Strokes are mainly caused by thromboembolic obstruction of a major cerebral artery. Major clinical manifestations include paralysis hemiplegia, aphasia, memory, and learning disorders. In the case of ischemic stroke (IS), hyperactive platelets contribute to advancing an acute thrombotic event progression. Therefore, the principal goal of treatment is to recanalize the occluded vessel and restore cerebral blood flow by thrombolysis or mechanical thrombectomy. However, antiplatelets or thrombolytic therapy may increase the risk of bleeding. Beyond the involvement in thrombosis, platelets also contribute to the inflammatory process induced by cerebral ischemia. Platelet-mediated thrombosis and inflammation in IS lie primarily in the interaction of platelet receptors with endothelial cells and immune cells, including T-cells, monocytes/macrophages, and neutrophils. Following revascularization, intervention with conventional antiplatelet medicines such as aspirin or clopidogrel does not substantially diminish infarct development, most likely due to the limited effects on the thrombo-inflammation process. Emerging evidence has shown that T cells, especially regulatory T cells (Tregs), maintain immune homeostasis and suppress immune responses, playing a critical immunomodulatory role in ischemia-reperfusion injury. Hence, considering the deleterious effects of inflammatory and immune responses, there is an urgent need for more targeted agents to limit the thrombotic-inflammatory activity of platelets and minimize the risk of a cerebral hemorrhage. This review highlights the involvement of platelets in neuroinflammation and the evolving role of Tregs and platelets in IS. In response to all issues, preclinical and clinical strategies should generate more viable therapeutics for preventing and managing IS with immunotherapy targeting platelets and Tregs.

As natural functional bioactive ingredients found in foods and plants, polyphenols play various antioxidant and anti-inflammatory roles to prevent the development of disease and restore human health. The multi-target modulation of polyphenols provides a novel practical therapeutic strategy for neurodegenerative diseases that are difficult to treat with traditional drugs like glutathione and cholinesterase inhibitors. This review mainly focuses on the efficacy of polyphenols on ischemic stroke, Parkinson's disease and Alzheimer's disease, including in vivo and in vitro experimental studies. It is further emphasized that polyphenols exert neuroprotective effects primarily through inhibiting production of oxidative stress and inflammatory cytokines, which may be the underlying mechanism. However, polyphenols are still rarely used as medicines to treat neurodegenerative diseases. Due to the lack of clinical trials, the mechanism of polyphenols is still in the stage of insufficient exploration. Future large-scale multi-center randomized controlled trials and in-depth mechanism studies are still needed to fully assess the safety, efficacy and side effects of polyphenols.

Falls are the main contributor to both fatal and nonfatal injuries in elderly individuals as well as significant sources of morbidity and mortality, which are mostly induced by impaired balance control. The ability to keep balance is a remarkably complex process that allows for rapid and precise changes to prevent falls with multiple systems involved, such as musculoskeletal system, the central nervous system and sensory system. However, the exact pathogenesis of falls caused by balance disorders in the elderly has eluded researchers to date. In consideration of aging phenomenon aggravation and fall risks in the elderly, there is an urgent need to explore the pathogenesis and treatments of falls caused by balance disorders in the elderly. The present review discusses the epidemiology of falls in the elderly, potential pathogenic mechanisms underlying multiple systems involved in falls caused by balance disorders, including musculoskeletal system, the central nervous system and sensory system. Meanwhile, some common treatment strategies, such as physical exercise, new equipment based on artificial intelligence, pharmacologic treatments and fall prevention education are also reviewed. To fully understand the pathogenesis and treatment of falls caused by balance disorders, a need remains for future large-scale multi-center randomized controlled trials and in-depth mechanism studies.

Autism Spectrum Disorder(ASD) is a set of neurodevelopmental conditions that affect patients' social abilities. In recent years, many studies have employed deep learning to diagnose this brain dysfunction through functional MRI (fMRI). However, existing approaches solely focused on the abnormal brain functional connections but ignored the impact of regional activities. Due to this biased prior knowledge, previous diagnosis models suffered from inter-site heterogeneity and inter-individual phenotypic differences. To address this issue, we propose a novel feature extraction method for fMRI that can learn a personalized lower-resolution representation of the entire brain networking regarding both the functional connections and regional activities. Specifically, we abstract the brain imaging as a graph structure and straightforwardly downsample it to sparse substructures by hierarchical graph pooling. The down-scaled feature vectors are embedded into a population graph where the hidden inter-subject heterogeneity and homogeneity are explicitly expressed as inter-and intra-community connectivity differences. Subsequently, we fuse the imaging and non-imaging information by graph convolutional networks (GCN), which recalibrates features to node embeddings under phenotypic statistics. By these means, our framework can extract features directly and efficiently from the entire fMRI and be aware of implicit inter-individual variance. We have evaluated our framework on the ABIDE-I dataset with 10-fold cross-validation. The present model has achieved a mean classification accuracy of 85.95% and a mean AUC of 0.92, better than the state-of-the-art methods. Our code and support documents are available at github.com/jhonP-Li/ASD_GP_GCN

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