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Background and objectiveTo investigate the use of high-resolution magnetic resonance imaging (HR-MRI) to identify the characteristics of culprit plaques in intracranial arteries, and to evaluate the predictive value of the characteristics of culprit plaques combined with the modified Essen score for the recurrence risk of high-risk non-disabling ischemic cerebrovascular events (HR-NICE) patients.MethodsA retrospective analysis was conducted on 180 patients with HR-NICE at the First Affiliated Hospital of Xinxiang Medical University, including 128 patients with no recurrence (non-recurrence group) and 52 patients with recurrence (recurrence group). A total of 65 patients with HR-NICE were collected from the Sixth Affiliated Hospital of Shanghai Jiaotong University as a validation group, and their modified Essen scores, high-resolution magnetic resonance vessel wall images, and clinical data were collected. The culprit plaques were analyzed using VesselExplorer2 software. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for recurrence, and a nomogram was constructed using R software to evaluate the discrimination of the model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to evaluate the model performance. Calibration curves and Decision Curve Analysis (DCA) were used to evaluate the model efficacy.ResultsIntra-plaque hemorrhage (OR = 3.592, 95% CI = 1.474–9.104, p = 0.006), homocysteine (OR = 1.098, 95% CI = 1.025–1.179, p = 0.007), and normalized wall index (OR = 1.114, 95% CI = 1.027–1.222, p = 0.015) were significantly higher in the recurrent stroke group than in the non-recurrent stroke group, and were independent risk factors for recurrent stroke. The performance of the nomogram model (AUC = 0.830, 95% CI: 0.769–0.891; PR-AUC = 0.628) was better than that of the modified Essen scoring model (AUC = 0.660, 95% CI: 0.583–0.738) and the independent risk factor combination model (AUC = 0.827, 95% CI: 0.765–0.889). The nomogram model still had good model performance in the validation group (AUC = 0.785, 95% CI: 0.671–0.899), with a well-fitting calibration curve and a DCA curve indicating good net benefit efficacy for patients.ConclusionHigh-resolution vessel wall imaging combined with a modified Essen score can effectively assess the recurrence risk of HR-NICE patients, and the nomogram model can provide a reference for identifying high-risk populations with good clinical application prospects.

Background and objectiveSymptomatic 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.MethodsThis 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.ResultsLASSO 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.ConclusionA 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.

BackgroundThe 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.MethodsHAP 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.ResultsWe 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).ConclusionThe 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.

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 qRTPCR 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. [ABSTRACT FROM AUTHOR]

Subarachnoid hemorrhage (SAH), one of the serious types of stroke incurred by bleeding into the space surrounding the brain, occurs when brains are deprived of oxygen by various factors, particularly an interruption to the blood supply or a ruptured aneurysm. Cerebral vasospasm (CVS) is one of the most common complications of SAH. It has been proposed that platelet-derived growth factor (PDGF) is involved in CVS. The aim of the present study was to analyze expression of PDGF in rabbit models of CVS. Post-SAH CVS rabbit models were created using endovascular puncture and employed to analyze the expression patterns of PDGF by enzyme-linked immunosorbent assay and immunohistochemistry. The results indicated that the creation of the rabbit model of CVS induced using endovascular puncture was successful and demonstrated the double phase changes observed in human CVS. The acute stage started at 12 h post-SAH with narrowing of the vascular lumen diameter. This narrowing appeared again on the seventh day in delayed CVS alongside increased thickness of vessel walls. PDGF-β expression was observed in vascular smooth muscle cells of the rabbit models. PDGF-β was expressed as early as 3 h post-SAH, it was evident after 1 day and reached a peak in 7 days, suggesting that PDGF-β is involved in the early stages of CVS. In the current study, it was confirmed that PDGF-β expression was present in the rabbit models of CVS, which may aid the elucidation of the pathogenesis of CVS, and also provide useful information for diagnosis and treatment of CVS. [ABSTRACT FROM AUTHOR]