Publications by authors named "P P Rupasinghe"

Unlabelled: Previous work reported unprecedented differences in the intrinsic susceptibility of the complex (MTBC) to pretomanid (Pa) using the Mycobacteria Growth Indicator Tube (MGIT) system. We tested 125 phylogenetically diverse strains from all known MTBC lineages (1-9) without known Pa resistance mutations and four strains with known resistance mutations as controls. This confirmed that MTBC, unlike most bacteria-antimicrobial combinations, displayed substantial differences in the intrinsic susceptibility relative to the technical variation of Pa MIC testing.

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Background: Treatment for fluoroquinolone-resistant multidrug-resistant/rifampicin-resistant tuberculosis (pre-XDR TB) often lasts longer than treatment for less resistant strains, yields worse efficacy results, and causes substantial toxicity. The newer anti-tuberculosis drugs, bedaquiline and delamanid, and repurposed drugs clofazimine and linezolid, show great promise for combination in shorter, less-toxic, and effective regimens. To date, there has been no randomized, internally and concurrently controlled trial of a shorter, all-oral regimen comprising these newer and repurposed drugs sufficiently powered to produce results for pre-XDR TB patients.

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Background: Fluoroquinolones (FQs) have substantial activity against the Mycobacterium tuberculosis complex (MTBc) by preventing bacterial DNA synthesis through DNA gyrase inhibition. The reference standard for FQ-resistance testing is phenotypic drug-susceptibility testing (pDST) based on growth inhibition of MTBc in drug-containing Mycobacteria Growth Indicator Tube system (MGIT) media at a critical concentration (CC) that differentiates phenotypically wild-type from nonwild-type MTBc and at a clinical breakpoint that identifies strains that will likely still respond to treatment at higher doses. Despite the recent introduction of powerful new TB drugs, highly sensitive detection of clinically defined FQ resistance remains key.

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Background: Hemolytic uremic syndrome (HUS) is a rare but challenging disease with varying degrees of mortality and prognosis. We aim to evaluate the trends and outcomes of hospitalizations due to HUS by utilizing a large population-based dataset.

Methods: We derived a study cohort from the Nationwide Inpatient Sample (NIS) for the years 2007-2018.

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