0098] Processor 101 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 102 can be used to store data used by processor 101 during operation.

[0099] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0100] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0101] Those skilled in the art will understand that the above-described embodiments are specific examples of implementing this application, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of this application. Any person skilled in the art can make their own modifications and alterations without departing from the spirit and scope of this application; therefore, the scope of protection of this application should be determined by the scope defined in the claims.

Claims

1. A method for evaluating the effectiveness of autism interventions, characterized in that, include: Collect test samples and control samples; the test samples are urine samples from children with autism, and the control samples are urine samples from healthy children; The test sample and the control sample were processed respectively to obtain NMR samples; The nuclear magnetic resonance sample was tested using a nuclear magnetic resonance spectrometer to obtain the nuclear magnetic resonance spectrum; Multivariate statistical analysis was performed on the nuclear magnetic resonance spectra to construct a urine metabolite model; The urine metabolite model was used to screen children with autism and to evaluate the effectiveness of autism interventions. The nuclear magnetic resonance (NMR) sample is tested using a nuclear magnetic resonance (NMR) spectrometer to obtain an NMR spectrum, including: The nuclear magnetic sample was tested according to the following parameters to obtain 1 H nuclear magnetic resonance spectrum; parameters: observation frequency 600.13 MHz, spectral width δ 14.0, sampling center point δ 4.710, sampling time 2.654 s, relaxation time 2.0 s, time domain 65536, scan number 64, gain value 32, temperature 298 K, line width factor 0.3 Hz; The urinary metabolite model is used to screen children with autism and evaluate the effectiveness of autism interventions, including: Based on the urine metabolite model, differential metabolic sites were obtained; Determine whether the VIP value of the differentially metabolizing site is greater than 1; If so, the differentially metabolized sites are identified and quantified using a targeted profiling analysis quantitative module; If not, no action will be taken; The urine metabolite models include urine metabolite models of autistic children before treatment and healthy children, as well as urine metabolite models of autistic children before and after treatment. Based on the urine metabolite models of children with autism before treatment and healthy children, the urine metabolites of children with autism before treatment and healthy children are compared to screen out the differential metabolic sites between children with autism and healthy children, so as to conduct early screening for children with autism. Based on a urinary metabolite model of children with autism before and after treatment, the efficacy of treatment was assessed by the changing trends of differential metabolic sites before and after treatment.

2. The identification method for evaluating the effectiveness of autism intervention according to claim 1, characterized in that, Multivariate statistical analysis was performed on the nuclear magnetic resonance spectra to construct a urinary metabolite model, including: Cluster analysis was performed on the nuclear magnetic resonance spectra; The nuclear magnetic resonance spectra of the test sample and the control sample were compared, and orthogonal partial least squares method was used for discriminant analysis to construct a urine metabolite model.

3. The identification method for evaluating the effectiveness of autism intervention according to claim 2, characterized in that, The cluster analysis includes principal component analysis and orthogonal partial least squares method for discriminant analysis; The nuclear magnetic resonance spectrum was analyzed using principal component analysis, including the following steps: The nuclear magnetic resonance spectroscopy data were organized into a format that could be directly imported into SIMCA-P software for analysis, resulting in a dataset. Import the dataset into the SIMCA-P software; In the model type, select to build a PCA-X model, set the data grouping according to the experimental grouping of the test sample and the control sample, and select the scaling mode as UV; Automatically fit the model and view the score and load plots; Permutation analysis was used to verify whether the model was overfitting.

4. The identification method for evaluating the effectiveness of autism intervention according to claim 3, characterized in that, The nuclear magnetic resonance spectrum is analyzed using the orthogonal partial least squares method, including the following steps: The nuclear magnetic resonance spectroscopy data were organized into a format that could be directly imported into SIMCA-P software for analysis, resulting in a dataset. Import the dataset into the SIMCA-P software; In the model type, select to build an OPLS-DA model, set the data grouping according to the experimental grouping of the test sample and the control sample, and select the scaling mode as UV; Automatically fit the model and view the score and load plots; Permutation analysis was used to verify whether the model was overfitting.

5. The identification method for evaluating the effectiveness of autism intervention according to claim 1, characterized in that, The identification and quantification of differentially metabolized sites using a targeted profiling analysis and quantification module includes: The targeted profile analysis and quantitative module performs deconvolution and multivariate linear fitting on the differentially metabolized sites, identifies and quantifies the NMR curve of the target compound from the NMR spectrum, and calculates the content of the target compound component based on the CSI intensity and concentration.

6. The identification method for evaluating the effectiveness of autism intervention according to claim 1, characterized in that, The process of processing the test sample and the control sample respectively to obtain the NMR sample includes: The test sample and the control sample were preprocessed respectively. Prepare phosphate buffer; The phosphate buffer solution was added to the pretreated test sample and the pretreated control sample, respectively, and mixed thoroughly to obtain a mixture of the test sample and a mixture of the control sample. The mixture of the test sample and the mixture of the control sample were respectively transferred into a standard nuclear magnetic resonance tube.

7. The identification method for evaluating the effectiveness of autism intervention according to claim 1, characterized in that, Before performing multivariate statistical analysis on the nuclear magnetic resonance spectrum, the method further includes: preprocessing the nuclear magnetic resonance spectrum.

Images