Publications by authors named "P B Deshpande"
Background/objectives: Maternal skin-to-skin contact (MSSC) in neonates has been shown to reduce nosocomial infections. In preterm infants, exposure to maternal skin commensals within the first 24 h may prevent colonization by hospital-acquired pathogens. However, the impact of early MSSC on skin colonization in preterm infants is unknown.
View Article and Find Full Text PDFDiscordance of 3rd and 4th generation QuantiFERON-TB Gold assays by pregnancy stages in India.
J Clin Tuberc Other Mycobact Dis
February 2025
Background: Pregnancy and HIV affect CD4+ T lymphocytes and impact performance of QuantiFERON-TB Gold (QFT). We compared the results of QFT with QuantiFERON-TB Gold Plus (QFT-Plus), which also measures CD8+ responses to TB antigens, during pregnancy and postpartum.
Methods: We screened 516 pregnant women for TB infection (TBI) with IGRA.
Article Synopsis
- Hybrid epoxy composites are considered ideal for low-density applications due to their high specific strength and stiffness, enhanced further by adding nanofillers like carbon nanotubes and graphene.
- Graphene quantum dots (GQDs) are particularly effective due to their small size, which improves the interfacial interactions and overall mechanical properties of the composite, overcoming issues commonly associated with traditional fillers.
- This study employs a molecular dynamics workflow to simulate the effects of functionalized GQDs in an epoxy matrix, revealing significant gains in strength (up to 56%) and stiffness (18%) from oxidized GQD-epoxy composites.
Pulmonary artery aneurysmal rupture is a rare cause of massive hemoptysis. When the suspected origin of bleeding is the pulmonary artery, comprehensive evaluation is necessary to determine aetiology and guide appropriate management. Behçet's disease and Hughes-Stovin Syndrome (HSS) are important differentials to consider after infections and malignancy have been ruled out.
View Article and Find Full Text PDFBackground: Accurate case length estimation is a vital part of optimizing operating room use; however, significant inaccuracies exist with current solutions. The purpose of this study was to develop and validate an artificial intelligence system for improved surgical case length prediction by applying natural language processing and machine-learning methods.
Methods: All inpatient elective surgical cases longer than 30 minutes completed between 2017 and 2023 at a single, quaternary care hospital were considered.