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A Singh

Researcher at Massachusetts Institute of Technology

Publications -  13
Citations -  536

A Singh is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Internal medicine & Multi-task learning. The author has an hindex of 7, co-authored 11 publications receiving 460 citations. Previous affiliations of A Singh include University of Bristol & Albany Medical College.

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Conditionally immortalized human glomerular endothelial cells expressing fenestrations in response to VEGF

TL;DR: Generation of conditionally immortalized human GEnC using technology with which ci podocytes have been produced is addressed, confirming successful immortalization and retaining morphological features of early-passage primary culture GenC up to at least p41.
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Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration

TL;DR: The results demonstrate that the relative importance of different predictors varies over time, and that using multi-task learning to account for this is an appropriate way to robustly capture the temporal dynamics in EHR data.
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Relation between myocardial glutathione content and extent of ischemia-reperfusion injury.

TL;DR: The extent of myocardial injury sustained during reperfusion is very dependent on the effectiveness of its antioxidant defenses and Markedly increased susceptibility to injury occurs when the GSH content in the ischemic myocardium becomes depleted.
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Rosiglitazone enhances glucose uptake in glomerular podocytes using the glucose transporter GLUT1.

TL;DR: ROSiglitazone has a direct and protective effect on glucose uptake in wild-type human podocytes and represents a novel mechanism by which PPARγ agonists may improve podocyte function in diabetic nephropathy.

Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration

TL;DR: In this paper, a multi-task learning based approach was proposed to predict short-term progression of renal dysfunction using temporal information in electronic health records (EHRs) for predicting loss of estimated glomerular filtration rate (eGFR).