Alzheimer’s disease (AD) presents a significant challenge to global health. It is characterized by progressive cognitive deterioration and increased rates of morbidity and mortality among older adults. Among the various pathophysiologies of AD, mitochondrial dysfunction, encompassing conditions such as increased reactive oxygen production, dysregulated calcium homeostasis, and impaired mitochondrial dynamics, plays a pivotal role. This review comprehensively investigates the mechanisms of mitochondrial dysfunction in AD, focusing on aspects such as glucose metabolism impairment, mitochondrial bioenergetics, calcium signaling, protein tau and amyloid-beta-associated synapse dysfunction, mitophagy, aging, inflammation, mitochondrial DNA, mitochondria-localized microRNAs, genetics, hormones, and the electron transport chain and Krebs cycle. While lecanemab is the only FDA-approved medication to treat AD, we explore various therapeutic modalities for mitigating mitochondrial dysfunction in AD, including antioxidant drugs, antidiabetic agents, acetylcholinesterase inhibitors (FDA-approved to manage symptoms), nutritional supplements, natural products, phenylpropanoids, vaccines, exercise, and other potential treatments.

This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHapley Additive exPlanations (SHAP) analysis. Data were collected from a national Stroke Registry from January 2014 to July 2022. Various predictive factors were considered, such as stroke severity at presentation, patient demographics, laboratory results, admission location, and other clinical features. Random forest, logistic regression, XGboost, support vector machines, and decision trees were trained and evaluated. SHAP analysis was conducted to identify key predictors. The RF model demonstrated superior performance in predicting prognosis, while LR was more effective in predicting in-hospital mortality. The National Institute of Health Stroke Score (NIHSS) and admission location were key predictors. Despite its limitations, this research underscores the importance of advancing stroke registries and emphasizes the necessity for comprehensive external validation of predictive models. Furthermore, it demonstrates the importance of initial stroke severity in influencing patient outcomes and highlights the significance of admission to stroke units in reducing poor outcomes. This may help shape interventions to enhance stroke center capacities and influence strategic policies. This study contributes towards developing more precise predictive models for hemorrhagic stroke outcomes, potentially impacting clinical practice and optimizing resource allocation significantly.

Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.

Despite the currently available treatment, one-third of epilepsy patients continue to experience seizures. Transcranial direct current stimulation (tDCS) has emerged as a potential neuromodulation approach for the non-invasive treatment of refractory epilepsy. This study aims to provide a comprehensive investigation of the efficacy and safety of tDCS in patients with drug-resistant epilepsy. The following databases were searched from inception until June 2023; PubMed, Scopus, Embase, WOS, EBSCO, Cochrane Central, and Ovid MEDLINE. Pooled mean difference was calculated for change in seizure frequency (SF), and number of Interictal epileptiform discharges (IEDs) at different follow-up intervals. We included nine parallel randomized sham-controlled trials with a total of 267 patients. Active tDCS patients had a significantly lower SF per month at 4 and 8 weeks (MD = -4.06, 95% CI [-6.01 to -2.12], p < 0.0001), and (MD = -2.66, 95% CI [-5.09 to -0.23], p = 0.03), respectively. However, weekly SF showed no statistically significant results at 4 weeks of follow-up. The IEDs were observed to significantly decline at 2, 4, and 8 weeks of follow-up. The reported adverse events were mild including mild itching and erythematous rash that resolved spontaneously. In conclusion, tDCS significantly reduced monthly SF and the number of IEDs. Future large RCTs with standard clear informed parameters are still required.

Primary central nervous system lymphoma (CNSL) is a rare, aggressive non-Hodgkin lymphoma confined to the CNS. Although radiation and chemotherapy, particularly high-dose methotrexate (HD-MTX), are effective treatments, the relapse rates remain high, prompting the exploration of novel therapeutic options. Ibrutinib, an irreversible Bruton tyrosine kinase (BTK) inhibitor, has shown promise in various B-cell malignancies, including CNSL. This systematic review and meta-analysis aimed to evaluate the safety and efficacy of ibrutinib in the treatment of CNSL, focusing on overall response (OR), complete response (CR), partial response (PR), progression-free survival (PFS), overall survival (OS), and adverse events. A comprehensive search of the PubMed, Google Scholar, and Scopus databases was conducted. The included studies were prospective and retrospective studies focusing on ibrutinib as monotherapy or in combination with CNSL. Data extraction and quality assessment were independently performed by two reviewers, and statistical analyses were conducted using R version 4.4.0. Fourteen studies (eight cohort studies and six clinical trials) involving 784 patients were included. The median age was 61 years, with nearly equal sex distribution. The meta-analysis for CNSL, the partial response rate was 29.52%, complete response rate was 49.19%, and overall response rate was 72.11%. For PCNSL, the partial response rate was 20.85%, complete response rate was 48.13%, and overall response rate was 66.92%. For SCNSL, the partial response rate was 29.42%, complete response rate was 44.64%, and overall response rate was 66.82%. Significant heterogeneity was observed in some comparisons. There were no significant differences in the efficacy of ibrutinib between CNSL subtypes. Ibrutinib shows promising efficacy in improving partial and complete response rates in CNSL. The substantial heterogeneity observed underscores the need for further well-designed studies to confirm these findings and explore the optimal use of ibrutinib in CNSL treatment protocols. Future trials should consider comparing ibrutinib to standard therapies and investigate its long-term efficacy and safety profile.