The utilization of cell-free therapies derived from extracellular vesicles (EVs) has garnered mounting interest as a promising approach to address the myriad challenges associated with ischemic stroke. These Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) possess considerable therapeutic potential due to their inherent properties, including low immunogenicity, efficient cargo transportation, and the ability to cross the blood-brain barrier. This review examines the mechanisms underlying mesenchymal stem cell-derived EVs in the treatment of ischemic stroke. Future research should aim to identify optimal strategies for EV-based interventions, including combination therapy and preconditioning strategies.

Background/Objectives: Liver cirrhosis is a major global health challenge and a key risk factor for hepatocellular carcinoma (HCC), a malignancy with high mortality due to late diagnosis. This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data, using single-cell data to identify macrophage-associated transcriptomic changes during the progression from cirrhosis to HCC, and using bulk data to validate these findings in independent cohorts, while developing predictive models for early risk assessment. Methods: We integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing datasets derived from liver tissues of cirrhosis and HCC patients. Single-cell data were used to identify macrophage subtypes and their dynamic transcriptional changes, while bulk data provided validation in independent cohorts. Gene expression and network analyses were performed, and candidate genes were used to construct diagnostic models with Lasso regression, Random Forest, and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using receiver operating characteristic curves. Results: We identified eleven macrophage-associated genes, among which KLK11, MARCO, CFP, KRT19, GAS1, SOD3, and CYP2C8 were downregulated in HCC, indicating loss of tumor-suppressive and pro-apoptotic functions, while TOP2A, CENPF, MKI67, and NUPR1 were upregulated, reflecting enhanced cell cycle progression, proliferation, and M2 polarization. These are all associated with the progression from liver cirrhosis to HCC. Based on these findings, we established predictive models using Lasso, Random Forest, and XGBoost, which stratified cirrhotic patients into high- and low-risk groups according to cutoff values using liver tissue transcriptomic data. All three models demonstrated high diagnostic performance. Conclusions: This study highlights the critical role of macrophage-associated transcriptomic remodeling in liver disease progression. The machine learning–based predictive models offer a promising approach for early diagnosis and clinical decision-making in patients with cirrhosis.

Gut microbiota dysbiosis has been associated with ischemic stroke (IS), myocardial infarction (MI), and aging, but whether these contexts share reproducible microbial features remains unclear. We conducted an exploratory and hypothesis-generating descriptive study of genus-level microbiota patterns across an internal IS cohort and publicly available external IS, MI, and age-stratified or longevity-associated datasets. Analyses were performed within predefined age strata and interpreted cautiously because of the small internal cohort, cross-cohort heterogeneity, and the absence of direct metabolite, intestinal barrier, inflammatory, or microbial activity measurements. No taxon in the internal cohort remained statistically significant after false-discovery-rate correction; therefore, all taxonomic observations were treated as descriptive. Candidate overlapping features included repeated detection of Escherichia-Shigella and Klebsiella and non-uniform patterns among genera previously associated with short-chain fatty acid metabolism, including Faecalibacterium, Blautia, and Roseburia. Lachnoclostridium and Bacteroides showed opposite abundance gradients in selected cross-dataset comparisons. These observations suggest possible ecological overlap across ischemic disease and age-associated microbiome contexts, but they do not establish causality, disease-specific biomarkers, or shared microbial function. The mechanistic models discussed in this manuscript are literature-informed hypotheses based on exploratory compositional data and require future validation in larger, harmonized longitudinal cohorts using metagenomic, metabolomic, clinical, and experimental measurements.