Molecular phenotyping of oxidative stress in diabetes mellitus with point-of-care NMR system

Abstract

Diabetes mellitus is one of the fastest-growing health burdens globally. Oxidative stress, which has been implicated in the pathogenesis of diabetes complication (e.g., cardiovascular event), remains poorly understood. We report a new approach to rapidly manipulate and evaluate the redox states of blood using a point-of-care NMR system. Various redox states of the hemoglobin were mapped out using the newly proposed (pseudo) two-dimensional map known as T1–T2 magnetic state diagram. We exploit the fact that oxidative stress changes the subtle molecular motion of water proton in the blood, and thus inducing a measurable shift in magnetic resonance relaxation properties. We demonstrated the clinical utilities of this technique to rapidly stratify diabetes subjects based on their oxidative status in conjunction to the traditional glycemic level to improve the patient stratification and thus the overall outcome of clinical diabetes care and management.

Review of Microdevices for Hemozoin-Based Malaria Detection

“…The authors concluded this in their mouse studies where a highly sensitive detection compared to the current methods was reported (less than 10 parasites/µL versus 50–200 parasites/µL and 100–200 of microscopy and RDTs, respectively) [ 80 ]. Following this unprecedented development, several similar studies were reproduced [ 47 , 86 , 87 , 88 ], and new techniques were established to improve the infected RBCs separation (using microfluidics) and exploited for drugs studies [ 89 ]. In fact, Kong et al [ 44 ] combined lab-on-a-chip microfluidics and magnetic resonance relaxometry (MRR) in order to accurately detect malaria infection (as shown in Figure 3 A).…”

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans

“…Mujumdar and Vaidehi employed pipeline based AdaBoost classifier, Gradient Boost classifier and Random forest classifier for predicting diabetes mellitus with the considered datasets 37 . Peng et al demonstrated the clinical utilities to rapidly stratify diabetes subjects based on their oxidative status in conjunction to the traditional glycemic level to improve the patient stratification and thus the overall outcome of clinical diabetes care and management 38 . Pen et al developed a new methodology for rapid, label-free molecular phenotyping of biological fluids (e.g., blood) by exploiting the recent advances in fast and highly efficient multidimensional inverse Laplace decomposition technique.…”

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.…”

“…High resolution nuclear magnetic resonance (NMR) spectroscopy is a powerful and attractive approach in biochemistry (e.g., protein analysis, 1 metabolomics, 2‐4 ) and inorganic chemistry 5 . In recent years however, with the rapid advances in NMR engineering (e.g., IC‐based spectrometer, 6‐12 microfluidic‐based chip, 13‐17 artificial intelligence 18,19 ) utilizing small foot‐print permanent magnet, time‐domain NMR instrumentations have seen a myriad of interesting applications from point‐of‐care testing (PoCT) medical diagnosis 7,13,20‐26 to industrial food science 27,28 and in‐situ oil‐gas exploration 29,30 …”