A major side effect of reperfusion therapy following myocardial infarction is myocardial ischemia-reperfusion injury (MIRI). Electroacupuncture preconditioning (EA-pre) has a long history in the treatment of cardiovascular diseases. Here, we demonstrate how EA-pre attenuates MIRI by affecting the phagocytosis of neuronal dendritic spines of microglia of the fastigial nucleus (FN^microglia). We observed that EA-pre increased activity in FN^GABA and then improved myocardial injury by inhibiting abnormal activities of glutaminergic neurons of the FN (FN^Glu) during MIRI. Interestingly, we observed changes in the quantity and shape of FN microglia in mice treated with EA-pre and a decrease in the phagocytosis of FN^GABA neuronal dendritic spines by microglia. Furthermore, the effects of improving MIRI were reversed when EA-pre mice were chemically activated by intra-FN lipopolysaccharide injection. Overall, our results provide new insight indicating that EA-pre regulates microglial engulfment capacity, thus promoting the improvement of cardiac sympathetic nervous disorder during MIRI.

Ischemic stroke (IS), a leading cause of disability and mortality worldwide, remains a significant challenge due to its complex pathogenesis. Glycolysis, a central metabolic pathway, plays a critical role in bridging the gap between metabolic dysfunction and neurological impairment. During ischemic conditions, glycolysis replaces oxidative phosphorylation as the primary energy source for brain tissue. However, in the ischemia–reperfusion state, neuronal cells show a particular reliance on aerobic glycolysis. Immune cells, such as monocytes, also contribute to atheromatous plaque formation and thrombi through increased aerobic glycolysis. Given glycolysis’s involvement in various pathological stages of IS, it offers the potential for improved diagnosis, treatment, and prevention. This review comprehensively explores the role of glycolysis in different phases of IS, addresses existing controversies, and discusses its diagnostic and therapeutic applications. By elucidating the intricate relationship between glycolysis and IS, this review aims to provide novel insights for future research and clinical advancements.

Background: Dysphagia represents one of the common complications following a stroke, and post-stroke dysphagia (PSD) can lead to aspiration, pneumonia, and malnutrition, thus prolonging hospital stay, escalating medical expenditures, and imposing a substantial economic strain on both patients and society. The utilization of bibliometric analysis offers a quantitative approach for investigating the existing literature and recognizes the current status of the research. However, bibliometric analysis on the subject of PSD remains absent. Consequently, we carried out this study to provide researchers with insights, facilitating their further exploration of PSD.Methods: Conducting a bibliometric analysis of articles pertaining to PSD retrieved over the past two decades enables us to acquire the research hotspots and trends in this area. The publications concerning PSD were searched from the Core Collection of Web of Science, spanning the period ranging from 2003 to 2023. Articles or reviews published in English were included in this study. Subsequently, we employed CiteSpace and VOSviewer software to visualize the retrieved articles, thereby identifying the cooperative relationships of authors, institutions, and countries, as well as relevant information about journals and references.Results: This study comprised 866 papers in total, and the number of articles published each year shows an overall growth trend. As for the analysis of the authors, Dziewas R. was the most prolific author with 21 articles. The most frequently published institutions, countries, and journals were the University of Manchester, China, and Dysphagia, with 28, 254, and 75 publications, respectively. And the co-cited authors and journals with the highest counts were Martino R and Stroke. According to the analysis of keywords and references, dysphagia screening and assessment, prevention of pneumonia, rehabilitation approaches, and nutritional management of PSD are considered research hotpots. Additionally, future research may focus on the topics of systematic review and meta-analysis, noninvasive brain stimulation, and lesion location.Conclusion: Through the bibliometrics analysis of PSD, we can capture the research hotspots and frontiers of PSD, thereby providing inspiration and reference for subsequent studies in this field.

Background: Stroke stands as a prominent cause of mortality and disability worldwide, posing a major public health concern. Recent years have witnessed rapid advancements in artificial intelligence (AI). Studies have explored the utilization of AI in imaging analysis, assistive rehabilitation, treatment, clinical decision-making, and outcome and risk prediction concerning stroke. However, there is still a lack of systematic bibliometric analysis to discern the current research status, hotspots, and possible future development trends of AI applications in stroke.Methods: The publications on the application of AI in stroke were retrieved from the Web of Science Core Collection, spanning 2004–2024. Only articles or reviews published in English were included in this study. Subsequently, a manual screening process was employed to eliminate literature not pertinent to the topic. Visualization diagrams for comprehensive and in-depth analysis of the included literature were generated using CiteSpace, VOSviewer, and Charticulator.Results: This bibliometric analysis included a total of 2,447 papers, and the annual publication volume shows a notable upward trajectory. The most prolific authors, countries, and institutions are Dukelow, Sean P., China, and the University of Calgary, respectively, making significant contributions to the advancement of this field. Notably, stable collaborative networks among authors and institutions have formed. Through clustering and citation burst analysis of keywords and references, the current research hotspots have been identified, including machine learning, deep learning, and AI applications in stroke rehabilitation and imaging for early diagnosis. Moreover, emerging research trends focus on machine learning as well as stroke outcomes and risk prediction.Conclusion: This study provides a comprehensive and in-depth analysis of the literature regarding AI in stroke, facilitating a rapid comprehension of the development status, cooperative networks, and research priorities within the field. Furthermore, our analysis may provide a certain reference and guidance for future research endeavors.