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| http://dx.doi.org/10.1093/ije/dyw194 | DOI Listing |
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FilmArray® Effectively Detects All Clades of F41 but Encounters Challenges with Other Adenovirus Species.
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Division of Microbiology, Department of Pathology and Microbiology, Nihon University School of Medicine, Tokyo, Japan. Electronic address:
The BioFire FilmArray® Gastrointestinal (GI) Panel, a widely used diagnostic tool, is designed to detect the genetic material of 22 common pathogens responsible for gastroenteritis, including viruses, bacteria, and parasites. It can detect human adenovirus (HAdV) species F, particularly serotypes F40 and F41, which are the major causes of diarrhea and mortality in children. However, its potential shortcomings in detecting other HAdV species limit its effectiveness in broader HAdV detection in clinical settings and outbreak investigations.
View Article and Find Full Text PDFIntegrating machine learning, suspect and nontarget screening reveal the interpretable fates of micropollutants and their transformation products in sludge.
J Hazard Mater
January 2025
School of Environmental Studies, China University of Geosciences, Wuhan, Hubei 430074, China; National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. Electronic address:
Activated sludge enriches vast amounts of micropollutants (MPs) when wastewater is treated, posing potential environmental risks. While standard methods typically focus on target analysis of known compounds, the identity, structure, and concentration of transformation products (TPs) of MPs remain less understood. Here, we employed a novel approach that integrates machine learning for the quantification of nontarget TPs with advanced target, suspect, and nontarget screening strategies.
View Article and Find Full Text PDFDesign of a Co doped carbon Backbone with self-grown Au nanoparticles via a 'Triple Advantage' Strategy for sensitive dopamine detection.
J Colloid Interface Sci
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State Key Laboratory of New Pharmaceutical Preparations and Excipients, Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Key Laboratory of Analytical Science and Technology of Hebei Province, College of Chemistry and Materials Science, Hebei University 071002 Baoding, PR China. Electronic address:
In this study, a Co doped polyhedral carbon skeleton (Co CN) was prepared by nitrogen carbonization using ZIF-67 as a precursor. The Co CN features a rough surface with excellent electrical conductivity, and the Co atoms exhibit unique catalytic properties. Based on these characteristics, we used Co CN as a carrier to load Au nanoparticles (NPs) onto its surface through the linkage and reduction effects of polyoxometalates (POMs).
View Article and Find Full Text PDFDistinct peripheral pro-inflammatory profile associated with tuberous sclerosis complex and epilepsy.
Epilepsia
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Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada.
Objective: Tuberous sclerosis complex (TSC) is a monogenetic disorder associated with sustained mechanistic target of rapamycin (mTOR) activation, leading to heterogeneous clinical manifestations. Epilepsy and renal angiomyolipoma are the most important causes of morbidity in adult people with TSC (pwTSC). mTOR is a key player in inflammation, which in turn could influence TSC-related clinical manifestations.
View Article and Find Full Text PDFAutomatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.
Diagn Interv Radiol
January 2025
Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.
Purpose: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.
Methods: A total of 63 eligible participants were included and randomized into training and validation groups.
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