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| http://dx.doi.org/10.1016/j.resplu.2024.100812 | DOI Listing |
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Different fates between extracellular and intracellular antimicrobial resistome in full-scale activated sludge and membrane bioreactor processes.
Water Res
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Faculty of Geosciences and Civil Engineering, Kanazawa University, Kanazawa 920-1192, Japan; Center for Infectious Disease Education and Research (CiDER), Osaka University, 565-0871, Japan. Electronic address:
Treated effluent of wastewater treatment plants (WWTPs) are major sources of extracellular antimicrobial resistance genes (eARGs) into aquatic environments. This study aimed to clarify the fate and origins of eARGs from influent to treated effluent at a full-scale WWTP. The compositions of eARG and intracellular ARG (iARG) were acquired via shotgun metagenomic sequencing in influent wastewater, activated sludge, and treated effluent of the target WWTP, where identical wastewater was treated by conventional activated sludge (CAS) and membrane bioreactor (MBR) processes.
View Article and Find Full Text PDFWeight trajectories in aging humanized APOE mice with translational validity to human Alzheimer's risk population: A retrospective analysis.
PLoS One
January 2025
Center for Innovation in Brain Science, University of Arizona Health Sciences, Tucson, Arizona, United States of America.
Translational validity of mouse models of Alzheimer's disease (AD) is variable. Because change in weight is a well-documented precursor of AD, we investigated whether diversity of human AD risk weight phenotypes was evident in a longitudinally characterized cohort of 1,196 female and male humanized APOE (hAPOE) mice, monitored up to 28 months of age which is equivalent to 81 human years. Autoregressive Hidden Markov Model (AHMM) incorporating age, sex, and APOE genotype was employed to identify emergent weight trajectories and phenotypes.
View Article and Find Full Text PDFChanges in urinary concentrations of contemporary and emerging chemicals in commerce during the COVID-19 pandemic: Insights from the Environmental influences on Child Health Outcomes (ECHO) program.
PLoS One
January 2025
Department of Environmental Health Sciences, Columbia University, New York, NY, United States of America.
Previous research indicates that the COVID-19 pandemic catalyzed alterations in behaviors that may impact exposures to environmental endocrine-disrupting chemicals. This includes changes in the use of chemicals found in consumer products, food packaging, and exposure to air pollutants. Within the Environmental influences on Child Health Outcomes (ECHO) program, a national consortium initiated to understand the effects of environmental exposures on child health and development, our objective was to assess whether urinary concentrations of a wide range of potential endocrine-disrupting chemicals varied before and during the pandemic.
View Article and Find Full Text PDFEffects of mirror therapy on motor and functional recovery of the upper extremity in subacute stroke: Systematic review and meta-analysis.
PM R
January 2025
Department of Physical Therapy and Assistive Technology, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Objective: To review and synthesize existing evidence on the effect of mirror therapy (MT) on motor and functional recovery and the effect of unimanual and bimanual MT in individuals with subacute stroke.
Methodology: PubMed, Physiotherapy Evidence Database, Cochrane, and Airiti Library were searched for relevant studies. Randomized and pilot randomized controlled trials comparing MT with sham MT or conventional therapy were included.
Exploring timely and safe discharge from ICU: a comparative study of machine learning predictions and clinical practices.
Intensive Care Med Exp
January 2025
Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA.
Background: The discharge practices from the intensive care unit exhibit heterogeneity and the recognition of eligible patients for discharge is often delayed. Recognizing the importance of safe discharge, which aims to minimize readmission and mortality, we developed a dynamic machine-learning model. The model aims to accurately identify patients ready for discharge, offering a comparison of its effectiveness with physician decisions in terms of safety and discrepancies in discharge readiness assessment.
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