Method for screening and detecting endocrine disrupter-induced neurotoxicity biomarker

Abstract

The invention belongs to the technical field of neurotoxicology and multi-omics detection, and particularly relates to an endocrine disrupter induced neurotoxicity biomarker screening and detection method which comprises the following steps: (1) establishing a neurotoxicity animal model; (2) verifying the neurotoxicity model; (3) performing brain tissue transcriptome sequencing and neural pathway analysis and / or performing brain tissue metabolome sequencing and differential metabolite analysis; (4) performing machine learning screening on the biomarkers; (5) carrying out interpretability analysis; (6) constructing a structural equation model; and (7) calculating a neurotoxicity comprehensive index NDTI, and carrying out significance detection and toxicity grade determination. By integrating behavior-neurotransmitter-hormone-inflammation-transcriptome / metabolome and other multi-level data, a reproducible neurotoxicity index is established, and graded quantitative evaluation of neurodevelopment damage caused by different endocrine disruptors and screening verification of key biomarkers are achieved.

Description

Technical Field

[0001] This invention belongs to the field of neurotoxicology and multi-omics detection technology, specifically relating to a method for screening and detecting biomarkers of neurotoxicity induced by endocrine disruptors, which can be used for risk assessment of exposure to environmental pollutants and evaluation of the protective effects of candidate neuroprotective agents. Background Technology

[0002] Neurological disorders, including neurodevelopmental disorders, neurodegenerative diseases, and cognitive impairments, have become a significant public health burden globally. The occurrence and development of these diseases are influenced not only by genetic factors but also by environmental exposure. In recent years, endocrine-disrupting chemicals (EDCs), widely present in the environment, have been identified as key exogenous risk factors for neurotoxicity. These include flame retardants, plasticizers, pesticides, herbicides, and surfactants. They are prevalent in consumer and industrial products, highly stable, and easily accumulate in environmental media and organisms. Long-term exposure through the food chain or respiratory pathways can pose potential harm to the development and function of the nervous system.

[0003] Previous studies have shown that exposure to endocrine disruptors (EDCs) (such as BPA, DEHP, and methomyl) can cause disruptions in the hypothalamic-pituitary-gonadal (HPG) and hypothalamic-pituitary-thyroid (HPT) axes, thereby affecting neural stem cell proliferation and neural network formation. However, current neurotoxicity risk assessments mainly rely on behavioral observations and traditional biochemical indicators, lacking a unified molecular biomarker system and quantitative detection standards. Therefore, there is an urgent need to establish a multi-level, multi-indicator coupled neurotoxicity detection method that integrates gene transcription, neurotransmitter, and metabolic characteristics to achieve a quantifiable biomarker screening and validation system, providing a scientific basis for the neurotoxicological risk assessment of endocrine disruptors. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for screening and detecting biomarkers of neurotoxicity induced by endocrine disruptors. This invention, based on multi-omics data integration and statistical modeling, overcomes the limitations of existing neurotoxicity detection strategies that primarily rely on single behavioral, biochemical, or genetic indicators and lack systematic correlation and quantitative threshold determination. By integrating multi-level data from behavior, neurotransmitters, hormones, inflammation, and transcriptomics / metabolomics, this invention establishes a reproducible neurotoxicity diagnostic index (NDTI), enabling graded and quantitative assessment of neurodevelopmental damage induced by different endocrine disruptors (EDCs) and verification of key biomarkers.

[0005] To achieve the above technical objectives, the technical solution adopted in the embodiments of the present invention is as follows: A method for screening and detecting biomarkers of neurotoxicity induced by endocrine disruptors, comprising the following steps: (1) Establishment of neurotoxic animal model: Model animals were selected as experimental subjects, and control groups and drug exposure groups with different doses or durations were set up; (2) Validation of neurotoxicity model: The success of the neurotoxicity model was verified by animal movement behavior, neurotransmitter content in the brain, inflammatory factor level and transcription level of genes related to neural signaling pathways. The evaluation criteria were that there was a significant difference between the drug exposure group and the control group, P < 0.05. (3) Perform brain tissue transcriptome sequencing and neural pathway analysis and / or brain tissue metabolome sequencing and differential metabolite analysis; (4) Biomarker machine learning screening: The content of differentially expressed neurotransmitters and inflammatory factors, differentially expressed genes (DEGs) or differentially expressed metabolites (DEMs) are integrated into the initial screening feature set. The exposure dose or degree of behavioral damage is used as the classification label, and the Random Forest (RF) algorithm is used for feature screening and importance ranking. (5) Interpretability analysis: Real-time quantitative PCR (qRT-PCR) or targeted metabolomics are used to verify the expression stability and exposure response intensity of the core biomarkers to ensure the reproducibility and biological rationality of the screening results; (6) Constructing a structural equation model: The core indicators, including neurotransmitters, inflammatory factors and biomarkers, are homogenized and Z-score standardized, and a structural equation model (SEM) is fitted to calculate the standardized path coefficients and the direct, indirect and total effects. (7) Calculate the Neurotoxicity Index (NDTI) and perform significance testing and toxicity level determination.

[0006] Furthermore, in step (1), the model animal includes one of zebrafish, mouse and rat; The drugs administered to the exposure group were endocrine disruptors that are widely used in daily life or persistently present in the environment, and the exposure dose was controlled at a concentration (LC50) with no significant lethal effect. 10 -LC 20 Within the specified range, at least two dose gradients should be set up, with an exposure period of at least 28 days.

[0007] Further, in step (2), the animal movement behavior includes one or more of the following: average speed, average acceleration, total distance traveled, activity rate, preference behavior, and exploration index; The neurotransmitters in the brain include one or more of acetylcholine, dopamine, serotonin, and glutamate; The inflammatory factors include one or more of interleukins IL-1β, IL-4, IL-6, and tumor necrosis factor-α (TNFα).

[0008] Furthermore, in step (3), when performing brain tissue transcriptome sequencing and neural pathway analysis, brain tissues of animals in the control group and the exposed group were collected, total RNA was extracted, and transcriptome sequencing (RNA-seq) was used to analyze the expression changes of neural function-related genes. Using |log2FC| ≥ 1.0 and FDR < 0.05 as criteria, enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) was conducted to identify genes with significant changes in key pathways, including synaptic signaling, neurotransmitter synthesis, inflammatory signaling, and mitochondrial function.

[0009] Furthermore, in step (3), when performing brain tissue metabolome sequencing and differential metabolite analysis, animal brain tissues from the control group and the exposed group were collected, and metabolome analysis was performed by time-of-flight mass spectrometry (TOF-MS) after ultrasonic extraction with organic solvents. Using VIP > 1, FDR < 0.05 and FC > 1.5 as screening criteria, we focused on identifying differential metabolites related to neural development pathways. These differential metabolites included one or more of tryptophan metabolites, lipid oxidation products, and energy metabolism intermediates.

[0010] Furthermore, in step (4), the hyperparameters are optimized under the 5×5 nested cross-validation framework, and the mean decline accuracy is used as the basis for feature importance to select the top few key biomarkers.

[0011] Furthermore, in step (6), the model fitting indices require a goodness-of-fit index (GFI) > 0.90, a comparison fit index (CFI) > 0.90, a root mean square approximation error (RMSEA) < 0.08, and a root mean square standardized residual (SRMR) < 0.08.

[0012] Furthermore, in step (7), the calculation of the neurotoxicity composite index includes the following steps: a) Calculate the latent variable score using the standardized load λ from the SEM output: ; Among them, Z i x is the standardized score of the latent variable of the i-th module; ijLet λ be the standardized value of the j-th observed index of the i-th latent variable; ij For the corresponding load; b) Calculate the total effect τ (including direct and indirect paths) for each module and normalize it to the weight w. i : ; Among them, w i τ represents the normalized weight of the i-th module, reflecting its contribution to the overall neurotoxicity effect; τ represents the total effect of a single module, including the direct path effect from upstream to downstream indicators and all indirect path effects; T i This is the specific quantitative value of the total effect τ of the i-th module, that is, the quantitative result of the total effect from the upstream indicators to the downstream indicators of the i-th module, including direct and all indirect paths; c) Constructing the Neurotoxicity Comprehensive Index (NDTI) using a weighted summation: ; ; Among them, NDTI sample The Neurotoxicity Index (NDTI) is a comprehensive index for each independent sample, representing a quantitative result of the neurotoxicity level of a single sample. group Z is the comprehensive neurotoxicity index for each exposure group, representing the aggregated quantitative result of the overall neurotoxicity level of a specific exposure group; i,Group Z is the average standardized score of the i-th module in a certain exposure group, which is the sum of the scores of all samples in that exposure group. i The mean of Z; i,Con Z is the average standardized score of the i-th module in the control group, i.e., the score of all samples in the control group. i The mean.

[0013] Furthermore, in step (7), during the significance test and toxicity level determination, the NDTI... sample Perform ANOVA and Dunnett's post-hoc multiple comparisons, and base the results on the significance p-value. value Values ​​are used to classify toxicity levels, and NDTIs are reported simultaneously. group As a quantitative indicator of intensity: No significant neurotoxicity. Mild toxicity Moderate to mild toxicity Severe to mild toxicity.

[0014] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: This invention is primarily applicable to neurotoxicity models whose main mechanism is endocrine pathway disruption (abnormal signaling of thyroid hormones, estrogen, or glucocorticoids), including but not limited to damage caused by bisphenols, phthalates, pesticides, and persistent organic pollutants (POPs). This method innovatively integrates multi-omics analysis with structural equation modeling (SEM), enabling the determination of neurotoxicity intensity and grade under early, reproducible, and quantitative conditions, and outputting interpretable combinations of core biomarkers. It is reproducible, portable, and scalable.

[0015] The technical object of this invention is a neurotoxicity detection system based on multi-omics characteristics, which aims to achieve early identification, mechanism analysis and biomarker screening of neurotoxic effects caused by endocrine disruptors by integrating behavioral, biochemical indicators and molecular omics signals.

[0016] This method can be used not only to screen biomarkers for early neurological damage caused by exposure to endocrine disruptors, but also to quantitatively evaluate the protective effects of candidate neuroprotective agents (such as probiotics, antioxidants, and neurotrophic factors). The system is standardized, transferable to multiple platforms, and applicable to multiple species, and can serve fields such as environmental risk assessment, new chemical substance registration, and neurotoxic drug screening. Attached Figure Description

[0017] Figure 1 It is the structural equation model constructed in Embodiment 1 of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Example 1 A method for screening and detecting biomarkers of neurotoxicity induced by endocrine disruptors, comprising the following steps: (1) Establishment of an animal model of neurotoxicity: Healthy zebrafish (Danio rerio) were used as model animals to establish a neurotoxicity model using methomyl as a typical endocrine disruptor (EDC). The experiment was divided into three groups: control group (Ctrl), low-dose methomyl exposure group (Toxic - 0.05 mg / L), and high-dose exposure group (Toxic - 0.20 mg / L), with continuous exposure for 56 days. (2) Validation of neurotoxicity model: The successful establishment of the neurotoxicity model was verified by measuring motor behavior, neurotransmitter levels in the brain, inflammatory factor levels, and transcriptional levels of genes related to neural signaling pathways. Results showed that the average swimming speed, average acceleration, activity rate, and exploration index of zebrafish significantly decreased after methomyl exposure (P < 0.05). Significant changes were also observed in the levels of neurotransmitters in brain tissue, including acetylcholine, glutamate, serotonin, dopamine, and inflammatory factors (interleukins IL-1β, IL-4, IL-6, and TNFα) (P < 0.05). These results confirm the successful establishment of the neurotoxicity model. (3) Transcriptome sequencing and neural pathway analysis of brain tissue: Brain tissues were collected from animals in the control group, and the 0.05 mg / L and 0.20 mg / L exposure groups. Total RNA was extracted, and transcriptome sequencing (RNA-seq) was used to analyze the expression changes of genes related to neurological function. Differentially expressed genes were screened based on |log2FC| ≥ 1.0 and FDR < 0.05. The results showed that methomyl exposure significantly altered gene expression patterns in zebrafish brain tissue, identifying a total of 3414 differentially expressed genes. Compared with the control group, 1707 genes were downregulated and 301 genes were upregulated in the 0.05 mg / L exposure group; while in the 0.20 mg / L exposure group, 1772 genes were downregulated and 368 genes were upregulated.

[0020] (4) Biomarker machine learning screening: Differentially changing neurotransmitter levels, inflammatory cytokine levels, differentially expressed genes, or differentially expressed metabolites were integrated into a preliminary screening feature set. Exposure dose was used as the classification label, and a Random Forest (RF) algorithm was employed for feature selection and importance ranking. Hyperparameters (ntree = 1000) were optimized within a 5×5 nested cross-validation framework, and the mean decline accuracy was used as the criterion for feature importance, identifying the top 50 key differentially expressed genes. Correlation analysis was performed between these 50 key genes and neurotransmitter and inflammatory cytokine levels, ultimately identifying 3 neurotransmitters (5-HT, HVA, and ACh), 2 inflammatory cytokines (IL-1β and IL-4), and 5 genes (mapk13, arntl1a, guca1a, slc6a4b, and cacna1ab), totaling 10 core biomarkers. (5) Interpretability analysis: Real-time quantitative PCR (qRT-PCR) was used to verify that the expression of core gene markers was consistent with the sequencing results, ensuring the reproducibility and biological rationality of the screening results.

[0021] (6) Constructing a structural equation model: Core indicators, including neurotransmitters, inflammatory factors, and biomarkers, were homogenized and Z-score standardized. Based on the biological hierarchy of these biomarkers, a four-layer structural equation model of "exposure-inflammation-gene-neurotransmitter" was established. ; The standardized path coefficients and direct, indirect, and total effects were calculated, and the results are shown in [see figure]. Figure 1 As shown, the model fitting metrics all meet internationally accepted standards, indicating that the path relationship structure is reasonable and has high explanatory power. (7) Calculate the Neurotoxicity Index (NDTI) and perform significance testing and toxicity level determination, specifically including the following steps: a) Calculate the latent variable score Z using the normalized load λ from the SEM output. i : Based on confirmatory factor analysis (CFA), standardized factor loadings are output from the SEM model, and a standardized score is calculated for each latent variable (i.e., module).

[0022] ; Among them, Z i Let x be the standardized score of the latent variable of the i-th module. ij Let λ be the standardized value of the j-th observed indicator of the i-th latent variable. ij For the corresponding load; This can be achieved using the following R code: df_norm2$Z_Genes <- latent_score("Genes", L, df_norm) df_norm2$Z_Neuro <- latent_score("Neuro", L, df_norm) b) Total module effect T i Calculation: The direct and indirect path coefficients of each module to the neurotoxicity endpoint were estimated using a SEM model, and the total effect T was obtained. i : T i = D ti + I ti ; Among them, D ti -Direct effects, I ti -Indirect effects; For the gene "Genes" module T Genes= Exposure → Genes For the neurotransmitter "Neuro" module T Neuro = (Exposure → Neuro) + (Exposure →Genes → Neuro) This can be achieved using the following R code: total_EG <- beta_std["Genes_comp","Exposure"] total_EN <- beta_std["Neuro_comp","Exposure"] + beta_std["Neuro_comp","Genes_comp"] * total_EG c) Module weight w i Calculation: The absolute value of the total effect of each module is taken and normalized to obtain the weights that reflect the relative contribution of each module.

[0023] ; Among them, w i It is the normalized weight of the i-th module, reflecting the degree of contribution of this module to the overall neurotoxicity effect; τ represents the total effect of a single module, including the direct path effect and all indirect path effects from the upstream to the downstream indicators of that module. T i This is the specific quantitative value of the total effect τ of the i-th module, that is, the quantitative result of the total effect from the upstream indicators to the downstream indicators of the i-th module, including direct and all indirect paths; This can be achieved using the following R code: T_vec <- c(Genes = abs(total_EG), Neuro = abs(total_EN)) w_vec <- T_vec / sum(T_vec) d) Sample-level Neurotoxicity Composite Index (NDTI) Sample The neurotoxicity composite index for each sample is a weighted sum of the standardized scores of each latent variable.

[0024] ; Among them, NDTI sample The neurotoxicity index is a comprehensive index for each independent sample, which is a quantitative result of the neurotoxicity level of a single sample.

[0025] This can be achieved using the following R code: df_latent$NDTI_sample = w_vec["Genes"] * Z_Genes + w_vec["Neuro"] * Z_Neuro; e) Group-level Neurotoxicity Index (NDTI) Group Using the control group (Control, Con) as the baseline, calculate the absolute deviation of the experimental group relative to the control group.

[0026] ; Among them, NDTI group The neurotoxicity index for each exposure group is a quantitative summary of the overall neurotoxicity level of a particular exposure group. Z i,Group Z is the average standardized score of the i-th module in a certain exposure group, which is the sum of the scores of all samples in that exposure group. i The mean; Z i,Con Z is the average standardized score of the i-th module in the control group, i.e., the score of all samples in the control group. i The mean.

[0027] This can be achieved using the following R code: group_rel <- group_means %>% mutate(Genes_rel = abs(mean_Z_Genes - mZG), Neuro_rel = abs(mean_Z_Neuro - mZN)) %>% mutate(NDTI_group = w_vec["Genes"] * Genes_rel + w_vec["Neuro"] * Neuro_rel) Finally, the methomyl neurotoxicity index (NDTI) was calculated. group The concentrations were 7.31 (0.05 mg / L) and 8.02 (0.20 mg / L).

[0028] When conducting significance testing and determining toxicity levels, NDTI was used. sample Perform ANOVA and Dunnett's post-hoc multiple comparisons, and base the results on the significance p-value. value Values ​​are used to classify toxicity levels, and NDTIs are reported simultaneously. group As a quantitative indicator of intensity: No significant neurotoxicity. Mild toxicity Moderate to mild toxicity Severe to mild toxicity.

[0029] Analysis of variance (ANOVA) and Dunnett's multiple comparisons showed that the p-values ​​for both groups were less than 0.001, indicating that exposure to methomyl at concentrations of 0.05 mg / L and 0.20 mg / L could induce neurotoxic damage in zebrafish, and according to the grading criteria, both were classified as severe neurotoxicity.

[0030] This embodiment provides an operable and reproducible omics-based joint detection and toxicity quantitative assessment system. By screening key genes and metabolic signals using a random forest model and combining the quantitative module weights of a structural equation model, the established NDTI comprehensive index achieves a tiered, interpretable, and mechanistic integrated assessment of endocrine disruptor-induced neurotoxicity.

[0031] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A screening and detection method for biomarkers of neurotoxicity induced by endocrine disruptors, characterized in that, Comprise the following steps: (1) Establishment of a neurotoxicity animal model: select a model animal as the experimental object, set up a control group and different doses or time-course drug exposure groups; (2) Verification of the neurotoxicity model: whether the neurotoxicity model is successfully constructed is verified by animal movement behavior, brain neurotransmitter content, inflammatory factor level and transcription level of nerve signal pathway related genes, and the judgment basis is that there is a significant difference between the drug exposure group and the control group, P < 0.05; (3) Brain tissue transcriptome sequencing and neural pathway analysis and / or brain tissue metabolome sequencing and differential metabolite analysis; (4) Biomarker machine learning screening: the differentially changed neurotransmitters and inflammatory factor contents, differentially expressed genes or metabolome differential metabolites are integrated into the initial screening feature set, and the exposure dose or behavior damage degree is used as the classification label, and the random forest algorithm is used for feature screening and importance sorting; (5) Explainable analysis: the expression stability and exposure response intensity of the core markers are verified by real-time fluorescent quantitative PCR or targeted metabolomics to ensure the repeatability and biological rationality of the screening results; (6) Construction of structural equation model: the core indicators including neurotransmitters, inflammatory factors and biomarkers are homogenized and Z-score standardized, and a structural equation model is fitted to calculate the standardized path coefficients and direct, indirect and total effects; (7) Calculate the neurotoxicity comprehensive index NDTI, and perform significance test and toxicity grade determination.

2. The biomarker screening and detecting method for endocrine disruptor-induced neurotoxicity according to claim 1, wherein In step (1), the model animal includes one of zebrafish, mouse and rat; The drugs given to the exposure groups are selected from endocrine disruptors that are widely used in life or continuously exist in the environment, and the exposure dose is controlled within the range of no obvious lethal effect concentration LC 10 -LC 20 , and no less than two dose gradients are set, and the exposure period is not less than 28 days.

3. The biomarker screening and detecting method for endocrine disruptor-induced neurotoxicity according to claim 1, wherein In step (2), the animal movement behavior includes one or more of average speed, average acceleration, total moving distance, activity rate, preference behavior and exploration index; The brain neurotransmitter includes one or more of acetylcholine, dopamine, serotonin and glutamic acid; The inflammatory factor includes one or more of interleukin IL-1β, IL-4, IL-6 and tumor necrosis factor TNF-α.

4. The biomarker screening and detecting method for endocrine disruptor-induced neurotoxicity according to claim 1, wherein In step (3), when brain tissue transcriptome sequencing and neural pathway analysis are performed, the brain tissues of the control group and the exposure group are collected, total RNA is extracted, and the expression changes of genes related to neural function are analyzed by transcriptome sequencing; By Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis, genes with significant changes in key pathways including synaptic signaling, neurotransmitter synthesis, inflammatory signaling and mitochondrial function are identified, with |log2FC| ≥ 1.0, FDR < 0.05 as the standard.

5. The biomarker screening and detecting method for endocrine disruptor-induced neurotoxicity according to claim 1, wherein In step (3), when brain tissue metabolome sequencing and differential metabolite analysis are performed, the brain tissues of the control group and the exposure group are collected, and after ultrasonic extraction with organic solvent, time-of-flight mass spectrometry is used for metabolome analysis; By VIP > 1, FDR < 0.05 and FC > 1.5 as the screening standard, differential metabolites related to neural development pathways are identified, including one or more of tryptophan metabolites, lipid oxidation products and energy metabolism intermediates.

6. The biomarker screening and detecting method of endocrine disruptor-induced neurotoxicity according to claim 1, wherein, In step (4), the hyperparameters are optimized in the 5x5 nested cross-validation framework, and the top several key biomarkers are selected according to the average decrease in accuracy as the basis for feature importance.

7. The biomarker screening and detecting method of endocrine disruptor-induced neurotoxicity according to claim 1, wherein In step (6), the structural equation model fitting index requires that the goodness-of-fit index GFI > 0.90, the comparative fitting index CFI > 0.90, the root mean square error RMSEA < 0.08, and the root mean square residual SRMR < 0.

08.

8. The biomarker screening and detecting method of endocrine disruptor-induced neurotoxicity according to claim 1, wherein, In step (7), the calculation of the neurotoxicity composite index includes the following steps: a) Calculate the latent variable score based on the standardized load λ output by SEM: ; where Z i is the standardized score of the ith module latent variable, x ij is the standardized value of the jth observed indicator of the ith latent variable, λ ij is the corresponding loading; b) calculating the total effect τ of each module and normalizing to the weight w i : ; wherein w i is the normalized weight of the i-th module, reflecting the contribution of the module to the overall neurotoxic effect; τ represents the total effect of a single module, including the direct path effect and all indirect path effects from upstream indicators to downstream indicators in the module; T i The specific quantitative value of the total effect τ of the i-th module, i.e., the quantitative result of the total effect of the upstream indicator to the downstream indicator on the i-th module, contains direct and all indirect paths. c) Construct the neurotoxicity composite index NDTI by weighted summation: ; ; Among them, NDTI sample The neurotoxicity index is a comprehensive index for each independent sample, which is a quantitative result of the neurotoxicity level of a single sample. NDTI group The NDTI for each exposure group is a summary quantitative result of the overall neurotoxicity level of a certain exposure group. Z i,Group The average normalized score for the ith module in a certain exposure group, i.e., the mean of all samples Z i in that exposure group. Z i,Con The average normalized score for the ith module in the control group, i.e., the mean of all samples Z i in the control group.

9. The method for screening and detecting biomarkers of neurotoxicity induced by endocrine disruptors according to claim 1, characterized in that, In step (7), the significance test and toxicity grade determination are performed on the NDTI sample ANOVA analysis of variance and Dunnett post-hoc multiple comparisons are performed, and the toxicity grade is determined according to the significance p value value, while reporting the NDTI group as a strength quantitative indicator: No significant neurotoxicity, Mild toxicity Moderate to mild toxicity Severe mild toxicity.

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