The use of Aβ oligomers as biomarkers in the preparation of risk assessment products for mild cognitive impairment.

Abstract

This invention provides the use of a biomarker in the preparation of a risk assessment product for mild cognitive impairment (MCI), wherein the biomarker includes Aβ oligomers. The Aβ oligomer biomarker alone has significant predictive value for MCI, achieving an area under the receiver operating characteristic (AUC) of 0.851 (95% confidence interval: 0.803–0.895) on the test set when distinguishing between cognitively normal and MCI subjects, demonstrating its superior performance in differentiating MCI subjects.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to the use of Aβ oligomers as biomarkers in the preparation of risk assessment products for mild cognitive impairment. Background Technology

[0002] Mild cognitive impairment (MCI) is an intermediate clinical state between normal aging and dementia, characterized by an objective decline in cognitive function beyond age-expected levels, but without significant impairment in daily activities. The MCI population is at the highest risk of developing Alzheimer's disease (AD), with an annual conversion rate of approximately 10%-15%, far higher than that of healthy older adults. Therefore, precise risk stratification of MCI individuals and early identification of the "high-risk" subgroups most likely to rapidly progress to AD are of crucial clinical and social value for early intervention, optimized clinical trial enrollment, and allocation of healthcare resources.

[0003] Currently, early screening and risk assessment for mild cognitive impairment mainly rely on the following types of technologies, but they all have significant limitations when used for large-scale screening: Neuropsychological screening scales and their limitations: Primary screening commonly employs neurocognitive scales such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA). While these scales are easy to administer, they have inherent limitations as screening tools: insufficient sensitivity (they are not sensitive to early-stage MCI, especially mild cognitive decline in highly educated individuals); and susceptibility to interference (the reliability and validity fluctuate significantly due to the influence of the participant's education level, cultural background, emotional state at the time of testing, and the subjective judgment of the assessor). More importantly, these screening methods are time-consuming and labor-intensive, requiring professional personnel, and are difficult to meet the practical needs of large-scale, rapid initial screening in primary healthcare and communities.

[0004] Advances and challenges in peripheral blood biomarker detection: In recent years, research on blood biomarkers has made groundbreaking progress. Indicators such as Tau protein phosphorylated at threonine-181 / 217 (p-tau181, p-tau217) and glial fibrillary acidic protein (GFAP) have performance close to that of cerebrospinal fluid (CSF) analysis and even amyloid PET imaging. However, there are still huge challenges in transforming these cutting-edge biomarkers into universal screening tools: (1) High technical threshold and cost: Their accurate detection heavily relies on high-end platforms such as Simoa single-molecule array technology and mass spectrometry. These devices are expensive and complex to operate, making them difficult to popularize in primary hospitals; (2) The detection performance still needs to be uniformly verified: Although many studies have reported the excellent performance of biomarkers such as p-tau217, the sensitivity and specificity reported by different research cohorts and different detection platforms are different, and the results are not yet uniform. The optimal cutoff value still needs to be further verified and standardized in prospective, large-scale community cohorts. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, this invention provides the use of Aβ oligomers as biomarkers in the preparation of mild cognitive impairment risk assessment products. The purpose of this invention is to construct a predictive model directly targeting the current risk of mild cognitive impairment, rather than simply detecting the pathology of mild cognitive impairment. Such a tool can more accurately identify the groups most in need of immediate clinical intervention and referral, thereby achieving efficient allocation of limited medical resources. This invention cleverly integrates readily available clinical information with low-cost, widely available blood indicators.

[0006] The first aspect of the present invention provides the use of biomarkers in the preparation of risk assessment products for mild cognitive impairment, said biomarkers including Aβ oligomers (Aβ O).

[0007] Furthermore, the Aβ oligomer is a soluble Aβ dimer to dodecamer.

[0008] Preferably, the Aβ oligomer includes at least the Aβ1-42 fragment.

[0009] Furthermore, the biomarkers also include the APOEε4 gene and multi-domain risk factors; the multi-domain risk factors include demographic and clinical indicators related to the risk of mild cognitive impairment; preferably, the demographic and clinical indicators related to the risk of mild cognitive impairment include one or more of age, sex, years of education, body mass index, history of alcohol consumption, family history of dementia, hypertension, diabetes, hyperlipidemia, and depressive state.

[0010] Optionally, the mild cognitive impairment risk assessment product is selected from models, systems, reagents, kits, chips, or test strips; Furthermore, the Aβ oligomers are derived from serum.

[0011] Preferably, the reagents and / or kits include reagents that specifically recognize Aβ oligomers.

[0012] Preferably, the reagent that specifically recognizes Aβ oligomers is selected from antibodies that specifically recognize Aβ oligomers or their antigen-binding fragments, nucleic acid aptamers, peptides, or small molecule compounds.

[0013] More preferably, the antibody has an L chain of CDR1, CDR2, and CDR3, wherein the amino acid sequence of CDR1 is shown in SEQ ID NO.1; the amino acid sequence of CDR2 is shown in SEQ ID NO.2; and the amino acid sequence of CDR3 is shown in SEQ ID NO.3. The H chain has CDR1, CDR2, and CDR3, wherein the amino acid sequence of CDR1 is shown in SEQ ID NO.4; the amino acid sequence of CDR2 is shown in SEQ ID NO.5; and the amino acid sequence of CDR3 is shown in SEQ ID NO.6.

[0014] Specifically, as shown below:

[0015] In risk assessment for mild cognitive impairment (MCI), existing technologies using antibodies have limitations in the sensitivity and specificity of detecting key biomarkers, making it difficult to achieve accurate early risk warnings. In contrast, the antibody provided by this invention achieves a significant improvement in detection performance by recognizing highly disease-related specific antigenic epitopes.

[0016] In terms of sensitivity, the antibody of this invention exhibits superior low-abundance detection capability. Its unique recognition characteristics targeting the biomarker enable it to stably capture and detect minute signal changes in low-concentration samples such as peripheral blood, which are difficult for traditional antibodies to detect. This allows risk assessment products based on this antibody to identify the potential risk of MCI earlier, creating the possibility for ultra-early intervention.

[0017] Regarding specificity, the antibodies of this invention are meticulously designed and screened to effectively distinguish target biomarkers highly correlated with disease progression from other structurally similar interfering proteins. This characteristic results in extremely low cross-reactivity rates in complex biological samples, thereby minimizing the occurrence of false positive results. Therefore, detection products using the antibodies of this invention can provide risk assessment results with higher confidence, significantly improving the accuracy of risk stratification and prediction.

[0018] The reagent is not limited to being in liquid form.

[0019] The active ingredient in the reagents and / or kits may be solely the reagent that specifically recognizes Aβ oligomers, or it may contain other substances. That is, the reagent that specifically recognizes Aβ oligomers may be the sole active ingredient or one of the active ingredients in the reagents and / or kits. The reagents and / or kits may be used in combination with other products or alone.

[0020] The reagents and / or kits may be commercially available reagents or kits, or homemade reagents. A commercially available kit, for example, is an ELISA detection kit. The kit components may include, for example, one or more antibodies or functional antibody fragments capable of specifically binding to Aβ oligomers, as well as enzyme-labeled antibodies, stop solutions, standards, controls, buffers, etc.

[0021] The kit described in this invention may also include other commonly used reagents required for ELISA detection. Since these commonly used ELISA reagents can be purchased separately from the market or prepared in-house, the specific reagents to be included in the kit can be configured according to the customer's actual needs. For convenience, all reagents can also be included in the kit.

[0022] There are no special restrictions on the form of the reagents and / or kits; they can be in various forms such as solid, liquid, gel, semi-liquid, or aerosol.

[0023] like Figure 1 As shown, a second aspect of the present invention provides a risk assessment system for mild cognitive impairment, the system comprising: The data acquisition module 11 is used to acquire the test data of the subject, the test data including at least the data of the biomarkers mentioned above; The risk assessment module 12 is communicatively connected to the data acquisition module and is used to take the detection data of the data acquisition module as input, and output the risk probability value of the subject having mild cognitive impairment through a pre-trained machine learning model. The result output module 13 is communicatively connected to the risk assessment module and is used to compare the risk probability value with a preset threshold and output risk classification information based on the comparison result.

[0024] This invention provides a nonlinear fusion machine learning screening system and method based on serum Aβ oligomers (AβO), APOE ε4 carrier status, and multi-domain risk factor data. It captures the complex interactions between a single blood biomarker (Aβ oligomer) and readily available data through a trained machine learning model, achieving low-cost, high-sensitivity prediction of mild cognitive impairment. This technical solution has been successfully deployed as an online web application at: https: / / ad-screening-app-az0909.streamlit.app / Preferably, the machine learning model is a non-linear machine learning model.

[0025] The nonlinear machine learning model involved in this invention can be one or more of the following: decision tree, support vector machine, random forest, lightweight gradient booster, extreme gradient booster, k-nearest neighbor algorithm, multilayer perceptron classifier, and Gaussian Naive Bayes classifier model.

[0026] Nonlinear machine learning models significantly outperform traditional linear models and baseline models. This invention innovatively uses machine learning to model Aβ oligomers, APOE ε4 carrier status, and multi-domain risk factor data (age, sex: female, body mass index, years of education, alcohol consumption history, family history of dementia, hypertension, diabetes, hyperlipidemia, and depressive state). A systematic comparison of nine different algorithms revealed that, as shown in Table 1, nonlinear models generally performed better, traditional linear models (such as logistic regression) performed second best, and conventional baseline models (such as those using only age + APOE4) performed the worst. This finding confirms the complex nonlinear interactions between Aβ oligomers and other clinical characteristics, and that using nonlinear models is a necessary technique for achieving high-precision prediction.

[0027] Table 1. The ability of the 12 models to distinguish between cognitively normal and cognitively impaired.

[0028] Further preferably, the machine learning model is a random forest model. The random forest model of this invention demonstrated excellent screening capabilities in both the total population and key subgroups. Considering both AUC and sensitivity results, this invention ultimately selected the random forest model, which demonstrated extremely high discriminative power of 0.9279 (95% confidence interval: 0.8854–0.9589) on an independent test set of 192 individuals. Simultaneously, it was found that the model achieved an AUC of 0.8828 when distinguishing between MCI patients and cognitively normal individuals; and an AUC of 0.8840 when distinguishing between patients diagnosed with AD pathology by Amyloid PET or CSF and cognitively normal individuals. At the Youden index (0.3969), the model achieved a sensitivity of 93.3% and a specificity of 78.8%.

[0029] This invention confirms the predictive value of Aβ oligomers and reveals for the first time that fusing multidimensional routine data through a nonlinear model is a key technical solution for achieving high-precision (AUC > 0.92) and high-sensitivity (>93%) clinical cognitive impairment screening.

[0030] Aβ oligomer marker alone has significant predictive value for MCI. When distinguishing between cognitively normal subjects and those with MCI, the area under the receiver operating characteristic (AUC) of the test set can reach 0.851 (95% confidence interval: 0.803-0.895).

[0031] Furthermore, in the result output module, when the risk probability value is not less than a preset threshold, "non-low risk" classification information is output; when the risk probability value is less than the preset threshold, "low risk" classification information is output.

[0032] Preferably, the preset threshold value ranges from 0.28 to 0.31.

[0033] More preferably, the preset threshold is 0.2920.

[0034] On the other hand, preferably, the present invention employs a "dual threshold" strategy to optimize clinical triage efficiency.

[0035] In the result output module, the preset threshold includes a first preset threshold and a second preset threshold, wherein the first preset threshold is less than the second preset threshold; when the risk probability value is less than the first preset threshold, “low risk” classification information is output; when the risk probability value is greater than the second preset threshold, “high risk” classification information is output; when the risk probability value is not less than the first preset value and not greater than the second preset threshold, “medium risk” classification information is output.

[0036] Preferably, the first preset threshold value ranges from 0.28 to 0.31, and the second preset threshold value ranges from 0.40 to 0.44.

[0037] More preferably, the first preset threshold is 0.2920 and the second preset threshold is 0.4251.

[0038] Specifically, such as Figure 26 As shown and Figure 28 As shown, preset exclusion thresholds (Low Cutoff) and confirmation thresholds (High Cutoff) are defined: The exclusion threshold (first preset threshold) is set at 0.2920. This setting is based on the clinical goal of ensuring high sensitivity (sensitivity ≥ 90%), aiming to minimize missed diagnoses. At this threshold, test set data shows a negative predictive value (NPV) as high as 97.7%, meaning that subjects with scores below this value can be confidently classified as "low-risk," thus avoiding further expensive PET or cerebrospinal fluid examinations and significantly saving medical resources.

[0039] The diagnostic threshold (second preset threshold) is set at 0.4251. This is based on the clinical goal of ensuring high specificity, aiming to accurately identify high-risk individuals. Within this threshold range, test set data showed a specificity of 0.8030. Subjects with scores higher than this value are considered "high-risk" and will be strongly recommended for referral to a specialist for gold-standard diagnosis.

[0040] Gray zone (the area between the exclusion threshold and the confirmed threshold): Probability values ​​between 0.2920 and 0.425 are defined as the gray zone. Test data shows that only about 15.03% of the subjects fell into this range. For this group, the system outputs a "medium risk or follow-up recommended" message, suggesting combining the simplified cognitive scale for further assessment and regular follow-up.

[0041] MCI dual threshold analysis results: 1. MCI exclusion threshold (Rule-out Cutoff) = 0.2920. Target sensitivity: 90.0% | Actual NPV: 0.9770. Clinical significance: A score below this (0.29) can exclude MCI.

[0042] 2. Grey Zone: 0.2920 -0.4251. Distribution of the MCI / CU subgroup in the test set (N=153). Low Risk: 87 people (56.86%). Grey Zone: 23 people (15.03%). High Risk: 43 people (28.10%). 3. Independent screening performance of CU vs. MCI: Independent AUC (CU vs. MCI): 0.8828. Optimal cutoff: 0.4251. Performance at this cutoff: Sensitivity / Recall: 0.8571 (ability to detect MCI). Specificity: 0.8030 (ability to exclude CU).

[0043] The random forest model used in this invention, combined with the above-mentioned dual-threshold strategy, successfully diverted 84.96% of the subjects to a clearly defined "low-risk" or "high-risk" group.

[0044] Furthermore, the system also includes a data preprocessing module, which is communicatively connected to the data acquisition module, for preprocessing the raw data of the detection data so that the detection data can be used as input to the risk assessment module.

[0045] Preferably, the preprocessing includes imputing missing values ​​in the original data of the detection data, and / or standardizing the continuous feature data in the original data to generate a standardized feature vector.

[0046] Optionally, a pre-trained Iterative Imputer model can be used to impute any missing items in the original data.

[0047] Optionally, the continuous feature data in the original data can be standardized using a fitted Z-score standardization (StandardScaler) model. Continuous features can be one or more of age, body mass index, Aβ oligomer concentration, and years of education.

[0048] Optionally, the system may further include a data input module, which is communicatively connected to the data preprocessing module, for inputting the raw data of the detection data.

[0049] The machine learning model is trained on a training dataset, where each training sample includes: the aforementioned biomarker data, and a binary label characterizing whether the individual corresponding to the training sample has mild cognitive impairment.

[0050] The risk probability value in the risk assessment module is a continuous probability value between 0.0 and 1.0. This probability value represents the individual's current risk of having mild cognitive impairment.

[0051] The data on Aβ oligomers were obtained from serum samples of the subjects.

[0052] The APOEε4 gene carrier status data were obtained from the subjects' blood samples.

[0053] like Figure 2 As shown, a third aspect of the present invention provides a method for assessing the risk of mild cognitive impairment, the method comprising the following steps: S1, Obtain the test data of the subject, wherein the test data includes at least the aforementioned biomarker data; S2, taking the detection data mentioned in step S1 as input, the pre-trained machine learning model outputs a risk probability value indicating that the subject has mild cognitive impairment; S3, compare the risk probability value with a preset threshold, and output risk classification information based on the comparison result.

[0054] Furthermore, the demographic and clinical data related to the risk of mild cognitive impairment include one or more of the following: age, sex, years of education, body mass index, history of alcohol consumption, family history of dementia, hypertension, diabetes, hyperlipidemia, and depressive state.

[0055] The assessment results provided by this invention can serve as an auxiliary reference tool for clinicians, but are not directly equivalent to clinical risk assessment. The mild cognitive impairment risk assessment method described in this invention is used for non-disease risk assessment and treatment purposes.

[0056] Preferably, the machine learning model is a non-linear machine learning model.

[0057] The nonlinear machine learning model involved in this invention can be one or more of the following: decision tree, support vector machine, random forest, lightweight gradient booster, extreme gradient booster, k-nearest neighbor algorithm, multilayer perceptron classifier, and Gaussian Naive Bayes classifier model.

[0058] Further preferably, the machine learning model is a random forest model. The random forest model of this invention demonstrates excellent screening capabilities in both the total population and key subgroups. Considering both AUC and sensitivity results, this invention ultimately selected the random forest model, which exhibited extremely high discriminative power of 0.9279 (95% confidence interval: 0.8854–0.9589) on an independent test set of 192 individuals. Simultaneously, it was found that the model achieved an AUC of 0.8828 when distinguishing between MCI patients and the cognitively normal group (e.g., ...). Figure 11 D and Figure 27 When differentiating patients diagnosed with AD pathology by Amyloid PET or CSF from cognitively normal patients, the AUC reached 0.8840. At the Youden index (0.3969), the model achieved 93.3% sensitivity and 78.8% specificity (e.g., Figure 11 As shown in E).

[0059] Furthermore, in step S3, when the risk probability value is not less than a preset threshold, “non-low risk” classification information is output; when the risk probability value is less than the preset threshold, “low risk” classification information is output.

[0060] Preferably, the preset threshold value ranges from 0.28 to 0.31.

[0061] More preferably, the preset threshold is 0.2920.

[0062] On the other hand, preferably, the present invention employs a "dual threshold" strategy to optimize clinical triage efficiency.

[0063] In step S3, the preset threshold includes a first preset threshold and a second preset threshold, wherein the first preset threshold is less than the second preset threshold; when the risk probability value is less than the first preset threshold, “low risk” classification information is output; when the risk probability value is greater than the second preset threshold, “high risk” classification information is output; when the risk probability value is not less than the first preset value and not greater than the second preset threshold, “medium risk” classification information is output.

[0064] Preferably, the first preset threshold value ranges from 0.28 to 0.31, and the second preset threshold value ranges from 0.40 to 0.44.

[0065] More preferably, the first preset threshold is 0.2920 and the second preset threshold is 0.4251.

[0066] Furthermore, the method also includes the following steps: S11, preprocessing the raw data of the detection data so that the detection data of step S1 can be used in step S2.

[0067] Preferably, the preprocessing includes imputing missing values ​​in the original data of the detection data, and / or standardizing the continuous feature data in the original data to generate a standardized feature vector.

[0068] Optionally, a pre-trained Iterative Imputer model can be used to impute any missing items in the original data.

[0069] Optionally, the continuous feature data in the original data can be standardized using a fitted Z-score standardization (StandardScaler) model. Continuous features can be one or more of age, body mass index, Aβ oligomer concentration, and years of education.

[0070] The machine learning model is trained on a training dataset, where each training sample includes: the aforementioned biomarker data; and a binary label characterizing whether the individual corresponding to the training sample has mild cognitive impairment.

[0071] The risk probability value in step S2 is a continuous probability value between 0.0 and 1.0. This probability value represents the individual's current risk of having mild cognitive impairment.

[0072] The data on Aβ oligomers were obtained from serum samples of the subjects.

[0073] The APOEε4 gene carrier status data were obtained from the subjects' blood samples.

[0074] like Figure 3 As shown, the fourth aspect of the present invention provides a method for constructing a risk assessment model for mild cognitive impairment, comprising at least the following steps: SS1, obtain training sample data, which includes data of the aforementioned biomarkers and a binary label characterizing whether the individual corresponding to the training sample has mild cognitive impairment; SS2 uses the training sample data obtained from SS1 to build a machine learning model.

[0075] Furthermore, the demographic and clinical data related to the risk of mild cognitive impairment include one or more of the following: age, sex, years of education, body mass index, history of alcohol consumption, family history of dementia, hypertension, diabetes, hyperlipidemia, and depressive state.

[0076] This invention confirms that a predictive model constructed using only one feature of serum Aβ oligomers, such as... Figure 4As shown, when distinguishing between cognitively normal subjects and those with Alzheimer's disease (AD), the area under the receiver operating characteristic (AUC) curve on the test set reached 0.8604 (95% confidence interval: 0.8018 - 0.9100). This indicates that Aβ oligomers are a potent and independent biomarker for clinical cognitive status.

[0077] Preferably, the machine learning model is a non-linear machine learning model.

[0078] The nonlinear machine learning model involved in this invention can be one or more of the following: decision tree, support vector machine, random forest, lightweight gradient booster, extreme gradient booster, k-nearest neighbor algorithm, multilayer perceptron classifier, and Gaussian Naive Bayes classifier.

[0079] More preferably, the machine learning model is a random forest model.

[0080] Furthermore, the method also includes the following steps: SS11, preprocessing the original data of the training sample data so that the training sample data of step SS1 can be used in step SS2.

[0081] Preferably, the preprocessing includes imputing missing values ​​in the original data of the training sample data, and / or standardizing the continuous feature data in the original data to generate standardized feature vectors.

[0082] Optionally, a pre-trained Iterative Imputer model can be used to impute any missing items in the original data.

[0083] Optionally, the continuous feature data in the original data can be standardized using a fitted Z-score standardization (StandardScaler) model. Continuous features can be one or more of age, body mass index, Aβ oligomer concentration, and years of education.

[0084] The data on Aβ oligomers were obtained from serum samples of the subjects.

[0085] The APOEε4 gene carrier status data were obtained from the subjects' blood samples.

[0086] Total Aβ in the blood includes various forms such as Aβ monomers (mainly Aβ1-40 and Aβ1-42), oligomers (AβO), and fibrils.

[0087] There is a certain correlation between the increase in Aβ oligomers and the increase in total Aβ levels, but the relationship between the two is quite complex, specifically reflected in the following two aspects.

[0088] Aβ oligomers are part of the total Aβ pool. If the total amount of Aβ produced and released into the bloodstream by the brain or other tissues increases, the concentration of Aβ oligomers, as an intermediate form, may also increase. In this case, an increase in total Aβ may indicate a synchronous increase in oligomer levels.

[0089] The Aβ molecule is not a static mixture in the body, but a dynamic equilibrium system:

[0090] In the early stages of mild cognitive impairment, total Aβ production may not increase significantly. The more crucial change may lie in a decreased Aβ clearance capacity or an increased tendency for Aβ to aggregate. This leads to Aβ monomers more easily aggregating into oligomers. During this process, total Aβ levels may not show significant fluctuations, or even decrease due to oligomers more readily depositing in brain tissue or forming insoluble aggregates that escape the "detectable pool." Because the neurotoxicity of Aβ oligomers is far stronger than that of monomers and fibrillary forms, even if total Aβ levels remain stable, a shift in equilibrium towards oligomers will significantly enhance their toxic effects. This is the main basis for the current academic community's view of Aβ oligomers as a key pathogenic factor.

[0091] In summary, while an increase in Aβ oligomers is associated with an increase in total Aβ, the level of Aβ oligomers depends more on the dynamic aggregation process of Aβ molecules than on the total amount. In the pathological process of mild cognitive impairment, an increase in the proportion of Aβ oligomers (even if the total Aβ level does not change significantly) is considered to be the core mechanism driving neurotoxicity.

[0092] A fifth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the program is executed by a processor, it implements the aforementioned method for assessing the risk of mild cognitive impairment, and / or the aforementioned method for constructing a risk assessment model for mild cognitive impairment.

[0093] A sixth aspect of the present invention provides a computer processing device, including a processor and the aforementioned computer-readable storage medium, wherein the processor executes a computer program on the computer-readable storage medium to implement the aforementioned method for assessing the risk of mild cognitive impairment, and / or the steps of the aforementioned method for constructing a risk assessment model for mild cognitive impairment.

[0094] A seventh aspect of the present invention provides an electronic terminal, comprising: a processor, a memory, and a communicator; the memory is used to store a computer program, the communicator is used to communicate with an external device, and the processor is used to execute the computer program stored in the memory to cause the terminal to perform the aforementioned method for assessing the risk of mild cognitive impairment, and / or the aforementioned method for constructing a risk assessment model for mild cognitive impairment.

[0095] As described above, the use of the Aβ oligomer of the present invention as a biomarker in the preparation of a risk assessment product for mild cognitive impairment has the following beneficial effects: (i) The Aβ oligomer marker alone has significant predictive value for MCI. When distinguishing between cognitively normal subjects and subjects with MCI, its area under the receiver operating characteristic curve (AUC) on the test set can reach 0.851 (95% confidence interval: 0.803 - 0.895), which proves its superior performance in distinguishing subjects with MCI.

[0096] (II) High Precision and Innovative Strategy: This invention overcomes the subjectivity of traditional scale assessments. Test set evaluation results show that the 12-feature random forest model of this invention achieved excellent performance with an AUC of 0.9279 (95% confidence interval: 0.8854 - 0.9589). This high precision is not achieved by simply piling up risk factors, but rather through a nonlinear machine learning model, achieving for the first time a deep, nonlinear fusion between a single blood biomarker (Aβ oligomer) and multidimensional clinical information. This fusion can capture the synergistic amplification effect of complex combinations such as "advanced age and carrying the APOEε4 gene with high Aβ oligomer concentration" on risk, which is impossible with traditional linear models (such as logistic regression) or single biomarkers. Therefore, this performance is significantly better than the simple model using only Aβ oligomers (AUC = 0.8604), and even better than the conventional risk model of age + APOEε4 carrier status (AUC = 0.6858), breaking through the bottleneck of single-indicator performance and producing synergistically enhanced screening efficacy.

[0097] (III) Model Advancement, High Sensitivity, and High Practicality: The random forest model used in this invention inherently possesses advantages such as handling nonlinear relationships, automatically assessing feature importance, and insensitivity to outliers and noise, ensuring high stability of the model when facing complex data from community populations. An AUC value exceeding 0.92 on the independent test set strongly demonstrates that the model is not overfitting the training data, but rather possesses strong generalization ability, effectively applicable to new and unseen individuals. The model exhibits superior performance in identifying MCI, achieving excellent recognition accuracy (AUC > 0.88) in both subgroups including MCI patients and AD patients diagnosed by Amyloid PET / CSF. This not only confirms its high sensitivity to MCI pathology but also highlights its powerful ability to accurately capture early clinical signals.

[0098] (IV) Minimally Invasive, Convenient, and Scalable: This screening system boasts a dual advantage in convenience. At the core level, its key biomarker, Aβ oligomer, can be obtained simply through routine venous blood sampling (based on a basic ELISA platform). Compared to invasive lumbar puncture and the complex procedures of Amyloid PET, the operation is extremely simple and minimally invasive, significantly improving subject acceptance and compliance. At the system level, this invention combines this minimally invasive blood test with 10 routine clinical and demographic characteristics that can be quickly obtained through simple inquiries, greatly lowering the barrier to promotion and eliminating the need for cutting-edge equipment in top medical centers or highly scarce professional personnel. The system's final output is not a difficult-to-interpret probability number, but rather a binary classification result of "high risk" or "low risk" based on the optimal clinical decision threshold (0.3969) determined in advance through the Youden index, delivered directly through a publicly accessible online application. This provides primary care physicians with a clear and intuitive basis for decision-making, avoiding the subjectivity and uncertainty of result interpretation, making it highly suitable for large-scale promotion and application by non-specialist physicians in community and primary healthcare institutions.

[0099] (V) Systematization, Standardization, and Interpretability: This invention is a complete decision support system that minimizes human error. The system has fully solidified and automated the data preprocessing (including missing value imputation and data standardization) and model prediction processes. Users only need to input the raw data, and the system can automatically complete all subsequent complex calculations and output the final risk classification. This design eliminates errors introduced by manual operation or calculation mistakes, ensuring the objectivity and repeatability of the screening results. Attached Figure Description

[0100] Figure 1 This is a mild cognitive impairment risk assessment system according to an embodiment of the present invention.

[0101] Figure 2 This is an embodiment of the method for assessing the risk of mild cognitive impairment according to the present invention.

[0102] Figure 3 This is a method for constructing a risk assessment model for mild cognitive impairment according to an embodiment of the present invention.

[0103] Figure 4 This is the receiver operating characteristic curve (ROC) of serum Aβ in distinguishing between cognitively normal individuals and those with AD cognitive impairment.

[0104] Figure 5 This is the calibration curve for the random forest model.

[0105] Figure 6 It is a decision curve analysis of the random forest model (DCA).

[0106] Figure 7It is a SHAP dependency graph with twelve features.

[0107] Figure 8 This is a schematic diagram of an electronic terminal according to an embodiment of the present invention.

[0108] Figure 9 The descriptive distribution (A) and ROC curve (B) of serum Aβ oligomers are shown.

[0109] Figure 10 It compares the ROC curves of all machine learning models and the baseline model on the independent test set.

[0110] Figure 11 This is a clinical utility and performance validation of the random forest model.

[0111] A. Confusion Matrix: This section shows the model's specific classification performance at the optimal classification threshold. Interpretation: True Negative (Top Left: 104): 104 cognitively normal subjects were correctly predicted as normal by the model. False Positive (Top Right: 28): 28 normal subjects were incorrectly predicted as having the disease. False Negative (Bottom Left: 4): Only 4 patients with cognitive impairment (MCI / AD) were incorrectly predicted as normal (this is the most critical indicator for screening tools, with an extremely low false negative rate). True Positive (Bottom Right: 56): 56 patients were correctly identified by the model. Clinical Significance: The calculated sensitivity of the model is as high as 93.3% (56 / 60), and the specificity is 78.8% (104 / 132). This suggests that the model is well-suited as an "exclusionary screening tool" because it rarely misses out on genuine patients.

[0112] B. Decision Curve Analysis (DCA). This section evaluates the net benefit of the model in clinical practice. Interpretation: Green line (Random Forest Model): Represents the net benefit of using the model for screening. Yellow dashed line (Treat All): Assumes everyone needs intervention / further testing. Gray dashed line (Treat None): Assumes no intervention for anyone. Clinical significance: The green line is significantly higher than the yellow and gray lines over a wide range of probability thresholds. This means that using the model to determine who needs further testing provides the greatest net benefit to patients and the healthcare system compared to "test everyone" or "no one is tested" (i.e., benefiting patients by identifying those who need further testing without increasing unnecessary testing).

[0113] C. Calibration Curve. This section assesses whether the probabilities predicted by the model are truly reliable. Interpretation: X-axis: The average probability predicted by the model. Y-axis: The actual observed positive rate. Diagonal dashed line: Represents perfect calibration (predicted probability = actual probability). Purple line: The actual performance of the model. Clinical significance: The purple line is very close to the diagonal dashed line, and the Brier Score is only 0.1086 (lower is better). This indicates that the model is neither severely overconfident nor underconfident. For example, when the model predicts a 70% risk of disease, approximately 70% of people actually develop the disease.

[0114] D. MCI Subgroup ROC Curve (ROC Curve: MCI vs. CU). This section validates the model's performance in early, mild cases. Interpretation: This figure only compares patients with mild cognitive impairment (MCI) with those with normal cognitive function (CU). AUC = 0.883 (95% CI: 0.791–0.956). Clinical Significance: Even in the milder and more difficult-to-diagnose stage of MCI, the model maintains high discriminative power. This demonstrates the model's potential for early screening.

[0115] E. ROC Curve for Biomarker-Confirmed Subgroups (PET / CSF Confirmed vs. CU). This section validates the model's performance in "gold standard" confirmed cases. Interpretation: This figure compares patients with confirmed AD pathology via PET scan or cerebrospinal fluid (CSF) testing with cognitively normal individuals. AUC = 0.884 (95% CI: 0.796–0.947). Clinical Significance: This is a crucial validation. It demonstrates that the model not only differentiates between "people with cognitive symptoms" but also specifically identifies patients with the biological pathology of Alzheimer's disease.

[0116] Figure 12 This is a SHAP summary diagram of a random forest model used to predict cognitive impairment.

[0117] Figure 13 It is the SHAP main effect dependency graph of 12 features in the random forest model.

[0118] Figure 14 This is a SHAP interaction diagram between Aβ oligomers and age.

[0119] Figure 15 This is a flowchart of a method for assessing the risk of mild cognitive impairment according to an embodiment of the present invention.

[0120] Figure 16 This is verified by digital western blot.

[0121] Figure 17 This is a comparison of the Aβ oligomer content in the peripheral blood and cerebral cortex tissue of APP / PS1 mice and control mice.

[0122] Figure 18 Electrophoresis image of monoclonal antibody AC1-4; Figure 19 Figure showing the titer of anti-Aβ oligomer monoclonal antibody; Figure 20 Western spectral analysis of monoclonal antibody AC1-4; Figure 21 This is a comparison chart of antibody activities; Figure 22 For detection specificity map; Figure 23 This is a comparison of plasma Aβ oligomer levels between AD and normal mice; Figure 24 This is a comparison of Aβ oligomer content in the brain tissue of AD mice and normal mice; Figure 25 This is a comparison image of normal individuals and AD patients.

[0123] Figure 26X-axis (Threshold Probability): The probability value of the disease predicted by the model (0.0 to 1.0).

[0124] Y-axis (Metric Value): The values ​​of each evaluation indicator (0.0 to 1.0, i.e., 0% to 100%).

[0125] Curve Meanings: Solid green line (Sensitivity): Sensitivity. Decreases as the threshold increases; Solid blue line (Specificity): Specificity. Increases as the threshold increases; Dashed light green line (NPV): Negative predictive value. Remains at a very high level in the low threshold region; Dashed light blue line (PPV): Positive predictive value. Increases as the threshold increases. Vertical Dashed Lines (Cutoffs): Green dotted line (Low Cutoff): Set at 0.292; Blue dotted line (High Cutoff): Set at 0.4251. Low Risk / Rule-out Zone – Green background on the left; High Risk / Rule-in Zone – Red background on the right; Grey Zone / Indeterminate Zone – Yellow background in the middle. Figure 27 ROC curves distinguishing between cognitively normal and MCI.

[0126] Figure 28 Cognitive normality and MCI threshold classification. Detailed Implementation

[0127] The research in this invention is based on the National Key Research and Development Program of China, "Research on the Mechanism and Intervention of Psychological-Sleep-Cognitive Interaction in the Aging Process" (2023YFC36003200, 2023YFC3603201).

[0128] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0129] Before further describing specific embodiments of the present invention, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments described below; it should also be understood that the terminology used in the embodiments of the present invention is for describing specific embodiments and not for limiting the scope of protection of the present invention; in the specification and claims of the present invention, unless otherwise expressly stated in the text, the singular forms "a", "an" and "this" include the plural forms.

[0130] When numerical ranges are given in the embodiments, it should be understood that, unless otherwise stated in the present invention, both endpoints of each numerical range and any value between the two endpoints may be selected. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. In addition to the specific methods, apparatus, and materials used in the embodiments, based on the knowledge of the prior art possessed by one of ordinary skill in the art and the description of this invention, any prior art methods, apparatus, and materials similar to or equivalent to those described, apparatus, and materials in the embodiments of this invention may be used to implement the present invention.

[0131] Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in this invention all employ conventional techniques in molecular biology, biochemistry, chromatin structure and analysis, analytical chemistry, cell culture, recombinant DNA technology, and related fields.

[0132] Since the systems and methods in this invention are based on the same principle, the definitions, calculation methods, implementation methods, and preferred methods of the same features can be used interchangeably and will not be repeated.

[0133] This invention confirms the predictive value of Aβ oligomers and, for the first time, achieves high-precision (AUC > 0.92) and high-sensitivity (>93%) clinical cognitive impairment screening by fusing multidimensional conventional data through a nonlinear model.

[0134] The reliability of the system described in this invention has also been fully verified: such as Figure 5 The calibration curve (Brill score = 0.1086) shows that the probability values ​​output by the model are accurate and reliable. Figure 6 The decision curve analysis (DCA) shows that the model demonstrates a higher net benefit than the "all-treatment" or "no-treatment" strategies at almost all relevant clinical thresholds. Regarding interpretability, as... Figure 7 As shown, Shapley Additive exPlanations reveal that Aβ oligomers, family history of dementia, and APOE ε4 carrier status are strong risk predictors, allowing physicians to intuitively understand the specific factors leading to a patient's high risk.

[0135] It should be noted that the division of modules in the system of this invention is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. These modules can be implemented entirely in software through processing element calls; they can also be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the acquisition module can be a separate processing element, or it can be integrated into a chip. Alternatively, it can be stored in memory as program code, and its functions can be called and executed by a processing element. The implementation of other modules is similar. Furthermore, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through the integrated logic circuits in the hardware of the processor element or through software instructions.

[0136] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs, or Graphics Processing Units, GPUs). As another example, when a module is implemented through processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to form a system-on-a-chip (SOC).

[0137] like Figure 8 The diagram illustrates an electronic terminal provided by the present invention. The electronic terminal includes a processor 31, a memory 32, a communicator 33, a communication interface 34, and a system bus 35. The memory 32 and the communication interface 34 are connected to the processor 31 and the communicator 33 via the system bus 35 and communicate with each other. The memory 32 stores computer programs, the communicator 34 and the communication interface 34 communicate with other devices, and the processor 31 and the communicator 33 run the computer programs, enabling the electronic terminal to execute the various steps of the image analysis method described above.

[0138] The system bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write libraries, and read-only libraries). Memory may include Random Access Memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0139] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0140] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented using hardware related to a computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; the computer-readable storage medium may include, but is not limited to, floppy disks, optical disks, CD-ROMs (Read-Only Optical Disk Memory), magneto-optical disks, ROMs (Read-Only Memory), RAMs (Random Access Memory), EPROMs (Erasable Programmable Read-Only Memory), EEPROMs (Electrically Erasable Programmable Read-Only Memory), magnetic cards or optical cards, flash memory, or other types of media / machine-readable media suitable for storing machine-executable instructions. The computer-readable storage medium can be a product not connected to a computer device or a component used in a computer device.

[0141] In practice, the computer program is a routine, program, object, component, data structure, etc., that performs a specific task or implements a specific abstract data type.

[0142] The subjects described in this invention can be humans or other mammals, such as primates, rodents, mice, and monkeys.

[0143] The baseline characteristics of the study cohort (the baseline characteristics of the entire dataset, which were subsequently divided into training and test sets) were analyzed. The study cohort included 958 participants, stratified by risk assessment into a cognitively normal (CU) group (N=658) and a cognitively impaired (CI) group (N=298). Significant differences were observed between the two risk assessment groups; compared with the CU group, the CI group had a higher mean age, fewer years of education, and exhibited a higher proportion of APOEε4 carriers and depressive symptoms (Table 2). This complete cohort was then divided into a training set (N=770) and an independent test set (N=192). The stratification was confirmed to be balanced, with standardized mean differences (SMD) of all characteristics between the two groups well below 0.1.

[0144] Table 2. Baseline demographics and clinical characteristics of the study cohort

[0145] Independent risk assessment performance of serum Aβ oligomers (ABO): Given their central role, we first evaluated the distribution of the core biomarker, serum Aβ oligomers (AβO). Distribution ( Figure 9 The results (A) showed that the original Aβ oligomer level (median 134.9 pg / ml) was significantly higher in the CI group compared to the CU group (median 58.0 pg / ml). To quantify its independent risk assessment utility, we trained a logistic regression model containing only Aβ oligomers on the training set and evaluated it on the independent test set. This independent biomarker model showed strong discriminative power, with an area under the curve (AUC) of 0.860 (95% CI: 0.802–0.910), achieving 85% sensitivity and 77% specificity at the optimal probability cutoff point (0.50). Figure 9 (B).

[0146] Machine Learning Model Development and Performance Comparison: After evaluating the Aβ oligomer univariate model, we developed and compared 10 state-of-the-art machine learning (ML) models integrating all 12 pre-specified features, and compared them with two baseline logistic regression (LR) models (Table 3). The 12-feature model significantly outperformed both baseline models (Baseline LR (Age+APOE4) AUC = 0.686; Baseline LR (AβO Only) AUC = 0.860). The superiority of this multi-feature approach integrating Aβ oligomers and clinical data is evident in the ROC curve comparison (Table 3). Figure 10 This was intuitively confirmed in the data. The best-performing models on the independent test set were Support Vector Machine (SVM) (AUC = 0.930) and Random Forest (RF) (AUC = 0.928). The Random Forest (RF) model was chosen as the final model for all subsequent in-depth analyses due to its combination of high accuracy, robust cross-validation performance, and excellent interpretability. This model demonstrated excellent generalization ability, with its test set AUC (0.928) showing only a slight difference compared to the training set AUC (0.985), indicating minimal overfitting, and its performance was statistically significantly better than the baseline model (DeLong test, p < 0.001).

[0147] Table 3. Performance comparison of machine learning models in cognitive impairment classification

[0148] In-depth validation of the selected random forest model: The selected 12-feature RF model underwent a comprehensive validation process on an independent test set. Figure 11 For classification performance at the optimal Youden index threshold (0.397), please refer to the confusion matrix. Figure 11 The matrix (A) shows that the model correctly classified 104 out of 132 patients in the CU group (specificity 78.8%) and 56 out of 60 patients in the CI group (sensitivity 93.3%). Decision curve analysis (DCA) demonstrated its significant clinical applicability, as the RF model consistently provided a net benefit higher than the "all treatment" or "no treatment" strategies across a broad range of clinically relevant threshold probabilities. Figure 11 (B). Furthermore, the model was shown to have good calibration accuracy, with its calibration curve closely fitting the diagonal of perfect calibration, supported by a low Brier score (0.109) and a high BSS (0.494). Figure 11(C). This high level of reliability contrasts with the poor calibration of other high AUC models such as SVM. The model's robustness was demonstrated in key clinical subgroups, both in differentiating MCI patients (AUC = 0.883) Figure 11 (D) or is it distinguishing patients diagnosed by PET / CSF (AUC = 0.884) Figure 11 Both the E and CU control groups showed excellent distinguishing ability.

[0149] We used SHAP analysis to explain the final model: We employed SHAP (SHapley Additive exPlanations) analysis on the test set to explain the model's decision-making process. Global Feature Importance Summary Plot ( Figure 12 The results showed that serum Aβ oligomers were the most influential single feature to date (mean |SHAP| = 0.207), with a driving force far exceeding that of second-tier features (including BMI, age, APOE ε4 carrier status, and years of education). Notably, the effects of some traditional clinical risk factors (such as hypertension, hyperlipidemia, and alcohol consumption) on the model output were almost negligible. The SHAP main effect dependency plots for the 12 features are shown below. Figure 13 These relationships were further clarified, showing the direction and magnitude of each feature's contribution. The analysis confirmed that higher Aβ oligomer levels, older age, and female sex were associated with increased risk, while higher education levels were protective. These plots also revealed complex nonlinear patterns, such as the U-shaped association between BMI and risk, confirming the model's ability to capture synergistic relationships beyond simple linear associations. This synergy was clearly visualized in a SHAP interaction plot, showing that the positive effect of Aβ oligomers on risk was amplified in older individuals. Figure 14 ). Example 1

[0150] like Figure 15 As shown, this embodiment includes the following steps: Data Collection: During community health checkups, subject Y (70 years old) was assessed, and 12 characteristic data required for this invention were collected: Age: 70; Gender: Female; Body Mass Index (BMI): 24; Years of Education: 9 (years); Serum Aβ oligomer: 125.0 (pg / mL); APOEε4 carrier status: Yes; History of alcohol consumption: Yes; Family history of dementia: None; Hypertension: No; Diabetes: No; Hyperlipidemia: No; Depression status: No.

[0151] The 12 core features incorporated into this system model were collected through the following two main approaches: Blood Collection and Analysis (2 core features): A blood sample is collected from a routine venous blood draw and used for the following two key tests: Serum Aβ oligomer assay: Approximately 5 mL of venous blood was collected from the subject and injected into a serum coagulation tube. After blood collection, the tube was allowed to stand at room temperature (approximately 20-25°C) for 30-60 minutes to allow the blood to coagulate fully. The coagulated sample tube was then placed in a centrifuge and centrifuged at a relative centrifugal force of 1500-1800 xg for 10-15 minutes at room temperature. The supernatant after centrifugation is the serum. The serum was carefully aspirated, avoiding contact with the lower clot. The separated serum was aliquoted into polypropylene cryovials and immediately frozen at -80°C until analysis. Before testing, the sample tubes were thawed in a 37°C water bath for 15 minutes, and then the serum sample was used to determine the Aβ oligomer concentration using an enzyme-linked immunosorbent assay (ELISA).

[0152] APOE ε4 carrier status determination (using anticoagulant tubes): During the same blood collection session, collect approximately 3-5 mL of venous blood and inject it into an EDTA anticoagulant tube (usually a purple-tipped tube). Immediately after blood collection, gently invert and mix 8-10 times to prevent clotting. Centrifuge the EDTA anticoagulant tube at room temperature at a relative centrifugation force of approximately 1500 xg for 10-15 minutes. After centrifugation, the sample separates into three layers: an upper plasma layer, a thin middle layer of leukocytes, and a lower layer of erythrocytes. Carefully aspirate the upper plasma layer and collect the leukocyte layer from which genomic DNA is extracted. Use the extracted DNA for APOE genotyping to determine whether the individual is an APOE ε4 carrier.

[0153] Routine clinical and demographic data (10 features in total): Obtained through inquiry: Demographics (4 items): age, sex, years of education, body mass index.

[0154] Clinical and lifestyle factors (6 items): history of alcohol consumption, family history of dementia, hypertension, diabetes, hyperlipidemia, and depressive state (based on the Geriatric Depression Scale-15, a score greater than 4 indicates a depressive state).

[0155] Inputting data: Enter the collected subject data on the online webpage https: / / ad-screening-app-az0909.streamlit.app / .

[0156] Data Preprocessing: Input the data of “Subject Y” into the data preprocessing module. Missing Value Imputation (if missing values ​​exist during data collection): If a variable is missing, the system will start IterativeImputer and use the regression model of the remaining 10 features to estimate the value of the missing variable; Standardization: The system starts StandardScaler and applies the mean and standard deviation saved during training to the four continuous features of age (70), body mass index (BMI) (24), serum Aβ oligomer: 125.0, and years of education: 9, converting them into standardized values.

[0157] Risk prediction result 1: The 12 preprocessed feature vectors are input into the risk prediction module (a trained random forest model). After calculation, the model outputs a probability value. Output risk probability: 61.42%.

[0158] Decision Support and Interpretation 1-1: The decision support module compares this probability with the preset dual thresholds for MCI. The first preset threshold is 0.2920; the second preset threshold is 0.4251. Because 0.6142 (risk probability) > 0.4251, the system determines that "Subject Y" is "high-risk for MCI".

[0159] Decision Support and Interpretation 1-2: The decision support module compares this probability with the preset dual thresholds for MCI. The first preset threshold is 0.28; the second preset threshold is 0.40. Because 0.6142 (risk probability) > 0.40, the system determines "Subject Y" as "high-risk for MCI".

[0160] Decision Support and Interpretation 1-3: Classification Decision: The decision support module compares this probability with the preset MCI dual thresholds. The first preset threshold is 0.31; the second preset threshold is 0.44. Because 0.6142 (risk probability) > 0.44, the system determines "Subject Y" as "high MCI risk".

[0161] Risk prediction result 2: The preprocessed feature vector of the individual Aβ oligomer results was input into the risk prediction module. The remaining 11 features (APOEε4 gene and multi-domain risk factor) were all set to the average level of the population (using a trained random forest model). After calculation, the model outputs a probability value. Output risk probability: 44.69%.

[0162] Decision Support and Explanation 2: Compare this probability with the preset MCI dual thresholds. The first preset threshold is 0.2920; the second preset threshold is 0.4251. Because 44.69% (risk probability) > 0.4251, the system determines "Subject Y" as "high MCI risk". Example 2

[0163] like Figure 15 As shown, this embodiment includes the following steps: Data Collection: During a community health check, subject "Subject X" (65 years old) was assessed, and 12 characteristic data required for this invention were collected: Age: 65; Gender: Female; Body Mass Index (BMI): 22; Years of Education: 12 (years); Serum Aβ oligomer: 50.0 (pg / mL); APOEε4 carrier status: No; History of alcohol consumption: Yes; Family history of dementia: None; Hypertension: No; Diabetes: No; Hyperlipidemia: No; Depression status: No.

[0164] The 12 core features incorporated into this system model were collected through the following two main approaches: Blood Collection and Analysis (2 core features): A blood sample is collected from a routine venous blood draw and used for the following two key tests: Serum Aβ oligomer assay: Approximately 5 mL of venous blood was collected from the subject and injected into a serum coagulation tube. After blood collection, the tube was left to stand at room temperature (approximately 20-25°C) for 30-60 minutes to allow the blood to fully coagulate. The coagulated sample tube was then placed in a centrifuge and centrifuged at a relative centrifugal force of 1500-1800 xg for 10-15 minutes at room temperature. The supernatant after centrifugation is the serum. The serum was carefully aspirated, avoiding contact with the lower clot. The separated serum was aliquoted into polypropylene cryovials and immediately frozen at -80°C until analysis. Before testing, the sample tubes were thawed in a 37°C water bath for 15 minutes, and then the serum sample was used to determine the Aβ oligomer concentration using an enzyme-linked immunosorbent assay (ELISA).

[0165] APOE ε4 carrier status determination (using anticoagulant tubes): During the same blood collection session, collect approximately 3-5 mL of venous blood and inject it into an EDTA anticoagulant tube (usually a purple-tipped tube). Immediately after blood collection, gently invert and mix 8-10 times to prevent clotting. Centrifuge the EDTA anticoagulant tube at room temperature at a relative centrifugation force of approximately 1500 xg for 10-15 minutes. After centrifugation, the sample separates into three layers: an upper plasma layer, a thin middle layer of leukocytes, and a lower layer of erythrocytes. Carefully aspirate the upper plasma layer and collect the leukocyte layer from which genomic DNA is extracted. Use the extracted DNA for APOE genotyping to determine whether the individual is an APOE ε4 carrier.

[0166] Routine clinical and demographic data (10 features in total): Obtained through inquiry: Demographics (4 items): age, sex, years of education, body mass index.

[0167] Clinical and lifestyle factors (6 items): history of alcohol consumption, family history of dementia, hypertension, diabetes, hyperlipidemia, and depressive state (based on the Geriatric Depression Scale-15, a score greater than 4 indicates a depressive state).

[0168] Inputting data: Enter the collected subject data on the online webpage https: / / ad-screening-app-az0909.streamlit.app / .

[0169] Data Preprocessing: Input the data of “Subject X” into the data preprocessing module. Missing Value Imputation (if missing values ​​exist during data collection): If a variable is missing, the system will start IterativeImputer and use the regression model of the remaining 10 features to estimate the value of the missing variable; Standardization: The system starts StandardScaler and applies the mean and standard deviation saved during training to the four continuous features of age (65), body mass index (BMI) (22), serum Aβ oligomer: 50.0, and years of education: 12, converting them into standardized values.

[0170] Risk prediction result 1: The 12 preprocessed feature vectors are input into the risk prediction module (a trained random forest model). After calculation, the model outputs a probability value. Output risk probability: 8.78%.

[0171] Decision Support and Interpretation 1-1: Classification Decision: The decision support module compares this probability with the preset MCI dual thresholds. The first preset threshold is 0.2920; the second preset threshold is 0.4251. Because 8.78% (risk probability) < 0.2920, the system determines "Subject X" as "low MCI risk".

[0172] Decision Support and Interpretation 1-2: Classification Decision: The decision support module compares this probability with the preset MCI dual thresholds. The first preset threshold is 0.28; the second preset threshold is 0.40. Because 8.78% (risk probability) < 0.28, the system determines "Subject X" as "low MCI risk".

[0173] Decision Support and Interpretation 1-3: Classification Decision: The decision support module compares this probability with the preset MCI dual thresholds. The first preset threshold is 0.31; the second preset threshold is 0.44. Because 8.78% (risk probability) < 0.31, the system determines "Subject X" as "low MCI risk".

[0174] Risk prediction result 2: The preprocessed feature vector of the individual Aβ oligomer results was input into the risk prediction module (a trained random forest model), and the remaining 11 features (APOEε4 gene and multi-domain risk factor) were all set to the average level of the population. After model calculation, a probability value was output. Output risk probability: 2.88%.

[0175] Decision Support and Explanation 2: Compare this probability with the preset MCI dual thresholds. The first preset threshold is 0.2920; the second preset threshold is 0.4251. Because 2.88% (risk probability) < 0.2920, the system determines "Subject X" as "low MCI risk". Example 3

[0176] like Figure 15 As shown, this embodiment includes the following steps: Data Collection: During a community health check, subject Z (62 years old) was assessed, and 12 characteristic data required for this invention were collected: Age: 62; Gender: Male; Body Mass Index (BMI): 27; Years of Education: 9 (years); Serum Aβ oligomer: 95.0 (pg / mL); APOEε4 carrier status: No; History of alcohol consumption: Yes; Family history of dementia: None; Hypertension: Yes; Diabetes: Yes; Hyperlipidemia: Yes; Depression status: Yes.

[0177] The 12 core features incorporated into this system model were collected through the following two main approaches: Blood Collection and Analysis (2 core features): A blood sample is collected from a routine venous blood draw and used for the following two key tests: Serum Aβ oligomer assay: Approximately 5 mL of venous blood was collected from the subject and injected into a serum coagulation tube. After blood collection, the tube was allowed to stand at room temperature (approximately 20-25°C) for 30-60 minutes to allow the blood to coagulate fully. The coagulated sample tube was then placed in a centrifuge and centrifuged at a relative centrifugal force of 1500-1800 xg for 10-15 minutes at room temperature. The supernatant after centrifugation is the serum. The serum was carefully aspirated, avoiding contact with the lower clot. The separated serum was aliquoted into polypropylene cryovials and immediately frozen at -80°C until analysis. Before testing, the sample tubes were thawed in a 37°C water bath for 15 minutes, and then the serum sample was used to determine the Aβ oligomer concentration using an enzyme-linked immunosorbent assay (ELISA).

[0178] APOE ε4 carrier status determination (using anticoagulant tubes): During the same blood collection session, collect approximately 3-5 mL of venous blood and inject it into an EDTA anticoagulant tube (usually a purple-tipped tube). Immediately after blood collection, gently invert and mix 8-10 times to prevent clotting. Centrifuge the EDTA anticoagulant tube at room temperature at a relative centrifugation force of approximately 1500 xg for 10-15 minutes. After centrifugation, the sample separates into three layers: an upper plasma layer, a thin middle layer of leukocytes, and a lower layer of erythrocytes. Carefully aspirate the upper plasma layer and collect the leukocyte layer from which genomic DNA is extracted. Use the extracted DNA for APOE genotyping to determine whether the individual is an APOE ε4 carrier.

[0179] Routine clinical and demographic data (10 features in total): Obtained through inquiry: Demographics (4 items): age, sex, years of education, body mass index.

[0180] Clinical and lifestyle factors (6 items): history of alcohol consumption, family history of dementia, hypertension, diabetes, hyperlipidemia, and depressive state (based on the Geriatric Depression Scale-15, a score greater than 4 indicates a depressive state).

[0181] Inputting data: Enter the collected subject data on the online webpage https: / / ad-screening-app-az0909.streamlit.app / .

[0182] Data Preprocessing: Input the data of “Subject Z” into the data preprocessing module. Missing Value Imputation (if missing values ​​exist during data collection): If a variable is missing, the system will start IterativeImputer and use the regression model of the remaining 10 features to estimate the value of the missing variable; Standardization: The system starts StandardScaler and applies the mean and standard deviation saved during training to the four continuous features of age (62), body mass index (BMI) (27), serum Aβ oligomer: 95.0, and years of education: 9, converting them into standardized values.

[0183] Risk Prediction 1: Input the 12 preprocessed feature vectors into the risk prediction module (a trained random forest model). After calculation, the model outputs a probability value. Output risk probability: 32.63%.

[0184] Decision Support and Interpretation 1: Classification Decision: The decision support module compares this probability with the preset MCI thresholds. The first preset threshold is 0.2920; the second preset threshold is 0.4251. Because 32.63% (risk probability) > 0.2920 and < 0.4251, the system determines "Subject Z" as "Medium Risk in MCI".

[0185] Decision Support and Interpretation 1-2: Classification Decision: The decision support module compares this probability with the preset MCI dual thresholds. The first preset threshold is 0.28; the second preset threshold is 0.40. Because 32.63% (risk probability) > 0.28 and < 0.40, the system determines "Subject Z" as "high MCI risk".

[0186] Decision Support and Interpretation 1-3: Classification Decision: The decision support module compares this probability with the preset MCI dual thresholds. The first preset threshold is 0.31; the second preset threshold is 0.44. Because 32.63% (risk probability) > 0.31 and < 0.44, the system determines "Subject Z" as "high MCI risk".

[0187] Risk Prediction 2: The preprocessed feature vector of the individual Aβ oligomer results is input into the risk prediction module (a trained random forest model). The remaining 11 features (APOE ε4 gene and multi-domain risk factors) are all set to the average level of the population. After model calculation, a probability value is output. Output risk probability: 23.95% Decision Support and Explanation 2: Compare this probability with the preset MCI dual thresholds. The first preset threshold is 0.2920; the second preset threshold is 0.4251. Because 23.95% (risk probability) < 0.2920, the system determines "Subject Z" as "low MCI risk".

[0188] The dual thresholds calculated using data from individuals with MCI and those with normal cognitive function (excluding AD) are 0.2920-0.4251. Therefore, 0.3263 represents a moderate risk for MCI. Thus, 0-0.2920 indicates low risk for MCI, 0.2920-0.4251 indicates moderate risk, and values ​​above 0.4251 indicate high risk. A more detailed explanation follows. Phase 1: Low-risk zone (probability value < 0.2920). Definition: High-confidence zone with normal cognition. Explanation: The predicted probability of the subject is below the low cutoff value of MCI (0.2920). Within this range, the model excludes the risk of MCI with high specificity. Clinical recommendation: No intervention is required; routine regular check-ups are recommended.

[0189] Phase Two: MCI Gray Zone (0.2920 to 0.4251). Definition: The uncertain zone in the transition from cognitively normal to MCI. Explanation: The subject's score has exceeded the safety threshold for cognitively normality, but has not yet reached the high confidence threshold for diagnosing MCI (0.4251). At this point, the model indicates a risk, but a diagnosis cannot be made based solely on the current data. Clinical Recommendation: It is recommended to include the subject in the follow-up list; further follow-up or supplementary low-cost testing may be necessary.

[0190] Phase 3: Diagnosis of MCI (>0.4251). Definition: A clearly defined area of ​​cognitive impairment. Interpretation: Lower limit: A score exceeding the high cutoff value of MCI (0.4251) indicates that the model is confident that the subject is no longer cognitively normal but has entered the MCI stage. Clinical recommendation: Clinical intervention should be initiated. This is the optimal window for drug trials or lifestyle interventions to delay the progression to Alzheimer's disease (AD).

[0191] Clinical Recommendation: Traditional Approach: Subjects Y and Z may be classified as normal by the scale due to mild or absent symptoms, leading to screening failure. Invention: This system provides an objective high-risk indication driven by the core pathological substance Aβ oligomer in MCI. The detection accuracy of 12 eigenvalues ​​is higher than that of a single eigenvalue. Physicians should recommend further neuropsychological evaluation for Subject Y and refer them to a specialist clinic. Simultaneously, interpretability analysis is invoked: the system utilizes the Shapley incremental interpretation module to reveal to physicians that high Aβ oligomer values, advanced age, APOEε4 carrier status, female sex, and a positive history of alcohol consumption are the main driving factors increasing their risk probability. Physicians can then intervene in the patient's case based on this information.

[0192] The Aβ oligomer detection method in this embodiment of the invention detects soluble oligomers (dimers to dodecamers) polymerized from Aβ monomers. Verification was performed using fully automated digital western blot capillary electrophoresis on peripheral serum from AD patients. The results are as follows: Figure 16 The oligomer component is mainly composed of the Aβ1-42 fragment. Since Aβ1-42 has two more hydrophobic amino acids than Aβ1-40, it is more hydrophobic and easier to aggregate. Therefore, it is considered to be the most toxic subtype of oligomers.

[0193] The advantages of the testing technology include: Using a patented antibody that can specifically recognize Aβ oligomers as the core raw material, combined with a unique repeat epitope sandwich method, high specificity and high sensitivity detection of Aβ oligomers are achieved (authorized patent number: ZL 202510139096.8).

[0194] Validation experiments conducted in AD model mice (APP / PS1) Experimental Methods: 1. Three 6-month-old male APP / PS1 AD model mice and three wild-type control mice were selected. Blood was collected from the heart after anesthesia, and an anticoagulant was added. The blood was centrifuged, and the supernatant plasma was collected. Simultaneously, brain tissue was removed, and 1 mL of RIPA strong lysis agent (containing protease inhibitors and phosphatase inhibitors) was added. The tissue was homogenized using a Tissue Lyser II tissue homogenizer at 30 Hz for 8 min, centrifuged at 14000 rpm for 30 min at 4℃, and the supernatant was collected as brain tissue homogenate. 2. Using the prepared detection kit, Aβ oligomers in the plasma and brain tissue homogenates of AD mice and control mice were detected, respectively. The content of Aβ oligomers in the peripheral blood and brain tissue of AD model mice was significantly higher than that in control mice. The detection results are as follows: Figure 17 As shown, the levels of Aβ oligomers in the blood and cerebral cortex of the mice were significantly higher than those in the control mice, indicating that the detection method based on peripheral blood Aβ oligomers can effectively reflect relevant changes in brain tissue.

[0195] The results of the validation experiments conducted in AD model mice (APP / PS1) are as follows: Figure 17 As shown, the levels of Aβ oligomers in the blood and cerebral cortex of mice were significantly higher than those in control mice, indicating that based on this detection method, the detection of Aβ oligomers in peripheral blood can effectively reflect relevant changes in brain tissue.

[0196] Example 4 Preparation of Aβ oligomers 1. Dissolve 5 mg of Aβ42 peptide (purchased from Hubei Qiangyao Biotechnology) in hexafluoroisopropanol (HFIP) to prepare a 1 mg / ml solution. Seal at room temperature for 60 min, place on ice for 10 min, and then place in a fume hood with the lid off to evaporate at room temperature overnight to allow the HFIP to evaporate completely.

[0197] 2. Add a small amount of dimethyl sulfoxide (DMSO) to the treated peptide tube to reconstitute it, dilute it with PBS to 2 mg / ml, and then place it on a 37°C constant temperature shaker and shake at a frequency of 800 rpm.

[0198] 3. After shaking for 48 hours, centrifuge at 10,000 rpm for 30 minutes at 4 degrees Celsius. The supernatant is the Aβ oligomer.

[0199] 1. Animal immunization and polyclonal antibody titer determination (1) Animal selection: Five healthy female Balb / c mice aged 5 weeks were selected and immunized with the immunogen prepared in Example 2.

[0200] (2) Immunization method: Immunization is carried out by subcutaneous injection at multiple points. The first immunization is given with Freund's complete adjuvant, and subsequent immunizations are given with incomplete adjuvant. The second immunization is given three weeks after the first immunization, and then every two weeks thereafter, for a total of four immunizations.

[0201] (3) Immunization dose: 0.1 mg / animal / time.

[0202] (4) Collection of polyclonal antibody serum: Two weeks after the last immunization, blood was collected from the tail and diluted 100 times with PBS. The mixture was centrifuged at 3000 rpm for 10 min, the supernatant was collected and aliquoted, and stored at -20℃.

[0203] (5) After coating the enzyme-linked immunosorbent assay (ELISA) plate with Aβ oligomers, the plate was blocked. Polyclonal antibody serum and enzyme-labeled secondary antibody were added, and the titer of the polyclonal antibody was determined by colorimetric reaction. The results are shown in Table 4.

[0204] Table 4. Determination of polyclonal antibody titers in mice.

[0205] Preliminary ELISA results show that mice numbered 3 had the best immune response.

[0206] 2. Cell fusion and screening of hybridoma cells.

[0207] (1) Recovery and culture of SP2 / 0 tumor cells. SP2 / 0 tumor cells were taken out from the liquid nitrogen tank, dissolved in a 37°C water bath, transferred to a centrifuge bottle, and RPMI1640 / 10 preheated at 37°C was added. The cells were centrifuged at 1000 rpm for 10 min, the supernatant was discarded, the cell clumps were broken up, 10 mL of RPMI1640 / 10 was added to the centrifuge bottle and mixed well. The mixture was then placed in a 5% CO2, 37°C incubator for later use.

[0208] (2) Preparation of feeder cells. Take one unimmunized Bal b / c mouse, remove the eyeball to collect blood, then euthanize the mouse by pulling its neck and immerse it in 75% alcohol. Carefully cut open the mouse peritoneum with sterile forceps and scissors to fully expose the peritoneal cavity. Draw 10 mL of cold HAT culture medium into a 10 mL sterile syringe and inject it into the mouse peritoneal cavity. Gently squeeze the mouse peritoneal cavity with an alcohol swab to aspirate the culture medium containing feeder cells.

[0209] (3) Preparation of spleen cells. The first immunized Balb / c mouse with a good serum titer was used. Positive blood was collected by gouging out the eyeballs, the mouse was euthanized by cervical dislocation, and then immersed in 75% alcohol. The mouse was then placed ventrally, and its spleen was removed. The spleen was ground using sterile scissors and forceps. 5 mL of preheated GNK washing solution was slowly added to nylon mesh gauze to wash the spleen cells, preparing a single-cell suspension. This suspension was then transferred to a 50 mL centrifuge tube, centrifuged at 1000 rpm for 10 min, the supernatant was discarded, the cell clumps were broken up, and the cells were resuspended for later use.

[0210] (4) Cell fusion and screening of positive hybridoma cells. Select SP2 / 0 tumor cells in logarithmic growth phase, transfer to a 50mL centrifuge bottle, centrifuge, discard the supernatant, break up the cell clumps, add preheated GNK washing solution, mix the prepared spleen cells and tumor cells in the same centrifuge bottle at a ratio of 6:1-10:1, centrifuge, discard the supernatant, and break up the cell clumps. Place in a 37℃ water bath, add 1mL of preheated polymerization agent PEG-1500 while shaking the centrifuge tube, incubate at 37℃ for 5min, add GNK solution to 40mL, centrifuge at 1200rpm for 15min, discard the supernatant, break up the cell clumps, add 40mL of preheated HAT selective medium, mix the cells evenly, add to the culture plate, 100µL / well, and incubate in a 5% CO2, 37℃ incubator.

[0211] (5) When the fused cells in the wells of the culture plate grow to 1 / 3 of the bottom of the well, the supernatant is collected, and positive hybridoma cells are screened by indirect ELISA. Cells with high titer and good sensitivity are selected and transferred to 24-well cell culture plates for expansion culture. The 24-well cell culture plates are pre-coated with feeder cells prepared with HAT medium. When the cells grow to 1 / 2 of the bottom of the well, the supernatant is collected to measure their potency (Table 5).

[0212] Table 5: Titer of the screened monoclonal antibodies

[0213] Five hybridoma cell lines with high titers were obtained through screening, among which AC1-4 had the highest titer.

[0214] (1) Sterile liquid paraffin was injected into the peritoneum of multiparous female Bal b / c mice. Five positive hybridoma cells were cultured separately. When the cells were in the logarithmic growth phase, AC1-4 positive hybridoma cells prepared in Example 2 were injected into the peritoneum. After about 7-10 days, the mice's abdomens were significantly enlarged and felt tight when touched. Ascites fluid could then be collected.

[0215] (2) Centrifuge the preserved ascites fluid and collect the supernatant. Dilute with acetate-sodium acetate buffer 5 times, adjust the pH to 4.5, stir at room temperature for 30 min, and slowly add n-octanoic acid dropwise to a concentration of 25 µL / mL. Centrifuge at 6000 rpm for 30 min, collect the supernatant, add 1 / 10 volume of 10×PBS, mix well, adjust the pH to 7.4, cool to 4℃, add solid ammonium sulfate to a concentration of 0.2778 g / mL, centrifuge at 6000 rpm for 20 min, and discard the supernatant. Resuspend the precipitate in PBS and dialyze at 4℃ for 12 h, changing the medium 2-3 times during this period. Collect the dialysate (purified antibody) and store at -20℃ for later use.

[0216] The purified antibody derived from AC1-4 was detected by electrophoresis, and the results are as follows: Figure 18 As shown, the purified antibody before reduction was a single band, and after reduction it contained two specific bands, which were consistent with the size of the heavy and light chains of IgG. This indicates that this method can effectively purify antibodies secreted by hybridoma cells of strain AC1-4, and it is named monoclonal antibody AC1-4.

[0217] (1) The binding of the purified anti-Aβ oligomer monoclonal antibody to the Aβ oligomer antigen was determined by enzyme-linked immunosorbent assay (ELISA) to verify the biological activity of the monoclonal antibody. The specific operation is as follows: The Aβ oligomer antigen was diluted to 1 μg / mL with carbonate buffer (CB, pH 9.6) and added at a rate of 100 μL / well to a 96-well microplate. The plate was incubated overnight at 4°C. The liquid in the wells was discarded, and 200 μL of blocking buffer (0.02 M PBS, 1% BSA, 5% sucrose) was added to each well. The plate was incubated at 37°C for 2 hours. The liquid in the wells was then discarded, and the plate was air-dried to obtain the antigen-coated plate.

[0218] HRP-labeled goat anti-mouse secondary antibody: A modified sodium periodate method was used. 5 mg of HRP was dissolved in 0.5 mL of purified aqueous solution, and 0.5 mL of freshly prepared 0.1 mol / L NaIO4 was added. The mixture was incubated at 4°C for 30 min, at which point the solution changed from brown to dark green. 0.5 mL of 2.5% ethylene glycol was added, and the reaction was stopped at room temperature in the dark for 30 min. 5 mg of the antibody to be labeled was added, and the pH was adjusted to 9.0 using 1.0 mol / L, pH 9.5 carbonate buffer (CB). The mixture was mixed and incubated overnight at 4°C. 0.2 mL of 5 mg / mL sodium borohydride solution was added, mixed, and incubated at 4°C for 2 hours. Dialysis was performed overnight at 4°C using 0.01 mol / L, pH 7.4 PBS, with three buffer changes. The mixture was centrifuged at 8000 rpm for 10 min to remove the precipitate. The supernatant was collected to obtain the HRP-labeled secondary antibody.

[0219] Different concentrations of anti-Aβ oligomeric monoclonal antibody were added to the antigen-coated plate, incubated at 37°C for 1 hour, and washed 3 times. 100 μL of 1:5000 diluted HRP-labeled secondary antibody was added, incubated at 37°C for 1 hour, and washed 3 times. 100 μL of TMB chromogenic solution was added, incubated at 37°C for 15 minutes, 50 μL of stop solution was added, and the absorbance was measured using a microplate reader (at 450 nm).

[0220] See results Figure 19 As the concentration of anti-Aβ oligomeric monoclonal antibody increases, the OD value gradually rises. The titer of the anti-Aβ oligomeric monoclonal antibody is 0.12 ng / ml.

[0221] (2) The specificity of the anti-Aβ oligomer monoclonal antibody was verified by Western blot.

[0222] a) Electrophoresis gel preparation: Prepare 10% separating gel and 5% stacking gel according to Table 6 and Table 7.

[0223] Table 6: Formulation of 10% Separating Binder

[0224] Table 7: 5% Concentrated Gum Formulation

[0225] b) Sample loading electrophoresis: Mix 40 μl of protein with 10 μl of 5× sample loading buffer to prepare the sample loading solution. Add the protein marker and protein sample sequentially, adding 20 μl of sample solution to each well. Fill the well with electrophoresis buffer, cover the well, and turn on the power. First, use a constant voltage of 70V for electrophoresis for about 30 minutes. After the bromophenol blue indicator enters the separating gel, switch to a constant voltage of 90V. When the indicator reaches about 0.5 cm from the bottom of the gel, turn off the power and remove the gel plate.

[0226] c) Transfer: Wet the PVDF membrane with anhydrous methanol for at least 30 seconds, then rinse with dd H2O for 2 minutes, and immerse in transfer buffer for 5 minutes. Assemble the transfer "sandwich" structure in the following order: black side (negative electrode) → sponge pad → 3 layers of filter paper → gel → transfer membrane → 3 layers of filter paper → sponge pad → red side (positive electrode), removing air bubbles between each layer. Then transfer the sandwich to the electrophoresis tank, black side to black side. Connect the positive and negative electrodes, and place the transfer cassette into the electrophoresis apparatus with the membrane facing the positive electrode. Add transfer buffer, place the electrophoresis apparatus in ice water, and transfer at a constant current of 200 mA for 90 minutes. After the transfer is complete, quickly remove the PVDF membrane and block it with 5% BSA at room temperature for 2 hours. Wash the membrane with TBST for 5 minutes × 3 times.

[0227] d) Blocking: Prepare the blocking solution: Add 2.5g of skim milk powder to 50ml of freshly prepared TBST solution and shake well. Pour some TBST solution onto a cutting plate, place the PVDF membrane on the cutting plate, mark it, and cut the membrane. Add the blocking solution and place the plate on a horizontal shaker. Incubate at room temperature for 60 minutes with shaking. Remove the blocked membrane and rinse the hybridization membrane three times with washing buffer.

[0228] e) Antibody incubation: After blocking the hybridization membrane, add primary antibody and incubate overnight at 4°C. Wash the membrane three times with 1×TBST solution for 10 min each time. Add secondary antibody and incubate at 4°C for 2 h.

[0229] f) Development: Remove the membrane from the TBST buffer, remove excess buffer, and lay it flat on a cardboard sheet with the protein side facing up. Add the prepared working dilution to cover the membrane. Incubate the membrane with the working dilution for 1-5 minutes, ensuring complete surface coverage. Remove excess liquid, wrap the PVDF membrane with plastic wrap, and expose, develop, and fix it with X-ray film in a dark room.

[0230] like Figure 20 Channel 1 represents the Aβ oligomer antigen, channel 2 represents the Aβ monomer antigen, and channel 3 represents the blood sample from an AD patient. The anti-Aβ oligomer monoclonal antibody can recognize both the Aβ oligomer antigen and the Aβ monomer. Furthermore, the results for channel 3 indicate that this antibody can specifically recognize Aβ oligomers in human blood samples.

[0231] 1) Total RNA was extracted from hybridoma cells AC1-4 using the Trizol method, and cNDA was synthesized using the BD SMART™ reverse transcription kit.

[0232] 2) Amplification primers (5'-CTCAGGGAARTARCCYTTGAC-3', SEQ ID NO: 8) were designed based on the constant region sequence of the mouse antibody heavy chain, and the adapter primers in the kit were used for PCR amplification to obtain the heavy chain fragment of the anti-Aβ oligomeric mouse monoclonal antibody, which was then sequenced after the pGEM-T vector was constructed.

[0233] 3) Amplification primers (5'-TCACTGCCATCAATCTTCCAC-3'SEQ ID NO: 9) designed based on the constant region sequence of the mouse antibody light chain and adapter primers in the kit were used for PCR amplification to obtain the light chain fragment of the anti-Aβ oligomeric mouse monoclonal antibody, which was then sequenced after the pGEM-T vector was constructed.

[0234] 4) CDR analysis of anti-Aβ oligomeric mouse monoclonal antibody AC1-4.

[0235] Based on the sequences of the heavy and light chain variable regions of the AC1-4 antibody obtained through sequencing, and referring to the definition method for antibody CDR analysis at http: / / www.bioinf.org.uk / abs / #cdrdef, the CDR sequence of the light chain variable region of the anti-oligomeric mouse monoclonal antibody AC1-4 is shown in Table 8. The CDR sequence of the heavy chain variable region of the anti-Aβ oligomeric mouse monoclonal antibody AC1-4 is shown in Table 9.

[0236] Table 8: CDR sequence of the light chain variable region of anti-Aβ oligomeric mouse monoclonal antibody AC1-4

[0237] Table 9: CDR sequence of the heavy chain variable region of anti-Aβ oligomeric mouse monoclonal antibody AC1-4

[0238] 5) Obtain the sequence of the humanized monoclonal antibody AC1-4. The frame regions (HFR1, HFR2, HFR3, HFR4) of the heavy chain variable region and the frame regions (LFR1, LFR2, LFR3, LFR4) of the obtained anti-Aβ oligomeric mouse monoclonal antibody AC1-4 were compared with the IgBlast database (IMGT human Vgenes (F+ORF+in-frameP)) to obtain the human FR region with the highest homology. Further consideration was given to the amino acid domains of the human frame region to determine the human frame region sequence. This sequence was then combined with the light chain variable region CDR and heavy chain variable region CDR of the anti-Aβ oligomeric mouse monoclonal antibody AC1-4 to obtain the following: The humanized anti-Aβ oligomeric monoclonal antibody HAC1-4 light chain variable region is shown in SEQ ID NO: 10.

[0239] The humanized anti-Aβ oligomeric monoclonal antibody HAC1-4 heavy chain variable region is shown in SEQ ID NO: 11.

[0240] DVVMTQSPLSLPVTPGEPASISCRSAQSILYSKTLHWLLQKPGQSPQRLIYAVSNRQSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCKQSTHTPRTFGGGTKVEIKRTVAAPSV (SEQ ID NO: 10) QVQLVESGGGVVQPGRSLRLSCAASGFAFSRYSMHWVRQAPGKGLEWVAQIWFGDGKKYYSDTVKGRFTISRDNSKNTLYLQMNTLRAEDTAVYYCARDHYIGSSDVWGKGTTVTVSSAS (SEQ ID NO: 11) The light chain variable region and heavy chain variable region of the murine anti-Aβ oligomeric murine monoclonal antibody AC1-4 differ from those of the humanized anti-Aβ oligomeric murine monoclonal antibody HAC1-4 only in the murine frame region; the CDR remains unchanged. The frame region in the humanized antibody can be replaced with the murine one using conventional techniques to obtain the light chain variable region and heavy chain variable region of the murine anti-Aβ oligomeric murine monoclonal antibody AC1-4.

[0241] Example 5: Preparation of humanized anti-Aβ oligomeric mouse monoclonal antibody HAC1-4.

[0242] 1. Construction of the eukaryotic expression vector: The variable region of the humanized anti-Aβ oligomeric mouse monoclonal antibody HAC1-4 was linked to the human IgG constant region (Fc) gene to form the full-length heavy and light chains. This was then recombined into the eukaryotic expression vector pcDNA3.1 via corresponding restriction enzyme sites.

[0243] Specifically, the vector expressing the HAC1-4 light chain is obtained by replacing the BamHI and EcoRI restriction sites of the eukaryotic expression vector pcDNA3.1 with a DNA molecule composed of the variable region of the humanized anti-Aβ oligomeric mouse monoclonal antibody HAC1-4 light chain and the constant region of the human IgG light chain gene; this vector expresses the monoclonal antibody HAC1-4 light chain. The nucleotide sequence of the DNA molecule composed of the humanized anti-Aβ oligomeric mouse monoclonal antibody HAC1-4 light chain variable region and the human light chain constant region gene is composed of SEQ ID NO:12 and SEQ ID NO:13 (human IgG light chain constant region gene); Light chain variable region: GATGTGGTGATGACCCAGAGCCCGCTGAGCCTGCCGGTGACCCCGGGCGAACCGGCGAGCATTAGCTGCCGCAGCGCGCAGAGCATTCTGTATAGCAAAACCCTGCATTGGCTGCTGCAGAAACCGGGCCAGAGCCCGCAGCGCCTGATTTATGCGGTGAGCAACCGCCAGAGCGGCGTGCCGGATCGCTTTAGCGGCAGCGGCAGCGGCACCGATTTTACCCTGAAAATTAGCCGCGTGGAAGCGGAAGATGTGGGCGTGTATTATTGCAAACAGAGCACCCATACCCCGCGCACCTTTGGCGGCGGCACCAAAGTGGAAATTAAACGCACCGTGGCGGCGCCGAGCGTG (SEQ ID NO: 12) Light chain constant region: TTTATTTTTCCGCCGAGCGATGAACAGCTGAAAAGCGGCACCGCGAGCGTGGTGTGCCTGCTGAACAACTTTTATCCGCGCGAAGCGAAAGTGCAGTGGAAAGTGGATAACGCGCTGCAGAGCGGCAACAGCCAGGAAAGCGTGACCGAACAGGATAGCAAAGATAGCACCTATAGCCTGAGCAGCACCCTGACCCTGAGCAAAGCGGATTATGAAAAACATAAAGTGTATGCGTGCGAAGTGACCCATCAGGGCCTGAGCAGCCCGGTGACCAAAAGCTTTAACCGCGGCGAATGC(SEQ ID NO: 13) The amino acid sequence of the light chain of the monoclonal antibody HAC1-4 consists of SEQ ID NO: 10 and SEQ ID NO: 14 (human IgG light chain constant region).

[0244] Light chain constant region: FIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC (SEQ ID NO: 14) The vector expressing the HAC1-4 heavy chain antibody was obtained by replacing the BamHI and EcoRI restriction sites of the eukaryotic expression vector pcDNA3.1 with a DNA molecule composed of the variable region of the humanized anti-Aβ oligomeric mouse monoclonal antibody HAC1-4 heavy chain and the constant region of the human IgG heavy chain gene; this vector expresses the monoclonal antibody HAC1-4 heavy chain. The nucleotide sequence of the DNA molecule composed of the humanized anti-Aβ oligomeric mouse monoclonal antibody HAC1-4 heavy chain variable region and the human IgG heavy chain constant region gene is composed of SEQ ID NO: 15 and SEQ ID NO: 16 (human IgG heavy chain constant region gene); Heavy chain variable region: CAGGTGCAGCTGGTGGAAAGCGGCGGCGGCGTGGTGCAGCCGGGCCGCAGCCTGCGCCTGAGCTGCGCGGCGAGCGGCTTTGCGTTTAGCCGCTATAGCATGCATTGGGTGCGCCAGGCGCCGGGCAAAGGCCTGGAATGGGTGGCGCAGATTTGGTTTGGCGATGGCAAAAAATATTATAG CGATACCGTGAAAGGCCGCTTTACCATTAGCCGCGATAACAGCAAAAACACCCTGTATCTGCAGATGAACACCCTGCGCGCGGAAGATACCGCGGTGTATTATTGCGCGCGCGATCATTATATTGGCAGCAGCGATGTGTGGGGCAAAGGCACCACCGTGACCGTGAGCAGCGCGAGC(SEQ ID NO: 15) Heavy chain constant region: ACCAAAGGCCCGAGCGTGTTTCCGCTGGCGCCGAGCAGCAAAAGCACCAGCGGCGGCACCGCGGCGCTGGGCTGCCTGGTGAAAGATTATTTTCCGGAACCGGTGACCGTGAGCTGGAACAGCGGCGCGCTGACCAGCGGCGTGCATACCTTTCCGGCGGTGCTGCAGAGCAGCGGCCTGTATAGCCTGAGCAGCGTGGTGACCGTGCCGAGCAGCAGCCTGGGCACCCAGACCTATATTTGCAACGTGAACCATAAACCGAGCAACACCAAAGTGGATAAAAAAGTGGAACCGAAAAGCTGCGATAAAACCCATACCTGCCCGCCGTGCCCGGCGCCGGAACTGCTGGGCGGCCCGAGCGTGTTTCTGTTTCCGCCGAAACCGAAAGATACCCTGATGATTAGCCGCACCCCGGAAGTGACCTGCGTGGTGGTGGATGTGAGCCATGAAGATCCGGAAGTGAAATTTAACTGGTATGTGGATGGCGTGGAAGTGCATAACGCGAAAACCAAACCGCGCGAAGAACAGTATAACAGCACCTATCGCGTGGTGAGCGTGCTGACCGTGCTGCATCAGGATTGGCTGAACGGCAAAGAATATAAATGCAAAGTGAGCAACAAAGCGCTGCCGGCGCCGATTGAAAAAACCATTAGCAAAGCGAAAGGCCAGCCGCGCGAACCGCAGGTGTATACCCTGCCGCCGAGCCGCGAAGAAATGACCAAAAACCAGGTGAGCCTGACCTGCCTGGTGAAAGGCTTTTATCCGAGCGATATTGCGGTGGAATGGGAAAGCAACGGCCAGCCGGAAAACAACTATAAAACCACCCCGCCGGTGCTGGATAGCGATGGCAGCTTTTTTCTGTATAGCAAACTGACCGTGGATAAAAGCCGCTGGCAGCAGGGCAACGTGTTTAGCTGCAGCGTGATGCATGAAGCGCTGCATAACCATTATACCCAGAAAAGCCTGAGCCTGAGCCCGGGCAAA (SEQ ID NO: 16)。 The amino acid sequence of the above-mentioned monoclonal antibody HAC1-4 heavy chain consists of SEQ ID NO: 11 and SEQ ID NO: 7 (human IgG heavy chain constant region). Heavy chain constant region: TKGPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTQTYICNVNHKPSNTKVDKKVEPKSCDKTHTCPPCPAPELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVE VHNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVYTLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK (SEQ ID NO: 7) Light chain = variable region of light chain + constant region of light chain; Heavy chain = variable region of heavy chain + constant region of heavy chain 2. Eukaryotic Antibody Expression: The vectors for expressing the HAC1-4 light chain and heavy chain of the antibody prepared in step 1 were linearized and digested with EcoRI enzyme. The linearized vectors for expressing the HAC1-4 light chain and heavy chain of the antibody were then used together to transfect CHO-K1-38 cells (Nanjing Genscript Biotech Co., Ltd., M00553) using a BioRadXcell electroporator at 300V and 900μF. 48 hours after transfection, the culture medium was replaced with antibiotic-containing medium, and the medium was changed every 2 days for 2 weeks. The culture medium was then collected.

[0245] The cell culture supernatant was collected by centrifugation of the culture medium, and the antibody in the supernatant was detected by indirect ELISA: The collected cell supernatant was added to the microplate coated with Aβ oligomer antigen, and incubated at 37°C for 1 h. After washing the plate, HRP-labeled secondary antibody was added, and the plate was incubated at 37°C for 1 h. After washing the plate, the substrate TMB was added for color development, and sulfuric acid was added to stop the reaction. The absorbance was measured by microplate reader (at 450 nm).

[0246] The results are shown in Table 10 below. It can be seen that the supernatant of cells with different numbers all contained AC1-4 monoclonal antibody.

[0247] Table 10: Antibody Detection Results

[0248] 3. Purification of humanized anti-Aβ oligomeric monoclonal antibody HAC1-4. Affinity purification was performed using Magrose Protein A antibody purification magnetic beads from Beaver Biotechnology.

[0249] (1) Pretreatment of magnetic beads: Vortex the purified antibody magnetic beads for 30 s to fully resuspend them; take 10% (v / v) of the magnetic bead suspension and place it in another new 1.5 mL centrifuge tube. Perform magnetic separation on the magnetic bead suspension, discard the supernatant, wash twice with Binding / Washing buffer, and perform magnetic separation. The magnetic beads in the tube can be used directly for protein separation.

[0250] Note: The amount of magnetic beads used in this step can be adjusted according to the maximum binding amount of the magnetic beads to the target protein. When the concentration of the target protein is greater than 150µg / mL, the amount of magnetic beads can be 1.2-1.5 times. If the concentration of the target protein is too low, such as below 70µg / mL, the amount of magnetic beads can be increased to improve the protein recovery rate, such as increasing it to 3 times.

[0251] (2) Protein adsorption: Add the pretreated magnetic beads to 1 ml of sample solution, shake well, place in a reverse mixer and mix overnight at 2-8℃ to promote full contact and adsorption between the sample and the magnetic beads. Collect the magnetic beads by magnetic separation and discard the supernatant.

[0252] (3) Magnetic bead washing: Add 1 mL Binding / Washing buffer to the centrifuge tube, shake to resuspend the magnetic beads, and then perform magnetic separation. Discard the supernatant. Repeat this operation 3 times.

[0253] (4) Protein elution: Add 1 mL of Elution buffer to the centrifuge tube that has been washed with magnetic beads, quickly resuspend it by pipetting or vortexing, and then place it in a centrifuge at room temperature (about 25°C) or gently rotate the centrifuge tube manually. After rotating for 10 min, perform magnetic separation and collect the supernatant into a new centrifuge tube.

[0254] Precautions: For this step, it is recommended to use elution buffer to achieve a final protein concentration of 0.6-1.2 mg / mL. At this concentration, more than 95% of the protein in the first elution will be eluted. If the amount of elution buffer is too small, some protein will remain on the magnetic beads during the first elution, resulting in a decrease in protein recovery.

[0255] (5) Neutralize pH: Add a certain amount of Neutrilization buffer to the protein elution solution in the above steps, generally 1 / 10 of the elution volume, so that the pH of the eluted protein is kept in a neutral environment, which is conducive to maintaining the biological activity of the protein and avoiding protein inactivation.

[0256] (6) Centrifugation: Centrifuge at 12000 rpm and 4℃ for 10 min, and collect the supernatant. The purified humanized anti-Aβ oligomeric monoclonal antibody HAC1-4 is obtained.

[0257] The purified humanized anti-Aβ oligomeric monoclonal antibody HAC1-4 was sequenced, and it was found to contain HAC1-4 monoclonal antibody. The light chain and heavy chain sequences of the HAC1-4 monoclonal antibody were consistent with the light chain and heavy chain sequences of the aforementioned HAC1-4, proving that the expressed antibody was HAC1-4 monoclonal antibody.

[0258] Example 6: Detection of the biological activity of humanized and murine anti-Aβ oligomeric monoclonal antibodies HAC1-4 / AC1-4 The difference in binding specificity of the purified humanized anti-Aβ oligomer monoclonal antibody HAC1-4 obtained above to the Aβ oligomer antigen was determined by enzyme-linked immunosorbent assay (ELISA) to verify the biological activity of the humanized monoclonal antibody. The specific procedures are as follows: 1) Dilute the Aβ oligomer antigen to 1 μg / mL with sodium bicarbonate buffer (pH 9.5), coat the ELISA plate, block and wash, and air dry for later use. 2) Add 100 μL of the purified humanized anti-Aβ oligomeric monoclonal antibody HAC1-4 obtained above (diluted to 2 μg / ml, with dilution buffer of pH 7.4, 0.02 mol / L PBS buffer), incubate, and wash; 3) Add 100 μL of HRP-labeled goat anti-human IgG antibody, incubate, and then wash; 4) After adding the substrate TMB for color development, add sulfuric acid to stop the reaction, and measure the absorbance using an ELISA reader (at 450 nm).

[0259] 5) The mouse monoclonal antibody AC1-4 and HRP-labeled goat anti-mouse IgG were used as controls to compare the differences in the measured values ​​of the two antibodies.

[0260] See test results Figure 21 It can be seen that the humanized monoclonal antibody HAC1-4 and the mouse monoclonal antibody AC1-4 have the same specificity for the Aβ oligomer antigen, indicating that the humanized monoclonal antibody maintains its biological activity against the Aβ oligomer antigen.

[0261] Example 7 Reagent Kit I. Preparation of the kit: A magnetic microparticle chemiluminescence kit was prepared using HAC1-4 antibody. The components include: HAC1-4 antibody magnetic beads, enzyme conjugate, calibrators / quality control products, washing buffer and luminescence solution.

[0262] 1) Preparation of HAC1-4 antibody magnetic beads: Take 10 mg of magnetic beads, add 1 ml of MES buffer (0.1 M, pH 5.0), wash once, then add 10 μl of 10 mg / ml EDC for activation. Activate at room temperature for 30 min. Add 0.2 mg of the humanized Aβ oligomeric antibody HAC1-4 prepared in Example 5, mix and react at room temperature for 2 hours, add 1 ml of 1% BSA, block at room temperature for 2 hours, wash 3 times with PBS containing 0.01% Tween 20, and finally store in 1 ml of PBS containing 1% BSA.

[0263] 2) Preparation of enzyme conjugates: A modified sodium periodate method was used. Dissolve 5 mg of HRP in 1 ml of 0.2 mol / L, pH 5.6 acetate buffer, add 0.1 ml of 1% DNFB anhydrous ethanol solution, and stir gently at room temperature for 1 h; add 0.5 mL of freshly prepared 0.1 mol / L NaIO4, at which point the solution changes from brown to dark green, and incubate at 4 °C for 30 min, at which point the solution changes from brown to dark green; add 1 ml of 2.5% ethylene glycol, and stir gently at room temperature for 1 h to terminate the reaction; add 10 mg of the humanized Aβ oligomer antibody HAC1-4 prepared in Example 5, and adjust the pH to 9.0 with 1.0 mol / L pH 9.5 CB; mix well and incubate at 4 °C overnight; add 0.1 ml of sodium borohydride solution, mix well, and incubate at 4 °C for 3 h; dialyze overnight at 4 °C with 0.01 mol / L, pH 7.4 PBS, changing the medium 3 times; centrifuge at 3000 r / min for 30 min to remove the precipitate; collect the supernatant. Dilute the solution 1:2000 to obtain an enzyme conjugate solution.

[0264] 3) Preparation of calibrators / quality control samples: The calibrator diluent is a 0.02M PBS buffer at pH 7.4 containing 1% BSA and 0.02% (v / v) Proclin-300. This is used as the zero calibrator (S0).

[0265] Calibrator solutions S1 to S5 are solutions containing different concentrations of Aβ oligomeric antigen, diluted with calibrator diluent. The concentrations are 31.25 pg / mL, 62.5 pg / mL, 125 pg / mL, 250 pg / mL, and 500 pg / mL, respectively.

[0266] The quality control solutions QC1 and QC2 are solutions containing different concentrations of Aβ oligomeric antigen, diluted with calibrator diluent. The concentrations are 62.5 pg / mL and 250 pg / mL, respectively.

[0267] 4) Preparation of washing solution: Weigh 2.9g disodium hydrogen phosphate dodecahydrate, 0.3g sodium dihydrogen phosphate dihydrate, 8.5g sodium chloride, and 0.5ml Tween 20, and add purified water to make up to 1L.

[0268] 5) The luminescent liquid was purchased from Solarbio, product number PE0010.

[0269] The above components are assembled into a kit.

[0270] II. Kit Usage: The Aβ oligomer detection kit prepared above should be used in conjunction with a fully automated chemiluminescence analyzer. The detection procedure is as follows: 1) Take 50 μl of antibody magnetic beads, apply a magnetic field to adsorb the magnetic beads, and remove the magnetic bead preservation solution.

[0271] 2) Take 50 μl of sample, mix it with magnetic beads, and incubate at 37 degrees Celsius for 15 min.

[0272] 3) Apply a magnetic field to adsorb the magnetic beads, remove the sample solution after the reaction, and wash the magnetic beads once with 200μl of washing solution.

[0273] 4) Take 50 μl of the enzyme conjugate, mix it with the magnetic beads, and incubate at 37 degrees Celsius for 15 min.

[0274] 5) Apply a magnetic field to adsorb the magnetic beads, remove the enzymes after the reaction, and wash the magnetic beads twice with 200μl of washing solution.

[0275] 6) Take 50 μl each of luminescent solutions A and B, mix them with magnetic beads, incubate at 37 degrees Celsius for 3 min, and detect the luminescence value.

[0276] III. Specific Recognition of Aβ Oligomers: Aβ polypeptide monomers and Aβ oligomer antigens were diluted to equimolar concentrations using a series of gradients. The prepared kit was then used in conjunction with a fully automated chemiluminescence analyzer for detection. Results are as follows: Figure 22 ;from Figure 22 It can be seen that as the molar concentration of the antigen increases, the detection signal of the Aβ oligomer antigen increases in a gradient, but the detection signal of the Aβ monomer remains low and flat, indicating that the kit established in this invention can specifically recognize the Aβ oligomer but not the monomer.

[0277] IV. Performance indicators of the reagent kit (blank limit, repeatability, specificity) Table 11: Performance Indicators of the Aβ Oligomer Detection Kit

[0278] 1) Blank limit Using the kit prepared above, a zero-concentration calibrator was used as a sample for detection. The measurement was repeated 20 times to obtain the absorbance values ​​(A values) of the 20 measurements. The average value (M) and standard deviation (SD) were calculated to obtain the A value corresponding to M+2SD. Based on the dose-response curve of the calibrator used in the kit, the A value corresponding to M+2SD was substituted into the curve to obtain the corresponding concentration value, which is the blank limit.

[0279] 2) Repeatability The quality control samples (Qc1, Qc2) were tested using the kits prepared above, with each test repeated 10 times. The mean concentration (Mean) and standard deviation (SD) of the test results were calculated.

[0280] Coefficient of variation (CV) = SD / Mean × 100%.

[0281] 3) Specificity Using a 500 pg / ml Aβ1-42 peptide solution as a specific sample, the above-prepared kit was used for detection. The detection was repeated twice, and the average value (M) of the results was calculated.

[0282] Cross-reactivity rate = M / 500 × 100%.

[0283] The results of the above performance indicators are shown in Table 11.

[0284] Example 8: Detection and verification of Aβ oligomers in peripheral blood and brain tissue of AD model mice 1. Sample Preparation: Three 6-month-old male APP / PS1 AD model mice and three wild-type control mice (purchased from Beijing Huafukang Biotechnology Co., Ltd.) were selected. Blood was collected from the heart after anesthesia, and anticoagulant was added. The mice were centrifuged, and the supernatant plasma was collected. Simultaneously, brain tissue was removed, and 1 mL of RIPA strong lysis agent (containing protease inhibitors and phosphatase inhibitors) was added to the brain tissue. The tissue was homogenized using a TissueLyser II tissue homogenizer at a frequency of 30 Hz for 8 min, centrifuged at 14,000 rpm at 4℃ for 30 min, and the supernatant was collected as brain tissue homogenate.

[0285] 2. Detection: Using the aforementioned detection kit and procedure, Aβ oligomers in the plasma and brain tissue homogenates of AD mice and control mice were detected, respectively. The levels of Aβ oligomers in the peripheral blood and brain tissue of AD model mice should be significantly higher than those in normal mice. The detection results were consistent with expectations. Figure 23 , Figure 24 As shown.

[0286] Serum samples were collected from the hospital, including 40 healthy controls and 20 samples clinically diagnosed with Alzheimer's disease (AD). The samples were tested using the detection kit and procedure prepared in Example 5. The results are shown in Tables 12, 13, and 14. Figure 25 : Table 12: Aβ oligomer detection results in normal individuals (pg / ml)

[0287] Table 13: Aβ oligomer detection results in AD patients (pg / ml)

[0288] Table 14: Statistical results (pg / ml) of normal individuals and AD samples

[0289] The results above show a significant difference between the normal group and the AD group (P<0.01), and the test results can distinguish between the two groups. The kit prepared using this method can be used for screening for cognitive impairment risk.

[0290] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any form or substance. It should be noted that those skilled in the art can make various improvements and additions without departing from the method of the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention. Any modifications, alterations, and equivalent changes made by those skilled in the art based on the above-disclosed technical content without departing from the spirit and scope of the present invention are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, and evolutions made to the above embodiments based on the essential technology of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. Use of biomarkers in the preparation of risk assessment products for mild cognitive impairment, wherein the biomarkers include Aβ oligomers.

2. The use as described in claim 1, characterized in that, It also includes one or more of the following features: a) The Aβ oligomer is a soluble Aβ dimer to dodecamer; preferably, the Aβ oligomer includes at least the Aβ1-42 fragment; b) The biomarkers also include the APOEε4 gene and multidomain risk factors; the multidomain risk factors include demographic and clinical indicators related to the risk of mild cognitive impairment; preferably, the demographic and clinical indicators related to the risk of mild cognitive impairment include one or more of age, sex, years of education, body mass index, history of alcohol consumption, family history of dementia, hypertension, diabetes, hyperlipidemia and depressive state; c) The mild cognitive impairment risk assessment product is selected from models, systems, reagents, kits, chips, or test strips; preferably, the reagents and / or kits include reagents that specifically recognize Aβ oligomers; more preferably, the reagents that specifically recognize Aβ oligomers are selected from antibodies that specifically recognize Aβ oligomers or their antigen-binding fragments, nucleic acid aptamers, peptides, or small molecule compounds; More preferably, the antibody has an L chain of CDR1, CDR2, and CDR3, wherein the amino acid sequence of CDR1 is shown in SEQ ID NO.1; the amino acid sequence of CDR2 is shown in SEQ ID NO.2; and the amino acid sequence of CDR3 is shown in SEQ ID NO.

3. The H chain has CDR1, CDR2, and CDR3, wherein the amino acid sequence of CDR1 is shown in SEQ ID NO.4; the amino acid sequence of CDR2 is shown in SEQ ID NO.5; and the amino acid sequence of CDR3 is shown in SEQ ID NO.

6. d) The Aβ oligomers are derived from serum.

3. A mild cognitive impairment risk assessment system, characterized in that, The system includes: A data acquisition module is used to acquire the test data of the subject, wherein the test data includes data of the biomarkers according to any one of claims 1-2; The risk assessment module is communicatively connected to the data acquisition module and is used to take the detection data from the data acquisition module as input, and output the risk probability value of the subject having mild cognitive impairment through a pre-trained machine learning model. The result output module is communicatively connected to the risk assessment module and is used to compare the risk probability value with a preset threshold and output risk classification information based on the comparison result.

4. The mild cognitive impairment risk assessment system as described in claim 3, characterized in that, It also includes one or more of the following features: a. The machine learning model is a non-linear machine learning model; preferably, the machine learning model is one or more of the following: decision tree, support vector machine, random forest, lightweight gradient booster, extreme gradient booster, k-nearest neighbor algorithm, multilayer perceptron classifier, and Gaussian Naive Bayes classifier model; more preferably, it is a random forest model; b. In the result output module, when the risk probability value is not less than a preset threshold, "non-low risk" classification information is output; When the risk probability value is less than a preset threshold, "low risk" classification information is output; preferably, the preset threshold ranges from 0.28 to 0.31; more preferably, the preset threshold is 0.2920; c. The system further includes a data preprocessing module, which is communicatively connected to the data acquisition module, for preprocessing the raw data of the detection data so that the detection data can be used as input to the risk assessment module; preferably, the preprocessing includes imputing missing values ​​in the raw data of the detection data, and / or standardizing the continuous feature data in the raw data to generate a standardized feature vector; d. The machine learning model is trained based on a training dataset, wherein the data for each training sample includes: data of the biomarkers described in any one of claims 1-2, and a binary label characterizing whether the individual corresponding to the training sample has mild cognitive impairment.

5. The mild cognitive impairment risk assessment system as described in claim 3, characterized in that, In the result output module, the preset threshold includes a first preset threshold and a second preset threshold, wherein the first preset threshold is less than the second preset threshold; When the risk probability value is less than the first preset threshold, output "low risk" classification information; When the risk probability value is greater than the second preset threshold, output "high risk" classification information; When the risk probability value is not less than a first preset value and not greater than a second preset threshold, "medium risk" classification information is output; preferably, the value range of the first preset threshold is 0.28 to 0.31, and the value range of the second preset threshold is 0.40 to 0.44; more preferably, the first preset threshold is 0.2920, and the second preset threshold is 0.4251.

6. A method for assessing the risk of mild cognitive impairment, characterized in that, The method includes the following steps: S1, acquire the test data of the subject, the test data including data of the biomarkers according to any one of claims 1-2; S2, taking the detection data mentioned in step S1 as input, the pre-trained machine learning model outputs a risk probability value indicating that the subject has mild cognitive impairment; S3, compare the risk probability value with a preset threshold, and output risk classification information based on the comparison result.

7. A method for constructing a risk assessment model for mild cognitive impairment, comprising at least the following steps: SS1, Obtain training sample data, the training sample data including: Data of the biomarkers according to any one of claims 1-2, and a binary label characterizing whether the individual corresponding to the training sample has mild cognitive impairment; SS2 uses the training sample data obtained from SS1 to build a machine learning model.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the mild cognitive impairment risk assessment method of claim 6, and / or the method for constructing a mild cognitive impairment risk assessment model of claim 7.

9. A computer processing apparatus, comprising a processor and a computer-readable storage medium as described in claim 8, characterized in that, The processor executes a computer program on the computer-readable storage medium to implement the steps of the mild cognitive impairment risk assessment method of claim 6, and / or the method for constructing a mild cognitive impairment risk assessment model of claim 7.

10. An electronic terminal, characterized in that, include: Processor, memory, and communication unit; The memory is used to store computer programs, the communicator is used to communicate with external devices, and the processor is used to execute the computer programs stored in the memory, so that the terminal executes the method for assessing the risk of mild cognitive impairment as described in claim 6, and / or the method for constructing a risk assessment model for mild cognitive impairment as described in claim 7.

Images