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Abnormal shifts in global climate, leading to extreme weather, significantly threaten the safety of individuals involved in outdoor activities. Hypothermia-induced coma or death frequently occurs in clinical and forensic settings. Despite this, the precise mechanism of central nervous system injury due to hypothermia remains unclear, hindering the development of targeted clinical treatments and specific forensic diagnostic indicators. The GEO database was searched to identify datasets related to hypothermia. Post-bioinformatics analyses, DEGs, and ferroptosis-related DEGs (FerrDEGs) were intersected. GSEA was then conducted to elucidate the functions of the Ferr-related genes. Animal experiments conducted in this study demonstrated that hypothermia, compared to the control treatment, can induce significant alterations in iron death-related genes such as PPARG, SCD, ADIPOQ, SAT1, EGR1, and HMOX1 in cerebral cortex nerve cells. These changes lead to iron ion accumulation, lipid peroxidation, and marked expression of iron death-related proteins. The application of the iron death inhibitor Ferrostatin-1 (Fer-1) effectively modulates the expression of these genes, reduces lipid peroxidation, and improves the expression of iron death-related proteins. Severe hypothermia disrupts the metabolism of cerebral cortex nerve cells, causing significant alterations in ferroptosis-related genes. These genetic changes promote ferroptosis through multiple pathways. [ABSTRACT FROM AUTHOR]

The heart is the main organ of the circulatory system and requires fatty acids to maintain its activity. Stress is a contributor to aggravating cardiovascular diseases and even death, and exacerbates the abnormal lipid metabolism. The cardiac metabolism may be disturbed by stress. Cholecystokinin (CCK), which is a classical peptide hormone, and its receptor (CCKR) are expressed in myocardial cells and affect cardiovascular function. Nevertheless, under stress, the exact role of CCKR on cardiac function and cardiac metabolism is unknown and the mechanism is worth exploring. After unpredictable stress, a common stress-inducing model that induces the development of mood disorders such as anxiety and reduces motivated behavior, we found that the abnormal contraction and diastole of the heart, myocardial injury, oxidative stress and inflammation of mice were aggravated. Cholecystokinin A receptor and cholecystokinin B receptor knockout (CCK1R2R-/-) significantly reversed these changes. Mechanistically, fatty acid metabolism was found to be altered in CCK1R2R-/- mice. Differential metabolites, especially L-tryptophan, L-aspartic acid, cholesterol, taurocholic acid, ADP, oxoglutaric acid, arachidonic acid and 17-Hydroxyprogesterone, influenced cardiac function after CCK1R2R knockout and unpredictable stress. We conclude that CCK1R2R-/- ameliorated myocardial damage caused by unpredictable stress via altering fatty acid metabolism. [ABSTRACT FROM AUTHOR]

Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy. [ABSTRACT FROM AUTHOR]

Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug–disease, drug–protein and protein–disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug–disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug–disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug–disease associations especially for new diseases. [ABSTRACT FROM AUTHOR]

Glia maturation factor-β (GMF-β) has been reported to promote glial differentiation, and act as a negative prognostic indicator in certain cancers. However, its roles in glioma progression remain unclear. Since neurogenesis and vasculogenesis were proved to share some common regulators during gliomagenesis, we aim to explore the potential impact of GMF-β on tumor neovascularization and patient survival in glioma. In this study, we first detected GMF-β expression not only in tumor cells but also in microvascular endothelia by double immunohistochemical staining. Both tumoral and endothelial GMF-β expression levels were positively correlated with tumor grade and microvessel density (MVD), while negatively associated with poor prognoses of the patients. Interestingly, multivariate analysis demonstrated that endothelial GMF-β expression level was the only independent predictor of progression-free and overall survival of glioma patients. The results of in vitro angiogenesis assay showed that GMF-β knockdown significantly inhibited tubulogenesis of human U87 glioblastoma cells. Furthermore, GMF-β knockdown suppressed tumor growth and the formation of human-CD31 positive (glioma cell-derived) microvessels in a mouse orthotopic U87 glioma model. Our results demonstrated that GMF-β is an important player in glioma progression via promoting neovascularization. GMF-β may therefore be a novel prognostic marker as well as a potential therapeutic target for glioma.