Results 271 to 280 of about 3,255,070 (396)

Post-COVID-19-pandemic changes and clinical characteristics of invasive group a streptococcal infections from 2015 to 2023. [PDF]

Infection
Tomidis Chatzimanouil MK, Rößler S, Nurjadi D, Iakovidis I, Berner R, Toepfner N, Bornstein TDGASSGSR, Aschoff R, Bornhäuser M, Güldner A, Gunzer F, Herold J, Schultz J, Wimberger P, Zahnert T.   +14 more
europepmc   +1 more source

Strong correspondence of the vaginal and rectal load of group B streptococci in pregnant women [PDF]

, 2013
Abdullah El Aila, Nabil, Claeys, Geert, Cools, Piet, Deschaght, Pieter, Lopes dos Santos Santiago, Guido, Vaneechoutte, Mario   +5 more
core   +2 more sources

A CLUSTER OF LATE ONSET GROUP B STREPTOCOCCAL INFECTIONS IN LOW BIRTH WEIGHT PREMATURE INFANTS

, 1986
J. John Weems, William R. Jarvis, G. Colman   +2 more
openalex   +1 more source

Clinical factors associated with pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections.

Jornal de Pediatria, 2012
T. Murphy, E. Storch, A. Lewin, Paula J. Edge, W. Goodman   +4 more
semanticscholar   +1 more source

Alterations and mechanistic insights of gut microbiota and its metabolites in type 2 diabetes mellitus and Alzheimer's disease

iMetaOmics, EarlyView.
This narrative review uniquely addresses how gut microbiota‐derived metabolites mediate overlapping pathologies of insulin resistance, neuroinflammation, and amyloidogenesis in type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD), proposing a framework for dual therapeutic targeting.
Guangyi Xu, Yu An, Yage Du, Zhaoming Cao, Jie Zheng, Jingya Wang, Tingyi Li, Xingen Lei, Yanhui Lu   +8 more
wiley   +1 more source

Invasive group A streptococcal infections in adults, France (2006-2010).

Clinical Microbiology and Infection, 2011
C. Plainvert, A. Doloy, J. Loubinoux, A. Lepoutre, G. Collobert, G. Touak, P. Trieu-Cuot, A. Bouvet, C. Poyart   +8 more
semanticscholar   +1 more source

Saliva MicroAge: A salivary microbiome based machine learning model for noninvasive aging assessment and health state prediction

iMetaOmics, EarlyView.
Saliva MicroAge is a machine learning‐based model designed to estimate biological age and assess health status using globally sourced salivary microbiome data. Trained on 4532 healthy samples, the model achieves high accuracy in predicting chronological age and captures health‐related deviations (MicroAgeGap) in various diseases.
Tiansong Xu, Yuting Niu, Chenyu Deng, Yoo Cheung, Yuman Li, Zhewen Hu, Shiyu Sun, Yiming Chen, Fan He, Gai Yang, Feng Chen, Chenggang Duan, Ying Huang, Xuliang Deng   +13 more
wiley   +1 more source

Group A Streptococcal Infections in Pediatric Age: Updates about a Re-Emerging Pathogen. [PDF]

Pathogens
Di Pietro GM, Marchisio P, Bosi P, Castellazzi ML, Lemieux P.   +4 more
europepmc   +1 more source

RTTAP: Empowering metatranscriptomic data analysis with a read‐based total‐infectome taxonomic solution

iMetaOmics, EarlyView.
Metatranscriptomic data analysis is a complex task due to its sheer volume and the need for sophisticated bioinformatics tools. To address this, we developed the Read‐based Total‐infectome Taxonomic Analysis Pipeline (RTTAP), an automated pipeline for metatranscriptomic data analysis that eliminates the need for users to manually select databases ...
Wei Jiang, Herui Liao, Mang Shi, Liangjun Chen, Yanni Sun   +4 more
wiley   +1 more source

Group A streptococcal infections in children

, 2012
A. Revelas, Odysseas Taxmazidis
semanticscholar   +1 more source