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http://dx.doi.org/10.1001/jama.2024.25818 | DOI Listing |
Rev Paul Pediatr
January 2025
Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
Objective: Group A Streptococcus (GAS) are Gram-positive cocci that colonize the nasopharynx and/or skin and in rare cases may cause severe invasive infections. Although these infections decreased during the COVID-19 pandemic, some countries have observed an increased number of invasive GAS (iGAS) diseases in recent years. The objective of this study was to describe a series of iGAS diseases in a referral hospital for the treatment of pediatric infectious disease in Minas Gerais State, Brazil, between September 2022 and August 2023.
View Article and Find Full Text PDFJ Med Microbiol
January 2025
Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, UK.
Bloodstream infections (BSIs) are one of the most serious infections investigated by microbiologists. However, the time to detect a BSI fails to meet the rapidity required to inform clinical decisions in real time. Blood culture (BC) is considered the gold standard for diagnosing bloodstream infections.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Orthopaedic Surgery, Traumatology and Plastic Surgery, University Hospital Leipzig, Liebigstrasse 20, 04103 Leipzig, Germany.
Hypophosphatasemia (HPE) may be temporary (tHPE) in the context of severe diseases, such as sepsis or trauma, or it may persist (pHPE), indicating an adult form of hypophosphatasia (HPP; OMIM 171760), a rare metabolic bone disorder caused by pathogenic nucleotide variants (PNVs) in the . The aim of this study was to analyze the role of auxiliary general biomarkers in verifying low alkaline phosphatase (ALP) serum activity level as an alert parameter for PNVs in the , which are indicative of HPP. In this retrospective analysis, we examined adult patients with an ALP serum activity level below 21 U/L.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Dedalus HealthCare, Antwerp, Belgium.
Background: In recent years, machine learning (ML)-based models have been widely used in clinical domains to predict clinical risk events. However, in production, the performances of such models heavily rely on changes in the system and data. The dynamic nature of the system environment, characterized by continuous changes, has significant implications for prediction models, leading to performance degradation and reduced clinical efficacy.
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