Rapid phenotyping towards personalized malaria medicine

Abstract

Malaria is major public health concerns which continues to claim the lives of more than 435,000 people each year. The challenges with anti-malarial drug resistance and detection of low parasitaemia forms an immediate barrier to achieve the fast-approaching United Nations Sustainable Development Goals of ending malaria epidemics by 2030. In this Opinion article, focusing on the recent published technologies, in particularly the nuclear magnetic resonance (NMR)-based diagnostic technologies, the authors offer their perspectives and highlight ways to bring these pointof-care technologies towards personalized medicine. To this end, they advocate an open sourcing initiative to rapidly close the gap between technological innovations and field implementation.

Clustering Nuclear Magnetic Resonance: Machine learning assistive rapid two‐dimensional relaxometry mapping

“…This methodology is highly disruptive to low‐field NMR applications, in particularly, to the recent reported NMR‐based PoCT medical diagnostic. These include the immuno‐magnetic labeled detection (e.g., tumor cells, 14,20,58 tuberculosis 44 and magneto‐DNA detection of bacteria 59 ) and the label‐free detection of various pathological states (e.g., blood oxygenation 15 /oxidation level, 10 malaria screening, 12,21,22,60 and diabetes mellitus 24 ). Interestingly, with the recent advances on machine learning technique, it has become remarkably efficient that a large data set that runs in (almost) ‘real‐time mode’ is possible.…”

Clustering NMR: Machine learning assistive rapid two-dimensional relaxometry mapping

“…This methodology is highly distruptive to the low-field NMR applications, in particularly, the recent reported NMR-based PoCT medical diagnostic. These include the immuno-magnetic labelled detection (e.g., tumour cells [14,20] , tuberculosis [37] and magneto-DNA detection of bacteria [38] ) and the label-free detection of various pathological states (e.g., blood oxygenation [15] /oxidation level [10] and malaria screening [21,22,39] ). Interestingly, with the recent advances on machine learning technique, it has become remarkably efficient that a large data run in almost in real-time mode , which open-up opportunity to combine real-time NMR (or MRI) with machine learning simultaneously.…”

Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis

“…Similarly, the same cell in different organisms of the same species has its microenvironment and genetic polymorphs/mutants that result in a personalized response to a similar stimulus or exposure, engendering the need for precision medicines. However, the biological system is so vastly variant [ 113 , 114 ] and dynamic that the key to designing an effective treatment or cure is knowledge [ 115 ]. Knowing all of the possible omics and their integration is key to understanding the organism as per its biochemical and genetic makeup, but with vast knowledge, the biggest hurdle is handling the enormous variants involved in making sense out of data without introducing false discoveries.…”