Narrow Results

Evolutionary mechanisms of adaptation to malaria are understudied in Asian endemic regions despite a high prevalence of malaria in the region. In our research, we performed a genome-wide screening for footprints of natural selection against malaria by comparing eight Asian population groups from malaria-endemic regions with two non-endemic population groups from Europe and Mongolia. We identified 285 adaptive genes showing robust selection signals across three statistical methods, iHS, XP-EHH, and PBS. Interestingly, most of the identified genes (82%) were found to be under selection in a single population group, while adaptive genes shared across populations were rare. This is likely due to the independent adaptation history in different endemic populations. The gene ontology (GO) analysis for the 285 adaptive genes highlighted their functional processes linked to neuronal organizations or nervous system development. These genes could be related to cerebral malaria and may reduce the inflammatory response and the severity of malaria symptoms. Remarkably, our novel population genomic approach identified population-specific adaptive genes potentially against malaria infection without the need for patient samples or individual medical records.

Malaria is a life-threatening mosquito-borne disease. Recently, the number of malaria cases has increased worldwide, threatening vulnerable populations. Malaria is responsible for a high rate of morbidity and mortality in people all around the world. Each year, many people, die from this disease, according to the World Health Organization (WHO). Thick and thin blood smears are used to determine parasite habitation and computer-aided diagnosis (CADx) techniques using machine learning (ML) are being used to assist. CADx reduces traditional diagnosis time, lessens socio-economic impact, and improves quality of life. This study develops a simplified model with selective features to reduce processing power and further shorten diagnostic time, which is important to resource-constrained areas. To improve overall classification results, we use a decision tree (DT)-based approach with image pre-processing called optimal features to identify optimal features. Various feature selection and extraction techniques are used, including information gain (IG). Our proposed model is compared to a benchmark state-of-art classification model. For an unseen dataset, our proposed model achieves accuracy, precision, recall, F-score, and processing time of 0.956, 0.949, 0.964, 0.956, and 9.877 s, respectively. Furthermore, our proposed model’s training time is less than those of the state-of-the-art classification model, while the performance metrics are comparable.