Publications by authors named "B Nag"

Article Synopsis
  • Central line-associated bloodstream infection (CLABSI) rates in Latin American ICUs are significantly higher than in high-income countries, prompting a need for intervention.
  • The INICC multidimensional approach, which includes an 11-component bundle, was implemented across 122 ICUs in nine Asian countries, resulting in a substantial decrease in CLABSI rates from 16.64 to 2.18 over 29 months.
  • The intervention not only reduced CLABSI rates by 87% but also significantly lowered the all-cause in-ICU mortality rate from 13.23% to 10.96%.
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Background: Identify urinary catheter (UC)-associated urinary tract infections (CAUTI) incidence and risk factors (RF) in 235 ICUs in 8 Asian countries: India, Malaysia, Mongolia, Nepal, Pakistan, the Philippines, Thailand, and Vietnam.

Methods: From January 1, 2014, to February 12, 2022, we conducted a prospective cohort study. To estimate CAUTI incidence, the number of UC days was the denominator, and CAUTI was the numerator.

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The multifold Sonogashira coupling of a class of aryl halides with arylacetylene in the presence of an equivalent of CsCO has been accomplished using a combination of Pd(CHCN)Cl (0.5 mol %) and cataCXium A (1 mol %) under copper-free and amine-free conditions in a readily available green solvent at room temperature. The protocol was used to transform several aryl halides and alkynes to the corresponding coupled products in good to excellent yields.

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Article Synopsis
  • The study aimed to analyze the rates and risk factors of central line-associated bloodstream infections (CLABSI) across 281 ICUs in 9 Asian countries from 2004 to 2022.
  • Out of 150,142 patients, a total of 1514 CLABSIs were recorded, with an overall infection rate of 5.08 per 1000 central line days, highest in femoral and temporary hemodialysis catheters.
  • Key risk factors for CLABSI included longer hospital stays before infection, tracheostomy use, hospitalization type, and facility ownership, particularly in publicly-owned and lower-middle-income country facilities.
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Decision-making in the peer-to-peer loan market has not been studied as extensively as traditional lending mostly because of the perceived risk in dealing with low credit borrowers seeking funding alternatives. We develop a machine learning-based approach to test the viability and usefulness in peer-to-peer loan repayment predictions among low credit borrowers. This analysis provides potential benefits that could strengthen the lending market with a more reliable method of identifying applications from promising candidates with low credit.

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