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Interpretable machine learning models reveal the partnership of microplastics and perfluoroalkyl substances in sediments at a century scale.
J Hazard Mater
December 2024
School of Environment, Nanjing Normal University, Nanjing 210023, China; Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, Nanjing 210023, China. Electronic address:
It is challenging to explore the complex interactions between perfluoroalkyl substances (PFASs) and microplastics in lake sediments. The partnership of perfluoroalkyl substances (PFASs) and microplastics in lake sediments are difficult to determine experimentally. This study utilized sediment cores from Taihu Lake to reconstruct the coexistence history and innovatively reveal the collaboration between PFASs and microplastics by using post-hoc interpretable machine learning methods.
View Article and Find Full Text PDFAnalytical and clinical performance of eight Simoa and Lumipulse assays for automated measurement of plasma p-tau181 and p-tau217.
Alzheimers Res Ther
December 2024
Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.
Background: Among the Alzheimer's disease (AD) biomarkers measured in blood, phosphorylated forms of tau (p-tau) have been shown to exhibit a particularly high diagnostic potential. Here, we performed a comprehensive method comparison study, followed by evaluation of the diagnostic performance of eight recent plasma p-tau immunoassays targeting different tau phosphorylation sites, different tau fragments, and that are measured by two distinct platforms.
Methods: We enrolled a cohort of 40 patients with AD at the stage of dementia (AD-dem) characterized by positive CSF A + T + profile, and a control group of 40 cognitively healthy participants (Control), to conduct a comprehensive method comparison for three plasma p-tau181 and five plasma p-tau217 assays run on the Simoa HD-X™ or Lumipulse G600II/G1200 platforms.
Quality of life and quality of care experience in Australian residential aged care: a retrospective cohort study of 1,772 residents.
BMC Geriatr
December 2024
Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, Sydney, NSW, 2109, Australia.
Background: In April 2023, quality of life (QOL) and quality of care experience (QCE) indicators were introduced as mandatory indicators in Australian residential aged care (RAC) to measure and monitor wellbeing and consumer experience respectively. In this study, we used data for the initial four months after their introduction to describe QOL and QCE scores, explore related factors and assess variations by completion mode and facility.
Methods: A retrospective cohort study using electronic data (Mar-Jun 2023) from 1,772 residents in 22 RAC facilities in metropolitan Sydney, Australia.
The effect of sample site and collection procedure on identification of SARS-CoV-2 infection.
Cochrane Database Syst Rev
December 2024
NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.
Background: Sample collection is a key driver of accuracy in the diagnosis of SARS-CoV-2 infection. Viral load may vary at different anatomical sampling sites and accuracy may be compromised by difficulties obtaining specimens and the expertise of the person taking the sample. It is important to optimise sampling accuracy within cost, safety and accessibility constraints.
View Article and Find Full Text PDFPheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond.
BMC Bioinformatics
December 2024
Centro Nacional de Análisis Genómico, C/Baldiri Reixac 4, 08028, Barcelona, Spain.
Background: Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype-phenotype correlation analysis. To support these efforts, the Global Alliance for Genomics and Health (GA4GH) established Phenopackets v2 and Beacon v2 standards for storing, sharing, and discovering genomic and phenotypic data. These standards provide a consistent framework for organizing biological data, simplifying their transformation into computer-friendly formats.
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