Vol. 5 No. 07 (2025): Volume 05 Issue 07

Moringa oleifera Lam., frequently lauded as the "miracle tree," is globally recognized for its exceptional nutritional value and a wide array of medicinal attributes. While its role as a nutritional powerhouse is extensively documented, a burgeoning body of evidence highlights the significant neuroprotective capabilities inherent in its extracts, particularly its oil. This article aims to comprehensively explore the potential of Moringa oleifera oil (MOO) to intricately modulate various cellular signaling pathways that are critically involved in the pathogenesis of neurodegenerative conditions. We meticulously examine its multifaceted actions, including its potent antioxidant, anti-inflammatory, and anti-apoptotic properties, and investigate how these diverse characteristics collectively contribute to the maintenance and enhancement of brain health. The review integrates the latest scientific discoveries concerning MOO's influence on crucial biological processes such as oxidative stress, inflammation, and cellular survival mechanisms, thereby underscoring its profound promise as a potential therapeutic agent in the challenging landscape of neurological disorders.

Early-stage chronic disease prediction is a critical aspect of healthcare that allows for timely interventions and personalized treatment, ultimately improving patient outcomes. In this study, we explore the use of deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, to predict the early stages of chronic diseases such as diabetes, cardiovascular diseases, and respiratory conditions. We compare the performance of LSTM with traditional machine learning models, including Random Forest, Gradient Boosting Machines (GBM), and Logistic Regression. The results show that LSTM outperforms the other models in terms of accuracy, precision, recall, F1-score, and AUC, demonstrating its superior ability to capture complex, temporal dependencies in medical data. The study highlights the potential of deep learning for early disease detection and its implications for personalized medicine, telemedicine, and healthcare optimization. However, challenges related to data quality, interpretability, and model generalization across diverse populations remain, and future work should address these issues to enhance the real-world applicability of AI-driven healthcare solutions.