Peripheral Organ Injury After Stroke

Tissue-resident macrophages exacerbate lung injury after remote sterile damage

Remote organ injury, which is a common secondary complication of sterile tissue damage, is a major cause of poor prognosis and is difficult to manage. Here, we report the critical role of tissue-resident macrophages in lung injury after trauma or stroke through the inflammatory response. We found that depleting tissue-resident macrophages rather than disrupting the recruitment of monocyte-derived macrophages attenuated lung injury after trauma or stroke. Our findings revealed that the release of circulating alarmins from sites of distant sterile tissue damage triggered an inflammatory response in lung-resident macrophages by binding to receptor for advanced glycation end products (RAGE) on the membrane, which activated epidermal growth factor receptor (EGFR). Mechanistically, ligand-activated RAGE triggered EGFR activation through an interaction, leading to Rab5-mediated RAGE internalization and EGFR phosphorylation, which subsequently recruited and activated P38; this, in turn, promoted RAGE translation and trafficking to the plasma membrane to increase the cellular response to RAGE ligands, consequently exacerbating inflammation. Our study also showed that the loss of RAGE or EGFR expression by adoptive transfer of macrophages, blocking the function of RAGE with a neutralizing antibody, or pharmacological inhibition of EGFR activation in macrophages could protect against trauma- or stroke-induced remote lung injury. Therefore, our study revealed that targeting the RAGE-EGFR signaling pathway in tissue-resident macrophages is a potential therapeutic approach for treating secondary complications of sterile damage.

Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms

Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide. Stroke, a cerebrovascular disorder, is one of the leading contributors to this burden among the top three causes of death. Early recognition of symptoms can encourage a balanced lifestyle and provide essential information for stroke prediction. To identify a stroke patient and risk factors, machine learning (ML) is a key tool for physicians. Due to different data measurement scales and their probability distributional assumptions, ML-based algorithms struggle to detect risk factors. Furthermore, when dealing with risk factors with high-dimensional features, learning algorithms struggle with complexity. In this study, rigorous statistical tests are used to identify risk factors, and PCA-FA (Integration of Principal Components and Factors) and FPCA (Factor Based PCA) approaches are proposed for projecting suitable feature representations for improving learning algorithm performances. The study dataset consists of different clinical, lifestyle, and genetic attributes, allowing for a comprehensive analysis of potential risk factors associated with stroke, which contains 5110 patient records. Using significant test (P-value <0.05), chi-square and independent sample t-test identified age, heart_disease, hypertension, work_type, ever_married, bmi, and smoking_status as risk factors for stroke. To develop the predicting model with proposed feature extraction techniques, random forests approach provides the best results when utilizing the PCA-FA method. The best accuracy rate for this approach is 92.55%, while the AUC score is 98.15%. The prediction accuracy has increased from 2.19% to 19.03% compared to the existing work. Additionally, the prediction results is robustified and reproducible with a stacking ensemble-based classification algorithm. We also developed a web-based application to help doctors diagnose stroke risk based on the findings of this study, which could be used as an additional tool to help doctors diagnose.

Fatty Acid-Binding Proteins: Their Roles in Ischemic Stroke and Potential as Drug Targets

Stroke is among the leading causes of death and disability worldwide. However, despite long-term research yielding numerous candidate neuroprotective drugs, there remains a lack of effective neuroprotective therapies for ischemic stroke patients. Among the factors contributing to this deficiency could be that single-target therapy is insufficient in addressing the complex and extensive mechanistic basis of ischemic brain injury. In this context, lipids serve as an essential component of multiple biological processes and play important roles in the pathogenesis of numerous common neurological diseases. Moreover, in recent years, fatty acid-binding proteins (FABPs), a family of lipid chaperone proteins, have been discovered to be involved in the onset or development of several neurodegenerative diseases, including Alzheimer’s and Parkinson’s disease. However, comparatively little attention has focused on the roles played by FABPs in ischemic stroke. We have recently demonstrated that neural tissue-associated FABPs are involved in the pathological mechanism of ischemic brain injury in mice. Here, we review the literature published in the past decade that has reported on the associations between FABPs and ischemia and summarize the relevant regulatory mechanisms of FABPs implicated in ischemic injury. We also propose candidate FABPs that could serve as potential therapeutic targets for ischemic stroke.