This article examines best practices and recommendations for the nutritional management of patients affected by Monkeypox (MPOX) in the Democratic Republic of Congo (DRC). It emphasizes the disease's unique pathophysiological characteristics and the contextual challenges related to food security. A literature review was conducted using reliable databases such as PubMed, Google Scholar, Mendeley, and The Lancet. Studies published between 2000 and 2024 were analyzed for their relevance and methodological quality, focusing on nutrition and viral diseases, particularly Monkeypox. The findings highlight the significance of diets rich in proteins, vitamins, minerals, and essential fatty acids to strengthen patients' immunity. Nutritional strategies include supplementation, oral rehydration, and diets tailored to complications such as dehydration and hypercatabolism. Major challenges were also identified, such as limited access to nutritious food in the DRC, logistical constraints, and socioeconomic factors exacerbating malnutrition. Proper nutritional care enhances immune response, reduces complications, and strengthens the resilience of affected populations. The article provides practical recommendations for healthcare professionals and policymakers, emphasizing the need for locally adapted interventions to optimize nutritional management and support public health efforts in this complex context.

Hypertension has become a common phenomenon. Primary factors are fast going life and tensions to keep pace with it. All these factors accounts towards heavy Antihypertensive drugs usage worldwide. The excess intake of these drugs may cause many abnormalities. Methyldopa is an antihypertensive drug, and shows strong interaction with metals. Interactions of methyldopa with some biologically important metals i.e. iron, copper, cobalt, nickel and zinc was studied by potentiometric method. Theoretical stability constant values were calculated using potentiometric titration curves and were refined through computer program “BEST”. The stoichiometry of the studied complexes was found ML2 through potentiometric method. Logβ2 values were obtained 10.0, 8.10, 8.02, 10.0, 10.0 and 9.50 for Fe(II)-Methyldopa, Fe(III)-Methyldopa, Cu(II)-Methyldopa, Co(II)-Methyldopa, Ni(II)-Methyldopa and Zn(II)-Methyldopa complexes respectively.

Night Eating Syndrome (NES) is a complex eating disorder characterized by excessive food intake during the evening and night time, often linked to emotional distress, circadian rhythm disruptions, and poor dietary habits. This paper explores the causes and consequences of NES, with a specific focus on hydration, emotional eating, and dietary strategies as potential intervention approaches. Emotional eating, a common feature of NES, is influenced by stress, mood disorders, and psychological distress, leading individuals to consume food as a coping mechanism. Additionally, dehydration plays a crucial but often overlooked role in NES, as the body may misinterpret thirst as hunger, triggering late-night cravings and reinforcing unhealthy eating patterns. Poor hydration can also contribute to fatigue, irritability, and increased emotional eating, further exacerbating NES symptoms. Effective dietary strategies, including balanced meal timing, nutrient-dense food choices, and hydration-focused interventions, may help regulate hunger cues, stabilize mood, and improve circadian alignment. This review highlights the need for a holistic approach in managing NES, incorporating psychological, nutritional, and behavioural modifications to promote healthier eating patterns and overall well-being.

This systematic review explores the applications of High-Performance Computing (HPC) in AI-driven disease prediction models, emphasizing the role of machine learning (ML) and deep learning (DL) algorithms in advancing healthcare diagnostics. HPC, by enabling rapid data processing and complex simulations, has become indispensable in analyzing large-scale medical data, such as genomic sequencing, biomedical imaging, and electronic health records (EHRs). Through the application of AI, particularly ML and DL models, healthcare systems can achieve more accurate disease predictions and personalized treatment strategies. These models, which learn from vast amounts of medical records and images, improve over time, becoming more precise in diagnosing conditions such as cancer and diabetes. This review assesses the effectiveness, scalability, and practical applications of HPC and AI in healthcare diagnostics, including drug discovery and disease progression modeling. We conducted a comprehensive literature search in multiple databases, including PubMed, Scopus, and IEEE Xplore, focusing on studies published between 2020 and 2025. After applying inclusion and exclusion criteria, 600 relevant studies were selected. The findings underscore the transformative potential of HPC and AI in healthcare, highlighting the accuracy and efficiency these technologies offer in disease prediction and treatment. Future research should focus on enhancing unsupervised learning algorithms and integrating HPC-enabled AI solutions into automated healthcare delivery systems, such as those supported by 5G networks and advanced neural networks. This review provides a critical overview of current advancements and sets the stage for further innovations in AI-driven healthcare diagnostics.

Kidney stones are a common urological condition that affect a significant portion of the global population. Their formation can be attributed to various genetic, metabolic, and environmental factors. This review discusses the pathophysiology, risk factors, clinical presentations, diagnostic methods, and treatment options for kidney stones. Furthermore, we explore the latest research trends, including minimally invasive techniques, the role of dietary modifications, and novel pharmaceutical therapies aimed at preventing stone recurrence.