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Background: Traumatic brain injury (TBI) and spinal cord injury (SCI) are acquired injuries to the central nervous system (CNS) caused by external forces that cause temporary or permanent sensory and motor impairments and the potential for long‐term disability or even death. These conditions currently lack effective treatments and impose substantial physical, social, and economic burdens on millions of people and families worldwide. TBI and SCI involve intricate pathological mechanisms, and the inflammatory response contributes significantly to secondary injury in TBI and SCI. It plays a crucial role in prolonging the post‐CNS trauma period and becomes a focal point for a potential therapeutic intervention. Previous research on the inflammatory response has traditionally concentrated on glial cells, such as astrocytes and microglia. However, increasing evidence highlights the crucial involvement of lymphocytes in the inflammatory response to CNS injury, particularly CD8+ T cells and NK cells, along with their downstream XCL1‐XCR1 axis. Objective: This review aims to provide an overview of the role of the XCL1‐XCR1 axis and the T‐cell response in inflammation caused by TBI and SCI and identify potential targets for therapy. Methods: We conducted a comprehensive search of PubMed and Web of Science using relevant keywords related to the XCL1‐XCR1 axis, T‐cell response, TBI, and SCI. Results: This study examines the upstream and downstream pathways involved in inflammation caused by TBI and SCI, including interleukin‐15 (IL‐15), interleukin‐12 (IL‐12), CD8+ T cells, CD4+ T cells, NK cells, XCL1, XCR1+ dendritic cells, interferon‐gamma (IFN‐γ), helper T0 cells (Th0 cells), helper T1 cells (Th1 cells), and helper T17 cells (Th17 cells). We describe their proinflammatory effect in TBI and SCI. Conclusions: The findings suggest that the XCL1‐XCR1 axis and the T‐cell response have great potential for preclinical investigations and treatments for TBI and SCI. [ABSTRACT FROM AUTHOR]

Background: A growing body of research shows that drug monomers from traditional Chinese herbal medicines have antineuroinflammatory and neuroprotective effects that can significantly improve the recovery of motor function after spinal cord injury (SCI). Here, we explore the role and molecular mechanisms of Alpinetin on activating microglia-mediated neuroinflammation and neuronal apoptosis after SCI. Methods: Stimulation of microglia with lipopolysaccharide (LPS) to simulate neuroinflammation models in vitro, the effect of Alpinetin on the release of pro-inflammatory mediators in LPS-induced microglia and its mechanism were detected. In addition, a co-culture system of microglia and neuronal cells was constructed to assess the effect of Alpinetin on activating microglia-mediated neuronal apoptosis. Finally, rat spinal cord injury models were used to study the effects on inflammation, neuronal apoptosis, axonal regeneration, and motor function recovery in Alpinetin. Results: Alpinetin inhibits microglia-mediated neuroinflammation and activity of the JAK2/STAT3 pathway. Alpinetin can also reverse activated microglia-mediated reactive oxygen species (ROS) production and decrease of mitochondrial membrane potential (MMP) in PC12 neuronal cells. In addition, in vivo Alpinetin significantly inhibits the inflammatory response and neuronal apoptosis, improves axonal regeneration, and recovery of motor function. Conclusion: Alpinetin can be used to treat neurodegenerative diseases and is a novel drug candidate for the treatment of microglia-mediated neuroinflammation. [ABSTRACT FROM AUTHOR]

Aims: To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients. Method: This was a retrospective study of perioperative medical data from patients undergoing non‐cardiac and non‐neurology surgery over 65 years old from January 2014 to August 2019. Forty‐six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC‐ROC), sensitivity, specificity, and precision. Results: In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765–0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%. Conclusions: The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model. [ABSTRACT FROM AUTHOR]