Background: The role of complement component 1q (C1Q) related genes on human atherosclerotic plaques (HAP) is less known. Our aim is to establish C1Q associated hub genes using single-cell RNA sequencing (scRNA-seq) and bulk RNA analysis to diagnose and predict HAP patients more effectively and investigate the association between C1Q and HAP (ischemic stroke) using bidirectional Mendelian randomization (MR) analysis.Methods: HAP scRNA-seq and bulk-RNA data were download from the Gene Expression Omnibus (GEO) database. The C1Q-related hub genes was screened using the GBM, LASSO and XGBoost algorithms. We built machine learning models to diagnose and distinguish between types of atherosclerosis using generalized linear models and receiver operating characteristics (ROC) analyses. Further, we scored the HALLMARK_COMPLEMENT signaling pathway using ssGSEA and confirmed hub gene expression through qRT-PCR in RAW264.7 macrophages and apoE-/- mice. Furthermore, the risk association between C1Q and HAP was assessed through bidirectional MR analysis, with C1Q as exposure and ischemic stroke (IS, large artery atherosclerosis) as outcomes. Inverse variance weighting (IVW) was used as the main method.Results: We utilized scRNA-seq dataset (GSE159677) to identify 24 cell clusters and 12 cell types, and revealed seven C1Q associated DEGs in both the scRNA-seq and GEO datasets. We then used GBM, LASSO and XGBoost to select C1QA and C1QC from the seven DEGs. Our findings indicated that both training and validation cohorts had satisfactory diagnostic accuracy for identifying patients with HPAs. Additionally, we confirmed SPI1 as a potential TF responsible for regulating the two hub genes in HAP. Our analysis further revealed that the HALLMARK_COMPLEMENT signaling pathway was correlated and activated with C1QA and C1QC. We confirmed high expression levels of C1QA, C1QC and SPI1 in ox-LDL-treated RAW264.7 macrophages and apoE-/- mice using qPCR. The results of MR indicated that there was a positive association between the genetic risk of C1Q and IS, as evidenced by an odds ratio (OR) of 1.118 (95%CI: 1.013–1.234, P = 0.027).Conclusion: The authors have effectively developed and validated a novel diagnostic signature comprising two genes for HAP, while MR analysis has provided evidence supporting a favorable association of C1Q on IS.

Accurate identification of high-risk vulnerable plaques and assessment of stroke risk are crucial for clinical decision-making, yet reliable non-invasive predictive tools are currently lacking. This study aimed to develop an artificial intelligence model based on high-resolution vessel wall imaging (HR-VWI) to assist in the identification of vulnerable plaques and prediction of stroke recurrence risk in patients with symptomatic intracranial atherosclerotic stenosis (sICAS). Between June 2018 and June 2024, a retrospective collection of HR-VWI images from 1806 plaques in 726 sICAS patients across four medical institutions was conducted. K-means clustering was applied to the T1-weighted imaging (T1WI) and T1-weighted imaging with contrast enhancement (T1CE) sequences. Following feature extraction and selection, radiomic models and habitat models were constructed. Additionally, the Vision Transformer (ViT) architecture was utilized for HR-VWI image analysis to build a deep learning model. A stacking fusion strategy was employed to integrate the habitat model and ViT model, enabling effective identification of high-risk vulnerable plaques in the intracranial region and prediction of stroke recurrence risk. Model performance was evaluated using receiver operating characteristic (ROC) curves, and model comparisons were conducted using the DeLong test. Furthermore, decision curve analysis and calibration curves were utilized to assess the practicality and clinical value of the model. The fused Habitat + ViT model exhibited excellent performance in both the validation and test sets. In the validation set, the model achieved an area under the curve (AUC) of 0.949 (95% CI: 0.927–0.969), with a sensitivity of 0.879 (95% CI: 0.840–0.945), a specificity of 0.905 (95% CI: 0.842–0.949), and an accuracy of 0.897 (95% CI: 0.870–0.926). In the test set, the AUC increased to 0.960 (95% CI: 0.941–0.973), with specificity rising to 0.963 and an accuracy of 0.885 (95% CI: 0.857–0.913). The DeLong test revealed statistically significant differences in AUC between the fused model and the single-modal models (test set, vs. ViT p = 0.000; vs. Habitat p = 0.000) Cox regression analysis showed that the Habitat + ViT index, based on the prediction probability of the Habitat + ViT model, was an independent predictor of stroke recurrence (HR: 2.07; 95% CI: 1.12–3.81), with significant predictive power for stroke events at multiple time points. Specifically, measured by AUC values, the model’s predictive performance at 1, 2, 3, and 4 years was 0.751 (95% CI: 0.679–0.823), 0.820 (95% CI: 0.760–0.876), 0.815 (95% CI: 0.753–0.877), and 0.780 (95% CI: 0.680–0.873), respectively. The integrated Habitat + ViT model based on HR-VWI demonstrated superior performance in identifying high-risk vulnerable plaques in sICAS patients and predicting stroke recurrence risk, providing valuable support for clinical decision-making. This study was supported by the 10.13039/501100001809 National Natural Science Foundation of China (grant 82204933). Henan Key Laboratory of Neurorestoratology (HNSJXF-2021-004), 2019 Joint Construction Project of Henan Provincial Health Committee and Ministry of Health (SB201901061), and the Xin Xiang City Acute Ischemic Stroke Precision Prevention and Treatment Key Laboratory.

Background: High-resolution magnetic resonance vessel wall imaging (HR-VWI) offers enhanced visualization of vascular structures, thereby facilitating the deep learning (DL) network’s acquisition of more extensive and detailed image information. This study aimed to develop a high-precision integrated model leveraging DL with an attention mechanism based on HR-VWI for predicting recurrent stroke in patients with symptomatic intracranial atherosclerotic stenosis (sICAS). Methods: A retrospective study was conducted involving 363 sICAS patients who underwent HR-VWI, with data divided into a training set (n=254) from Center 1 and a test set (n=109) from Center 2. Two convolutional neural network (CNN) models, ResNet50 and DenseNet169, were employed as feature extractors to capture image information from culprit plaques in HR-VWI. Integrating the Transformer attention mechanism, an advanced ensemble model, Trans-CNN, was constructed to predict stroke recurrence in sICAS patients. Model performance was evaluated using receiver operating characteristic (ROC) curves, with DeLong’s test for comparing models. Additionally, decision curve analysis (DCA) and calibration curves were utilized to assess the model’s practical and clinical value. Results: Trans-CNN demonstrated superior predictive performance, outperforming other models in both the training and test sets. Specifically, in the training set, Trans-CNN achieved an area under the curve (AUC) of 0.951 [95% confidence interval (CI): 0.923–0.974], accuracy of 0.880 (95% CI: 0.797–0.937), sensitivity of 0.900 (95% CI: 0.836–1.000), and specificity of 0.882 (95% CI: 0.757–0.948). Similarly, in the test set, it achieved an AUC of 0.912 (95% CI: 0.839–0.969), accuracy of 0.858 (95% CI: 0.743–0.936), sensitivity of 0.880 (95% CI: 0.693–1.000), and specificity of 0.810 (95% CI: 0.690–0.976). The AUC improvement of Trans-CNN over all other models was statistically significant (DeLong’s test, P<0.05). Calibration curve analysis revealed good agreement between predicted probabilities and observed outcomes in both sets. DCA further underscored the potential value of Trans-CNN in guiding clinical decision-making. Conclusions: The integrated model combining DL with an attention mechanism based on HR-VWI exhibits excellent performance in assessing the risk of stroke recurrence in sICAS patients. This advancement holds significant potential in assisting clinicians in diagnosis and developing individualized treatment strategies.

Background and objective: Symptomatic intracranial atherosclerotic stenosis (SICAS) is the most common etiology of ischemic stroke and one of the main causes of high stroke recurrence. The recurrence of stroke is closely related to the prognosis of ischemic stroke. This study aims to develop a machine learning model based on high-resolution vessel wall imaging (HR-VWI) to predict the risk of stroke recurrence in SICAS.Methods: This study retrospectively collected data from 180 SICAS stroke patients treated at the hospital between 2020.01 and 2022.01. Relevant imaging and clinical data were collected, and follow-up was conducted. The dataset was divided into a training set and a validation set in a ratio of 7:3. We employed the least absolute shrinkage and selection operator (LASSO) regression to perform a selection on the baseline data, laboratory tests, and neuroimaging data generated by HR-VWI scans collected from the training set. Finally, five machine learning techniques, including logistic regression model (LR), support vector machine (SVM), Gaussian naive Bayes (GNB), Complement naive Bayes (CNB), and k-nearest neighbors algorithm (kNN), were employed to develop a predictive model for stroke recurrence. Shapley Additive Explanation (SHAP) was used to provide visualization and interpretation for each patient. The model’s effectiveness was evaluated using average accuracy, sensitivity, specificity, precision, f1 score, PR curve, calibration curve, and decision curve analysis.Results: LASSO analysis revealed that “history of hypertension,” “homocysteine level,” “NWI value,” “stenosis rate,” “intracranial hemorrhage,” “positive remodeling,” and “enhancement grade” were independent risk factors for stroke recurrence in SICAS patients. In 10-fold cross-validation, the area under the curve (AUC) ranged from 0.813 to 0.912 in ROC curve analysis. The area under the precision-recall curve (AUPRC) ranged from 0.655 to 0.833, with the Gaussian Naive Bayes (GNB) model exhibiting the best ability to predict stroke recurrence in SICAS. SHAP analysis provided interpretability for the machine learning model and revealed essential factors related to the risk of stroke recurrence in SICAS.Conclusion: A precise machine learning-based prediction model for stroke recurrence in SICAS has been established to assist clinical practitioners in making clinical decisions and implementing personalized treatment measures.

This study aims to analyze the association between plaque characteristics and stroke recurrence across different degrees of stenosis, and to evaluate the predictive value of combining culprit plaque characteristics with the modified Essen score for stroke recurrence. This retrospective analysis included 368 intracranial atherosclerotic stenosis (ICAS) patients from two centers, categorized into mild-to-moderate stenosis (<70%, n = 155) and severe stenosis (≥70%, n = 213) groups. High-resolution vessel wall images and clinical data were analyzed. Univariate and multivariate Logistic regression analyses were conducted to determine risk factors for stroke recurrence. The predictive performance was evaluated using the area under the receiver operating characteristic curve. In the mild-to-moderate stenosis group, a higher Normalized Wall Index (NWI) (OR = 1.088, 95% CI: 1.009-1.186, P = .04) and the presence of intraplaque hemorrhage (IPH) (OR = 4.049, 95% CI: 1.227-16.065, P = .03) were risk factors for stroke recurrence, but not in severe stenosis. A combined model (IPH, NWI, modified Essen score) improved predictive performance over the modified Essen score alone, particularly in mild-to-moderate stenosis. In patients with luminal stenosis of less than 70%, elevated NWI and IPH were validated as important and effective indicators for predicting stroke recurrence, demonstrating significant predictive performance. Conversely, plaque characteristics showed limited predictive utility in severe stenosis, highlighting stenosis-dependent differences in plaque-related stroke risk. Integrating imaging biomarkers with modified Essen score optimizes recurrence prediction in mild-to-moderate ICAS.