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Lim, W. K.

Publications and source records attributed to Lim, W. K..

2 recordsLinked to original sources

Novel surface plasmon resonance biosensor that uses full-length Det7 phage tail protein for rapid and selective detection of Salmonella enterica serovar Typhimurium

We report a novel surface plasmon resonance (SPR) biosensor that uses the full-length Det7 phage tail protein (Det7T) to rapidly and selectively detect Salmonella enterica serovar Typhimurium. Det7T, which was obtained using recombinant protein expression and purification in Escherichia coli, demonstrated a size of [~]75 kDa upon SDS-PAGE and was homotrimeric in its native structure. Micro-agglutination and TEM data revealed that the protein specifically bound to the host, S. Typhimurium, but not to non-host E. coli K-12 cells. The observed protein agglutination occurred over a concentration range of 1.5[~]25 g.ml-1. The Det7T proteins were immobilized on gold-coated surfaces using amine-coupling to generate a novel Det7T-functionalized SPR biosensor, wherein the specific binding of these proteins with bacteria was detected by SPR. We observed rapid detection of ([~] 20 min) and typical binding kinetics with S. Typhimurium in the range of 5 x 104-5 x 107 CFU.ml-1, but not with E. coli at any tested concentration, indicating that the sensor exhibited recognition specificity. Similar binding was observed with 10% apple juice spiked with S. Typhimurium, suggesting that this strategy could be expanded for the rapid and selective monitoring of target microorganisms in the environment.

bioengineering

Digital phenotyping by consumer wearables identifies sleep-associated markers of cardiovascular disease risk and biological aging

Despite growing adoption of consumer wearables, the potential for sleep metrics from these devices to contribute to sleep-related biomedical research remains largely uncharacterized. Here we analyze sleep tracking data, along with questionnaire responses and multi-modal phenotypic data, generated from 482 normal volunteers. First, we provide a detailed comparison of wearable-derived and self-reported sleep metrics, particularly total sleep time (TST) and sleep efficiency (SE). We then identified demographic, socioeconomic and lifestyle factors associated with wearable-derived sleep duration. We also analyzed our multi-modal phenotypic data and showed that wearable-derived TST and SE are associated with various cardiovascular disease risk markers, whereas self-reported measures were not. Using whole-genome sequencing data, we estimated leukocyte telomere length and showed that volunteers with insufficient sleep also exhibit premature telomere attrition. Our study highlights the potential for sleep metrics generated by consumer wearables to provide novel insights into data generated from population cohort studies.

epidemiology