Comparative analysis of machine learning models for malaria detection using validated synthetic data: a cost-sensitive approach with clinical domain knowledge integration.

Malaria remains a critical global health challenge requiring innovative diagnostic approaches. Machine learning offers promising solutions for automated detection, but systematic algorithm comparison using clinically validated data remains limited. We systematically compared five machine learning models-Naive Bayes, Logistic Regression, Random Forest, XGBoost, and Enhanced Bayesian Logistic Regression-for malaria detection using a rigorously validated synthetic dataset ([Formula: see text]) representing Sub-Saharan African epidemiological conditions.

Identification of transport systems involved in eflornithine delivery across the blood-brain barrier.

Human African Trypanosomiasis (HAT) is a neglected parasitic disease that continues to persist in sub-Saharan Africa. It is fatal if untreated. The first stage of the disease is associated with the presence of the parasite in the periphery and the second stage with the presence of the parasites in the CNS.

Epidermal Growth Factor Receptor Kinase Inhibitor Ameliorates β-Amyloid Oligomer-Induced Alzheimer Disease in Swiss Albino Mice.

Alzheimer's disease (AD) is one of the major neurodegenerative disorders, and its incidence increases globally every year. Currently, available AD drugs symptomatically treat AD with multiple adverse effects. Gefitinib (GE) is an epidermal growth factor receptor (EGFR) kinase inhibitor.