Association between psychiatric disorders and intracranial aneurysms: evidence from Mendelian randomization analysis.

Objective: Several studies have explored the relationship between intracranial aneurysms and psychiatric disorders; nevertheless, the causal connection remains ambiguous. This study aimed to evaluate the causal link between intracranial aneurysms and specific psychiatric disorders. Methods: A two-sample Mendelian randomization (MR) analysis was conducted utilizing aggregated genome-wide association study (GWAS) data from the International Stroke Genetics Association for Intracranial Aneurysms (IAs), unruptured Intracranial Aneurysm (uIA), and aneurysmal Subarachnoid Hemorrhage (aSAH). Psychiatric disorder data, encompassing Schizophrenia (SCZ), Bipolar Disorder (BD), and Panic Disorder (PD), were sourced from the Psychiatric Genomics Consortium (PGC), while Cognitive Impairment (CI) data, comprising Cognitive Function (CF) and Cognitive Performance (CP), were obtained from IEU OpenGWAS publications. Causal effects were evaluated using inverse variance weighted (IVW), MR-Egger, and weighted median methods, with the robustness of findings assessed via sensitivity analyses employing diverse methodological approaches. Results: Our MR analysis indicated no discernible causal link between intracranial aneurysm (IA) and an elevated susceptibility to psychiatric disorders. However, among individuals with genetically predisposed unruptured intracranial aneurysms (uIA), there was a modest reduction in the risk of SCZ (IVW odds ratio [OR] = 0.95, 95% confidence interval [CI] 0.92-0.98, p = 0.0002). Similarly, IAs also exhibited a moderate reduction in SCZ risk (OR = 0.92, 95% CI 0.86-0.99, p = 0.02). Nevertheless, limited evidence was found to support a causal association between intracranial aneurysms and the risk of the other three psychiatric disorders. Conclusion: Our findings furnish compelling evidence suggesting a causal influence of intracranial aneurysms on psychiatric disorders, specifically, both IAs and uIA exhibit a negative causal association with SCZ.

Synthetic minority over-sampling technique-enhanced machine learning models for predicting recurrence of postoperative chronic subdural hematoma.

Objective: Chronic subdural hematoma (CSDH) is a neurological condition with high recurrence rates, primarily observed in the elderly population. Although several risk factors have been identified, predicting CSDH recurrence remains a challenge. Given the potential of machine learning (ML) to extract meaningful insights from complex data sets, our study aims to develop and validate ML models capable of accurately predicting postoperative CSDH recurrence. Methods: Data from 447 CSDH patients treated with consecutive burr-hole irrigations at Wenzhou Medical University's First Affiliated Hospital (December 2014-April 2019) were studied. 312 patients formed the development cohort, while 135 comprised the test cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to select crucial features associated with recurrence. Eight machine learning algorithms were used to construct prediction models for hematoma recurrence, using demographic, laboratory, and radiological features. The Border-line Synthetic Minority Over-sampling Technique (SMOTE) was applied to address data imbalance, and Shapley Additive Explanation (SHAP) analysis was utilized to improve model visualization and interpretability. Model performance was assessed using metrics such as AUROC, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis (DCA). Results: Our optimized ML models exhibited prediction accuracies ranging from 61.0% to 86.2% for hematoma recurrence in the validation set. Notably, the Random Forest (RF) model surpassed other algorithms, achieving an accuracy of 86.2%. SHAP analysis confirmed these results, highlighting key clinical predictors for CSDH recurrence risk, including age, alanine aminotransferase level, fibrinogen level, thrombin time, and maximum hematoma diameter. The RF model yielded an accuracy of 92.6% with an AUC value of 0.834 in the test dataset. Conclusion: Our findings underscore the efficacy of machine learning algorithms, notably the integration of the RF model with SMOTE, in forecasting the recurrence of postoperative chronic subdural hematoma. Leveraging the RF model, we devised an online calculator that may serve as a pivotal instrument in tailoring therapeutic strategies and implementing timely preventive interventions for high-risk patients.

NeuroD4 converts glioblastoma cells into neuron-like cells through the SLC7A11-GSH-GPX4 antioxidant axis.

Cell fate and proliferation ability can be transformed through reprogramming technology. Reprogramming glioblastoma cells into neuron-like cells holds great promise for glioblastoma treatment, as it induces their terminal differentiation. NeuroD4 (Neuronal Differentiation 4) is a crucial transcription factor in neuronal development and has the potential to convert astrocytes into functional neurons. In this study, we exclusively employed NeuroD4 to reprogram glioblastoma cells into neuron-like cells. In vivo, the reprogrammed glioblastoma cells demonstrated terminal differentiation, inhibited proliferation, and exited the cell cycle. Additionally, NeuroD4 virus-infected xenografts exhibited smaller sizes compared to the GFP group, and tumor-bearing mice in the GFP+NeuroD4 group experienced prolonged survival. Mechanistically, NeuroD4 overexpression significantly reduced the expression of SLC7A11 and Glutathione peroxidase 4 (GPX4). The ferroptosis inhibitor ferrostatin-1 effectively blocked the NeuroD4-mediated process of neuron reprogramming in glioblastoma. To summarize, our study demonstrates that NeuroD4 overexpression can reprogram glioblastoma cells into neuron-like cells through the SLC7A11-GSH-GPX4 signaling pathway, thus offering a potential novel therapeutic approach for glioblastoma.