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http://dx.doi.org/10.1161/JAHA.121.021142 | DOI Listing |
Clin Chem Lab Med
January 2025
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Sinai Health System, Toronto, Canada.
Cancer screening is considered to be a major strategy for combatting cancer. The United States Preventive Services Task Force (USPSTF) recommends screening for five cancers, but the strength of evidence about the effectiveness of screening is limited. To gain insights into the efficacy of early detection requires prospective, blinded, placebo-controlled clinical trials with decades of follow-up and inclusion of millions of participants.
View Article and Find Full Text PDFJ Virus Erad
December 2024
Vancouver Infectious Diseases Center, Vancouver, British Columbia, Canada.
Background: Several clinical trials, including the recently published the GRAND PLAN study from Vancouver Infectious Diseases Center (VIDC), have demonstrated the efficacy of hepatitis C (HCV) therapy among active drug users, including those facing significant addiction-related and social challenges. In the GRAND PLAN, we documented sustained virological response post-treatment Week12 (SVR12) in 108/117 (92.3 %) individuals (108/111 (mITT) or 97.
View Article and Find Full Text PDFNicotine Tob Res
January 2025
California Tobacco Prevention Program, California Department of Public Health, Sacramento, CA, USA.
Introduction: Low-income individuals bear a disproportionate share of the burden of tobacco use. This study tested the feasibility of increasing a quitline's reach to low-income tobacco users by collaborating with 211 information and referral agencies, which primarily serve people experiencing economic hardship.
Aims And Methods: Study participants (N = 114 888) were adult tobacco users referred to the California quitline by 211 agencies, referred by healthcare clinics, or self-referred from April 17, 2021 to December 31, 2023.
J Chem Inf Model
January 2025
Ecole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, 75 005 Paris, France.
In this contribution, we examine the interplay between target definition, data distribution, featurization approaches, and model architectures on graph-based deep learning models for thermodynamic property prediction. Through consideration of five curated data sets, exhibiting diversity in elemental composition, multiplicity, charge state, and size, we examine the impact of each of these factors on model accuracy. We observe that target definition, i.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!