Trimethylamine-N-oxide (TMAO) is a gut microbiota-derived metabolite, the production of which in vivo is mainly regulated by dietary choices, gut microbiota, and the hepatic enzyme flavin monooxygenase (FMO), while its elimination occurs via the kidneys. The TMAO level is positively correlated with the risk of developing cardiovascular diseases. Recent studies have found that TMAO plays an important role in the development of ischemic stroke. In this review, we describe the relationship between TMAO and ischemic stroke risk factors (hypertension, diabetes, atrial fibrillation, atherosclerosis, thrombosis, etc.), disease risk, severity, prognostic outcomes, and recurrence and discuss the possible mechanisms by which they interact. Importantly, TMAO induces atherosclerosis and thrombosis through lipid metabolism, foam cell formation, endothelial dysfunction (via inflammation, oxidative stress, and pyroptosis), enhanced platelet hyper-reactivity, and the upregulation and activation of vascular endothelial tissue factors. Although the pathogenic mechanisms underlying TMAO’s aggravation of disease severity and its effects on post-stroke neurological recovery and recurrence risk remain unclear, they may involve inflammation, astrocyte function, and pro-inflammatory monocytes. In addition, this paper provides a summary and evaluation of relevant preclinical and clinical studies on interventions regarding the gut-microbiota-dependent TMAO level to provide evidence for the prevention and treatment of ischemic stroke through the gut microbe–TMAO pathway.

As an essential constituent of the mitochondrial contact site and cristae organization system (MICOS), MIC19 plays a crucial role in maintaining the stability of mitochondrial function and microstructure. However, the mechanisms and functions of MIC19 in intracerebral hemorrhage (ICH) remain unknown and need to be investigated. Sprague Dawley (SD) rats injected with autologous blood obtained from the caudal artery, and cultured neurons exposed to oxygen hemoglobin (OxyHb) were used to establish and emulate the ICH model in vivo and in vitro. Lentiviral vector encoding MIC19 or MIC19 short hairpin ribonucleic acid (shRNA) was constructed and administered to rats by intracerebroventricular injection to overexpress or knock down MIC19, respectively. First, MIC19 protein levels were increased after ICH modeling. After virus transfection and subsequent ICH modeling, we observed that overexpression of MIC19 could mitigate cell apoptosis and neuronal death, as well as abnormalities in mitochondrial structure and function, oxidative stress within mitochondria, and neurobehavioral deficits in rats following ICH. Conversely, knockdown of MIC19 had the opposite effect. Moreover, we found that the connection between MIC19 and SAM50 was disrupted after ICH, which may be a reason for the impairment of the mitochondrial structure after ICH. In conclusion, MIC19 exerts a protective role in the subsequent injury induced by ICH. The investigation of MIC19 may offer clinicians novel therapeutic insights for patients afflicted with ICH.

Background: Intracranial atherosclerotic stenosis (ICAS) is a leading cause of ischemic stroke, particularly in the anterior circulation. Understanding the underlying stroke mechanisms is essential for guiding personalized treatment strategies. This study proposes an integrated framework that combines CT perfusion imaging, vascular anatomical features, computational fluid dynamics (CFD), and machine learning to classify stroke mechanisms based on the Chinese Ischemic Stroke Subclassification (CISS) system. Methods: A retrospective analysis was conducted on 118 patients with intracranial atherosclerotic stenosis. Key indicators were selected using one-way ANOVA with nested cross-validation and visualized through correlation heatmaps. Optimal thresholds were identified using decision trees. The classification performance of six machine learning models was evaluated using ROC and PR curves. Results: Time to Maximum (Tmax) > 4.0 s, wall shear stress ratio (WSSR), pressure ratio, and percent area stenosis were identified as the most predictive indicators. Thresholds such as Tmax > 4.0 s = 134.0 mL and WSSR = 86.51 effectively distinguished stroke subtypes. The Logistic Regression model demonstrated the best performance (AUC = 0.91, AP = 0.85), followed by Naive Bayes models. Conclusions: This multimodal approach effectively differentiates stroke mechanisms in anterior circulation ICAS and holds promise for supporting more precise diagnosis and personalized treatment in clinical practice.

Background: Intracranial atherosclerotic stenosis (ICAS) is a primary cause of ischemic stroke. Accurate assessment of anatomical and hemodynamic characteristics is crucial for treatment planning, yet current clinical evaluation primarily relies on luminal stenosis.Objective: This study aims to compare computational fluid dynamics (CFD) models based on digital subtraction angiography (DSA), computed tomography angiography (CTA) and CTA model incorporating DSA hemodynamic information (CMD) integrating DSA flow data with CTA morphological structure, evaluating their differences and consistency in ICAS assessment.Methods: 40 ICAS patients who underwent CTA and DSA were retrospectively included. Patient-specific CFD simulations were performed using standardized boundary conditions to assess morphological data and hemodynamic parameters, including pressure ratio, wall shear stress ratio, and high shear stress areas. Statistical analyses included paired comparisons, intraclass correlation coefficients (ICC), and Bland–Altman analysis.Results: CTA-based models demonstrated excellent consistency with DSA in anatomical measurements (ICC > 0.90). The CMD approach enhanced consistency in functional metrics, with CMD-derived PR and WSSR highly concordant with DSA results. When using CTA alone, WSSR was slightly underestimated, particularly in middle artery lesions. Subgroup analysis indicated that lesion location significantly influences flow and shear stress patterns.Conclusion: CTA-based CFD modeling serves as a reliable non-invasive alternative to DSA for morphological ICAS assessment. The CMD method further improves the accuracy of functional evaluation by integrating flow data. These findings support the integration of anatomical imaging with hemodynamic modeling to enhance the clinical potential for stroke risk stratification.

INTRODUCTION: The prognostic value of concurrent changes in subjective memory and objective cognition for later functional dependence remains unclear across populations. METHODS: We conducted a harmonized multicohort longitudinal analysis of the China Health and Retirement Longitudinal Study (CHARLS), US Health and Retirement Study (HRS), English Longitudinal Study of Ageing (ELSA), and Survey of Health, Ageing and Retirement in Europe (SHARE). Subjective memory and objective cognition were assessed at baseline (T0) and first follow‐up (T1). Participants were classified into four trajectory groups, and incident functional dependence after T1 was examined using cohort‐specific Cox models and pooled random‐effects meta‐analysis. Sensitivity analyses addressed alternative decline thresholds, continuous change‐score models, fixed follow‐up windows, baseline‐level adjustment, multiple imputation, and competing‐risk models treating death as a competing event in cohorts with adequate mortality information. RESULTS: Among 86,562 participants included in trajectory derivation and 62,329 included in fully adjusted outcome analyses, concordant decline showed the strongest and most consistent association with incident functional dependence. In pooled Cox analyses, concordant decline was associated with the greatest risk increase (hazard ratio [HR] 1.470, 95% confidence interval [CI] 1.381–1.566), with smaller associations observed for subjective‐only decline (HR 1.222, 95% CI 1.055–1.415) and objective‐only decline (HR 1.116, 95% CI 1.038–1.200). In competing‐risk analyses across CHARLS, HRS, and SHARE, concordant decline remained the strongest association after accounting for death (pooled subdistribution HR 1.386, 95% CI 1.273–1.509). DISCUSSION: Joint cognitive change, especially concordant decline, may improve identification of older adults at risk of future functional dependence.

Background: Intracranial atherosclerotic stenosis (ICAS) is a primary cause of ischemic stroke and understanding its pathogenesis aids in therapeutic decision-making. This study integrates computational fluid dynamics (CFD), CT perfusion (CTP) metrics, anatomical indexes, and machine learning to classify anterior circulation ICAS within the Chinese Ischemic Stroke Subclassification (CISS) framework and identify characteristics of different stroke mechanisms. Methods: A total of 118 ICAS patients were classified based on CISS criteria. Key indicators were identified through one-way ANOVA, correlation, and effect size analysis. A decision tree model established thresholds for stroke mechanisms, and six machine learning models were evaluated for classification performance using confusion matrices, ROC curves, and PR curves. Results: Time to Maximum (Tmax)> 4.0s, Area stenosis rate (AS%), wall shear stress ratio (WSSR), and pressure ratio (PR) were key indicators for classification, while cerebral blood flow (CBF) and cerebral blood volume (CBV) showed no significant differences across infarction types. Thresholds identified included Tmax > 4.0s = 49.85 ml, WSSR = 52.79/86.51, PR = 0.69, and AS% = 0.73. Logistic regression outperformed other models (AUC = 0.923, AP = 0.872), followed by the ensemble model. Conclusion: Combining CFD and CTP metrics with machine learning effectively classifies anterior circulation ICAS-related stroke mechanisms within CISS typing, offering a reliable approach for precise diagnosis and individualized treatment of ischemic stroke.