Diagnosing malaria from some symptoms: a machine learning approach and public health implications

Apicoplast-Resident Processes: Exploiting the Chink in the Armour of Plasmodium falciparum Parasites

The discovery of a relict plastid, also known as an apicoplast (apicomplexan plastid), that houses housekeeping processes and metabolic pathways critical to Plasmodium parasites' survival has prompted increased research on identifying potent inhibitors that can impinge on apicoplast-localised processes. The apicoplast is absent in humans, yet it is proposed to originate from the eukaryote's secondary endosymbiosis of a primary symbiont. This symbiotic relationship provides a favourable microenvironment for metabolic processes such as haem biosynthesis, Fe-S cluster synthesis, isoprenoid biosynthesis, fatty acid synthesis, and housekeeping processes such as DNA replication, transcription, and translation, distinct from analogous mammalian processes. Recent advancements in comprehending the biology of the apicoplast reveal it as a vulnerable organelle for malaria parasites, offering numerous potential targets for effective antimalarial therapies. We provide an overview of the metabolic processes occurring in the apicoplast and discuss the organelle as a viable antimalarial target in light of current advances in drug discovery. We further highlighted the relevance of these metabolic processes to Plasmodium falciparum during the different stages of the lifecycle.

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05.

Towards digital diagnosis of malaria: How far have we reached?

The need for precise and early diagnosis of malaria and its distinction from other febrile illnesses is no doubt a prerequisite, primarily when standard rapid diagnostic tests (RDTs) cannot be totally relied upon. At the time of disease outbreaks, the pressure on hospital staff remains high and the chances of human error increase. Therefore, in the era of digitalisation of medicine as well as diagnostic approaches, various technologies such as artificial intelligence (AI) and machine learning (ML) should be deployed to further aid the diagnosis, especially in endemic and epidemic situations. Computational techniques are now more at the forefront than ever, and the interest in developing such efficient technologies is continuously increasing. A comprehensive understanding of these digital technologies is needed to maintain the scientific rigour in these attempts. This would enhance the implementation of these novel technologies for malaria diagnosis. This review highlights the progression, strengths, and limitations of various computing techniques so far employed to diagnose malaria.