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MVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph.
J Chem Inf Model
January 2025
School of Computer Science and Technology, Soochow University, Jiangsu 215006, China.
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views.
View Article and Find Full Text PDFAssociations between fear of COVID-19 and mental health in Ghana: A sequential mediation model.
Heliyon
January 2025
Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, 1, University Rd., Tainan, 701401, Taiwan.
Introduction: Although the coronavirus disease 2019 (COVID-19) pandemic has ceased globally, individuals may still suffer from various psychological burdens in the post-COVID-19 era. The present observational cross-sectional study investigated how fear of COVID-19 can affect mental health through mediators including stress, perceived stigma, and preventive behaviors among young adults in Ghana.
Methods: A total of 635 participants aged between 18 and 29 years (mean age = 20.
"New Normal:" Opportunities and Challenges Faced by Syringe Service Programs Following the Effects of the COVID-19 Pandemic.
Subst Use Misuse
January 2025
Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, USA.
Syringe services programs (SSPs) provide critical evidence-based public health services that decrease harms from drug use for people who use drugs (PWUD). Many SSPs have experienced significant and evolving COVID-19-related disruptions. We aimed to characterize the impacts of COVID-19 on SSP operations in the United States approximately two years into the pandemic.
View Article and Find Full Text PDFAssessing the impact of the COVID-19 pandemic on small and medium businesses in North Carolina: Results from the Carolina PROSPER COVID-19 Worksite Impact Survey.
J Occup Environ Med
November 2024
Department of Health Behavior, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Objective: As the COVID-19 pandemic presented new challenges for businesses and worker safety and health, an interdisciplinary team launched the COVID-19 Worksite Impact Survey to assess COVID-19-related impacts and responses at small and medium businesses in 10 North Carolina counties.
Methods: We collected data from October 2 to December 1, 2020, and analyzed survey results to evaluate businesses' operational changes, concerns, needs, pandemic preparedness, workplace health promotion programming, and infection control practices.
Results: Most businesses, including essential ones, were inadequately prepared for the pandemic and did not implement the most effective COVID-19 infection control practices.
In silico evaluation of bisphosphonates identifies leading candidates for SARS-CoV-2 RdRp inhibition.
J Mol Graph Model
January 2025
School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia; Department of Health Sciences, University of York, York, YO10 5DD, UK. Electronic address:
The novel coronavirus disease (COVID-19) pandemic has resulted in 777 million confirmed cases and over 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Post Acute Sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well.
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