ALDH4A1-based plaque property classification method for carotid artery stenosis patient

Abstract

The invention discloses a carotid artery stenosis patient plaque property classification method based on ALDH4A1, and relates to the technical field of cerebrovascular diseases, and the method comprises the steps: S1, obtaining an atherosclerosis plaque tissue sample of a carotid artery stenosis patient; s2, planning types of atherosclerotic plaque tissue properties; s3, setting a threshold value of atherosclerotic plaque tissue property classification; s4, detecting the expression level of ALDH4A1 protein in the atherosclerotic plaque tissue sample; s5, classifying atherosclerotic plaque tissue properties based on the expression level of ALDH4A1 protein in the detection sample; and S6, verifying the result of the atherosclerotic plaque tissue property classification. According to the method, the threshold value of plaque property classification can be dynamically generated according to the clinical information of the patient, plaques are classified in combination with the physiological state of the patient, the accuracy of the plaque classification result is further improved by verifying the result after plaque classification, and a basis is provided for later treatment and intervention strategies.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of cerebrovascular diseases, and in particular to a carotid stenosis patient plaque property classification method based on ALDH4A1. BACKGROUND

[0002] Carotid stenosis is an important risk factor for cerebrovascular diseases, which has brought significant burden to patients and society. At present, the diagnosis and treatment of carotid stenosis mainly rely on imaging examination and interventional treatment. However, there are still certain limitations in the existing methods in terms of early diagnosis and prognosis evaluation. Therefore, it is necessary to carry out in-depth research on carotid stenosis. It has been found in previous studies that the stability of plaque is closely related to the structural characteristics. Previous studies have shown that erosive plaque is significantly correlated with increased risk of cerebrovascular events. Although there have been many studies on the characteristics of plaque and its influence on cerebrovascular events, the mechanism of erosive plaque and its biomarkers are still relatively scarce.

[0003] Recent studies have shown that erosive plaque is a unique pathological phenotype, which is characterized by rich neutrophil extracellular traps and poor lipid, and complete fibrous cap. Unlike macrophage apoptosis-dominant vulnerable plaque, erosive plaque shows endothelial activation, CD4+ T cell infiltration and immune inflammation. ALDH4A1, as an important enzyme, is related to various metabolic processes, and its expression level is regulated by inflammatory response. Studies have shown that ALDH4A1 plays an important role in plaque formation and stability. In addition, by analyzing the immune response mechanism of ALDH4A1, new targets for the treatment of cardiovascular and cerebrovascular diseases can be provided.

[0004] The application provides a carotid stenosis patient plaque property classification method based on ALDH4A1, which will not only help to deeply understand the pathological mechanism of carotid stenosis, but also provide new biomarkers and therapeutic targets for the diagnosis and intervention strategy of plaque. SUMMARY

[0005] The application provides a carotid stenosis patient plaque property classification method based on ALDH4A1, which solves the technical problem of providing new biomarkers and therapeutic targets for the intervention strategy by diagnosing the plaque property.

[0006] To solve the above technical problems, the application provides the following technical scheme: The application provides a carotid stenosis patient plaque property classification method based on ALDH4A1, comprising: S1: obtaining an atherosclerotic plaque tissue sample of a carotid stenosis patient; S2: planning the types of atherosclerotic plaque tissue properties; S3: Set the threshold for classifying the tissue properties of atherosclerotic plaques; S4: Detect the expression level of ALDH4A1 protein in atherosclerotic plaque tissue samples; S5: Based on the expression level of ALDH4A1 protein in the detected samples, the tissue properties of atherosclerotic plaques are classified; S6: Validate the results of the tissue nature classification of atherosclerotic plaques.

[0007] The beneficial effects of the technical solution provided by this invention include at least the following: This invention can dynamically generate thresholds for plaque classification based on the patient's clinical information, accurately classify plaques by combining the patient's own physiological state, and further improve the accuracy of plaque classification results by verifying the classification results, thus providing a basis for subsequent treatment and intervention strategies. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart of a method for classifying plaque characteristics in patients with carotid artery stenosis based on ALDH4A1, provided in an embodiment of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0011] Example A method for classifying plaque characteristics in patients with carotid artery stenosis based on ALDH4A1.

[0012] Please refer to Figure 1 This is a flowchart of a method for classifying plaque characteristics in patients with carotid artery stenosis based on ALDH4A1, provided in an embodiment of the present invention.

[0013] S1 is used to obtain atherosclerotic plaque tissue samples from patients with carotid artery stenosis; Atherosclerotic plaque tissue samples were obtained through carotid endarterectomy, and the plaque tissue samples were separated using non-pyrogenic instruments. It should be noted that non-thermal instruments can include, for example, cold scalpels, microscissors, and toothless forceps.

[0014] After plaque tissue separation, the anatomical location information of the plaque is recorded, including the orientation of the luminal surface and the identification of key areas; It should be noted that the key area identification should at least select one of the following: the plaque shoulder, the thinnest part of the fibrous cap, or the lipid core area; Plaque tissue samples were aliquoted within 5 minutes of being excised and then preserved by quick-freezing in liquid nitrogen at -80°C.

[0015] S2 defines the types of tissue properties of atherosclerotic plaques; The plaque tissue properties were divided into stable plaque group and unstable plaque group, and the unstable plaque group was further divided into erosive plaque group and ulcerative plaque group; The classification criteria for stable plaques are as follows: the thinnest part of the fibrous cap must be ≥150μm thick and the blue staining area of ​​Masson staining must be >80%. The classification criteria for erosive plaques are as follows: CD31 staining shows endothelial discontinuity and CD62p positive layer thickness ≥50μm. The classification criteria for ulcerative plaques are as follows: EVG staining confirms elastic lamina rupture and CD34 positive exposed collagen.

[0016] S3 sets the threshold for classifying the tissue properties of atherosclerotic plaques; The threshold setting is dynamically generated using a pre-trained threshold probability model. The threshold generation steps include receiving input patient clinical information, establishing a pre-trained threshold decision model, and calculating an adaptive threshold for plaque nature classification. Patient clinical information includes patient age, ischemic stroke history, and duration of statin use; It should be noted that patient clinical information can also include chronic diseases such as diabetes and hypertension as parameters input into the model.

[0017] The pre-trained threshold decision model takes ALDH4A1 expression value and patient clinical information as input and outputs a binary classification probability. The adaptive threshold is calculated based on the current patient's clinical information vector, using a pre-trained threshold decision model to obtain the ALDH4A1 critical value, as shown in the following formula:

[0018] In the formula, This represents the probability of the model outputting a stable patch. The critical value representing the stability of a plaque; This represents a vector of the patient's clinical information.

[0019] It should be noted that the adaptive threshold is adjusted according to the patient's own situation. If the patient's age is >55, the threshold for stable plaques is lowered by 5%. If the patient has an ischemic stroke within 12 months, the threshold for stable plaques is lowered by 10%. If the patient has been taking statin treatment for >6 months, the threshold for stable plaques is lowered by 15%. The adaptive threshold adjustments can be cumulative.

[0020] The model is based on plaque histopathological characteristics and incorporates ischemic stroke recurrence events within 12 months post-surgery to determine the true value of plaque stability. A regularization term is added to the loss function to suppress variance caused by batch effects of the detection platform during training, as shown in the following formula:

[0021] In the formula, Represents the loss function. Represents the binary cross-entropy loss. , These represent the true label value and the model prediction value, respectively. This represents the weight coefficient of the regularization term, used to control the degree of influence of the regularization term on the total loss. This indicates the batch size, which is the number of samples used in each training iteration. This represents the feature value of the j-th sample in the batch. It represents the average of all sample feature values ​​in the quantity.

[0022] S4: Detect the expression level of ALDH4A1 protein in atherosclerotic plaque tissue samples; The steps for detecting the expression level of ALDH4A1 protein include multiplex immunofluorescence localization and ELISA detection; Multiplex immunofluorescence localization was performed by co-localization detection of ALDH4A1 in the plaque area, T-bet in Th1 cells, CD68 in macrophages, and SMA staining in smooth muscle. It should be noted that during the detection process, the regulatory role of dendritic cells (DCs) on Th1 differentiation can be analyzed using a dual-source co-culture model (Fig. E). Specifically, this involves using CD1 cells isolated from human carotid artery plaques... + CD11c + DCs (flow cytometry-sorted purity >98%) were used in experimental group 1, consisting of autologous peripheral blood CD11c. + CD303 - HLA-DR + Myeloid dendritic cells (excluding plasmacytoid dendritic cells) were in experimental group 2, along with autologous naïve CD4. + T cells (Th0: CD45RA) + CD45RO - CD25 -(Purity >99%) cells were co-cultured at a 1:5 ratio for 72 hours. After co-culture, the cells were stimulated with PMA (50 ng / ml) + Ionomycin (1 μg / ml) + BFA (10 μg / ml) for 6 hours. IFN-γ was then quantitatively detected by flow cytometry. + Th1 cell proportion.

[0023] ELISA detection involves preparing standards, adding samples for incubation, adding chromogenic reagent, and then using a multi-functional microplate reader to read the absorbance value at a detection wavelength of 450 nm. The protein concentration of ALDH4A1 in the sample is calculated based on the standard curve.

[0024] S5: Based on the expression level of ALDH4A1 protein in the detected samples, the tissue properties of atherosclerotic plaques are classified; The plaque nature was determined by obtaining the ALDH4A1 immunohistochemical H-score of the patient's plaque tissue and comparing it with an adaptive threshold for classifying the nature of the patient's plaque tissue, and then classifying it based on the comparison results; The formula for calculating the ALDH4A1 immunohistochemical H-score value in plaque tissue is as follows:

[0025] In the formula, This represents the H-score value; The percentage of positive cells corresponding to the staining intensity; The comparison results are divided into H-score values ​​≥ T. adapt Value, 0.8×T adapt Value ≤ H-score <T adapt Value, H-score < 0.8 × T adapt There are three possible scenarios, corresponding to stable output, low-risk and vulnerable output, and high-risk and vulnerable output.

[0026] It should be noted that the stability output results correspond to the stable plaque group, the low-risk vulnerable type output results correspond to the erosive plaque group, and the high-risk vulnerable type output results correspond to the ulcerative plaque group.

[0027] S6: Validate the results of the tissue nature classification of atherosclerotic plaques.

[0028] The validation methods include high-risk targeted validation and non-high-risk sampling validation. High-risk targeted validation is used to validate plaques classified as high-risk vulnerable, while non-high-risk sampling validation is used to validate plaques classified as stable and low-risk vulnerable. High-risk targeted validation measures the thickness of the thinnest part of the fiber cap using optical coherence tomography within 24 hours; It should be noted that if the thickness of the thinnest part of the fiber cap is less than 65μm, the verification is successful; if the thickness is greater than or equal to 65μm, the threshold is reset for classification.

[0029] Non-high-risk sampling verification involves randomly selecting 10% of the samples and having them blinded by two pathologists.

[0030] It should be noted that if the Kappa coefficient of agreement between two pathologists is ≥0.8, the batch verification passes; if the Kappa coefficient is <0.8, the threshold is reset for classification.

[0031] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0032] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0033] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0034] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0035] Finally, it should be noted that the above description represents a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A method for classifying plaque characteristics in patients with carotid artery stenosis based on ALDH4A1, characterized in that, include: S1: Obtain atherosclerotic plaque tissue samples from patients with carotid artery stenosis; S2: Define the type of tissue properties of atherosclerotic plaques; S3: Set the threshold for classifying the tissue properties of atherosclerotic plaques; S4: Detect the expression level of ALDH4A1 protein in atherosclerotic plaque tissue samples; S5: Based on the expression level of ALDH4A1 protein in the detected samples, the tissue properties of atherosclerotic plaques are classified; S6: Validate the results of the tissue nature classification of atherosclerotic plaques.

2. The method for classifying plaque characteristics in carotid artery stenosis patients based on ALDH4A1 as described in claim 1, characterized in that, S1 obtains atherosclerotic plaque tissue samples from patients with carotid artery stenosis, wherein: The atherosclerotic plaque tissue samples were obtained through carotid endarterectomy and separated using non-pyrogenic instruments. After the plaque tissue sample is separated, the anatomical location information of the plaque is recorded, including the orientation of the lumen surface and the identification of key areas; The plaque tissue samples were aliquoted within 5 minutes of being ex vivo and then preserved by quick-freezing in liquid nitrogen at -80°C.

3. The method for classifying plaque characteristics in carotid artery stenosis patients based on ALDH4A1 as described in claim 1, characterized in that, The S2 plan defines the types of atherosclerotic plaque tissue properties, including: The atherosclerotic plaque tissue properties are divided into stable plaque group and unstable plaque group, and the unstable plaque group is further divided into erosive plaque group and ulcerative plaque group; The classification criteria for the stable plaque group are: the thinnest part of the fibrous cap is ≥150μm and the blue staining area of ​​Masson staining is >80%; the classification criteria for the erosive plaque group are: CD31 staining shows endothelial discontinuity and CD62p positive layer thickness is ≥50μm; and the classification criteria for the ulcerative plaque group are: EVG staining confirms elastic lamina rupture and CD34 positive exposed collagen.

4. The method for classifying plaque characteristics in carotid artery stenosis patients based on ALDH4A1 as described in claim 1, characterized in that, S3 sets a threshold for classifying the tissue properties of atherosclerotic plaques, wherein: The threshold setting is dynamically generated using a pre-trained threshold probability model. The threshold generation steps include receiving input patient clinical information, establishing a pre-trained threshold decision model, and calculating an adaptive threshold for plaque nature classification. The patient's clinical information includes the patient's age, ischemic stroke status, and duration of statin use; The pre-trained threshold decision model takes ALDH4A1 expression value and patient clinical information as input and outputs a binary classification probability. The adaptive threshold is obtained by solving the pre-trained threshold decision model based on the current patient's clinical information vector to obtain the ALDH4A1 critical value, as shown in the following formula: In the formula, This represents the probability of the model outputting a stable patch. The critical value representing the stability of a plaque; This represents a vector of the patient's clinical information.

5. The pre-trained threshold decision model as described in claim 4, wherein: The pre-trained threshold decision model is based on plaque histopathological characteristics and incorporates ischemic stroke recurrence events within 12 months post-surgery to determine the true value of plaque stability. A regularization term is added to the loss function to suppress variance caused by batch effects of the detection platform during training, as shown in the following formula: In the formula, Represents the loss function. Represents the binary cross-entropy loss. , These represent the true label value and the model prediction value, respectively. This represents the weight coefficient of the regularization term, used to control the degree of influence of the regularization term on the total loss. This indicates the batch size, which is the number of samples used in each training iteration. This represents the feature value of the j-th sample in the batch. It represents the average of all sample feature values ​​in the quantity.

6. The method for classifying plaque characteristics in carotid artery stenosis patients based on ALDH4A1 as described in claim 1, characterized in that, The S4 assay detects the expression level of ALDH4A1 protein in atherosclerotic plaque tissue samples, wherein: The steps for detecting the expression level of ALDH4A1 protein include multiplex immunofluorescence localization and ELISA detection; The multiplex immunofluorescence localization was detected by co-localization of ALDH4A1 in the plaque area, T-bet in Th1 cells, CD68 in macrophages, and sma staining in smooth muscle. The ELISA assay involves preparing a standard, adding a sample and incubating it, then adding a chromogenic agent. A multi-functional microplate reader is used to read the absorbance value at a detection wavelength of 450 nm, and the protein concentration of ALDH4A1 in the sample is calculated based on the standard curve.

7. The method for classifying plaque characteristics in carotid artery stenosis patients based on ALDH4A1 as described in claim 1, characterized in that, S5 classifies the tissue properties of atherosclerotic plaques based on the expression level of ALDH4A1 protein in the detected samples, wherein: The plaque tissue properties are determined by comparing the ALDH4A1 immunohistochemical H-score of the patient's plaque tissue sample with an adaptive threshold for classifying the patient's plaque tissue properties, and the classification is based on the comparison results. The formula for calculating the ALDH4A1 immunohistochemical H-score value of the plaque tissue is as follows: In the formula, This represents the H-score value; The percentage of positive cells corresponding to the staining intensity; The comparison results are divided into H-score values ​​≥ T. adapt Value, 0.8×T adapt Value ≤ H-score <T adapt Value, H-score < 0.8 × T adapt There are three possible scenarios, corresponding to stable output, low-risk and vulnerable output, and high-risk and vulnerable output.

8. The method for classifying plaque characteristics in carotid artery stenosis patients based on ALDH4A1 as described in claim 1, characterized in that, S6 verifies the results of the tissue nature classification of atherosclerotic plaques, wherein: The verification method includes high-risk targeted verification and non-high-risk sampling verification. The high-risk targeted verification is used to verify plaques classified as high-risk vulnerable, and the non-high-risk sampling verification is used to verify plaques classified as stable and low-risk vulnerable. The high-risk targeted verification was performed within 24 hours by measuring the thickness of the thinnest part of the fiber cap using optical coherence tomography. The non-high-risk sampling verification involves randomly selecting 10% of the samples and having them reviewed in a blinded manner by two pathologists.

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