Both chronic hepatitis B (CHB) and stroke contribute to a high burden of disease in the majority of low- and middle-income countries. Epidemiological studies yield conflicting results on the association between CHB and stroke, and the causal relationship remains inconclusive. This study aimed to assess the causal effects of CHB on stroke and its subtypes in East Asians by Mendelian randomization (MR) analysis. Variants associated with CHB were obtained from a genome-wide association study (GWAS) of Chinese samples as instrumental variables. The summary statistics for stroke in East Asians were derived from the largest published GWAS to date. Two-sample MR analyses were implemented to evaluate the causal effects of CHB on stroke and its subtypes by using the canonical inverse variance weighting method and other supplementary approaches. We observed an association between genetic predisposition to CHB and a decreased risk of large-artery atherosclerotic stroke (odds ratio = 0.872, 95% confidence interval = 0.786–0.967, p = 0.010). The causal effects of CHB on other stroke outcomes were not statistically significant. Evidence for heterogeneity and horizontal pleiotropy were not found in our analyses. This study provides genetic evidence for a negative association between CHB and stroke in East Asians, which helps improve our understanding of the etiology of stroke.

Medication recommendation based on electronic health records (EHRs) is a significant research direction in the biomedical field, which aims to provide a reasonable prescription for patients according to their historical and current health conditions. However, the existing recommended methods have many limitations in dealing with the structural and temporal characteristics of EHRs. These methods either only consider the current state while ignoring the historical situation, or fail to adequately assess the structural correlations among various medical events. These factors result in poor recommendation quality. To solve this problem, we propose an augmented graph structural–temporal convolutional network (A-GSTCN). Firstly, an augmented graph attention network is used to model the structural features among medical events of patients’ EHRs. Next, the dilated convolution combined with residual connection is applied in the proposed model, which can improve the temporal prediction capability and further reduce the complexity. Moreover, the cache memory module further enhances the model’s learning of the history of EHRs. Finally, the A-GSTCN model is compared with the baselines through experiments, and the efficiency of the A-GSTCN model is verified by Jaccard, F1 and PRAUC. Not only that, the proposed model also reduces the training parameters by an order of magnitude.

Immune mechanisms play a crucial role in the development of stroke. However, immune-related phenotypes are diverse and their associations with stroke are largely unknown. Here, we aimed to systematically explore the causal role of immune cell traits in stroke and its subtypes by leveraging data from genome-wide association studies (GWASs). Exposure data were obtained from a recent GWAS on 731 immune cell traits profiled by flow cytometry involving 3757 individuals. By conducting two-sample univariable Mendelian randomization (MR) analyses, each immune cell trait was assessed for causal relationships with stroke outcomes from the MEGASTROKE Consortium (40,585 cases and 406,111 controls). The robustness of the MR results was verified by a series of sensitivity analyses. We identified three significant associations after Bonferroni correction (P < 1.37E-05). Increased CD27 expression on memory B cell (OR = 1.23, 95% CI = 1.14-1.33, P = 2.78E-08), IgD^-CD38dim B cell (OR = 1.16, 95% CI = 1.09-1.23, P = 5.98E-06) and unswitched memory B cell (OR = 1.18, 95% CI = 1.10-1.27, P = 1.09E-05) were associated with a higher risk of large-artery atherosclerotic stroke (LAS). Furthermore, expression quantitative trait loci data also indicated elevated blood CD27 mRNA level was a risk factor for LAS (OR = 1.37, 95% CI = 1.02-1.84, P = 0.037). This study provided genetic evidence of the causal relationship between immune cell traits and stroke, highlighting the role of CD27 on memory B cell as a novel factor for LAS risk.

This study aimed to determine the possible risk factors for stroke outcomes based on prospective cohort study in China. A total of 146 stroke patients were recruited and divided into 2 groups, which assessed using the modified Rankin Scale (mRS), good outcomes (mRS <= 2) and poor outcomes group (mRS > 2). Demographic, clinical, and laboratory characteristics of participants were obtained from the medical record. The multivariable logistic regression analysis was employed to assess the risk factors for stroke outcomes. Of 146 participants, 28 (19.18%) were presented with poor outcomes at day 90. As a result of multivariable logistic regression analysis, a significantly increased risk of stroke outcomes was found in patients with Barthel Index (BI) score (stroke (OR 1.50, 95% CI 1.21 ~ 1.85, P < .001) and IS (OR 1.48, 95% CI 1.20 ~ 1.83, P < .001)).

Background: Astilbin is a bioactive flavonoid with documented anti-inflammatory properties; however, its sustained interactions with the gut microbiota remain poorly understood. Fecal samples from three healthy donors were pooled and fermented with astilbin in an in vitro human colonic model over 7 days. This exploratory study aimed to characterize the temporal ecological shifts associated with prolonged astilbin exposure. Methods: Time-resolved 16S rRNA gene sequencing, PICRUSt2 functional prediction, BugBase phenotypic inference, and pseudo-targeted metabolomics were integrated to track microbial-metabolic dynamics. Results: Astilbin exposure was associated with a highly coordinated, three-stage microbial succession. Day 3 (D3) emerged as a putative inflection point, where the enrichment of pioneer degraders (Flavonifractor, Bacteroides) was temporally correlated with the appearance of polyphenol cleavage intermediates. This transition featured an early decrease in markers of proteolytic fermentation alongside a transient in vitro lipid-stress response. By D7, the community shifted toward a stable configuration enriched in butyrogenic taxa (Roseburia, Subdoligranulum, Megamonas), with progressive depletion of potentially opportunistic pathogens (Escherichia-Shigella). Multi-omics integration suggested that these structural successions were strongly associated with marked metabolic shifts. Inflammatory lipid markers (e.g., leukotriene B4) showed a characteristic “D3-burst/D7-clearance” pattern, whereas potentially barrier-protective metabolites, particularly 3-indolepropionic acid (3-IPA, log₂FC = 2.00) and urolithin B (log₂FC = 1.18), accumulated substantially. Conclusions: This exploratory study provides valuable high-resolution insights into astilbin’s potential as a dynamic ecological modulator. It outlines a temporal framework illustrating how the gut microbiota may shift from proteolytic fermentation toward 3-IPA-associated homeostasis. Although limited by a pooled fecal model and the absence of a vehicle control, these hypothesis-generating findings offer a solid foundation for future in vivo studies and mechanistic validations across diverse human cohorts.