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MultiChem: predicting chemical properties using multi-view graph attention network.
BioData Min
January 2025
Department of Computer Science, Hanyang University, Seoul, Republic of Korea.
Background: Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures.
View Article and Find Full Text PDFMapping the landscape of Hospital at home (HaH) care: a validated taxonomy for HaH care model classification.
BMC Health Serv Res
January 2025
Institute Patient-Centered Digital Health, Bern University of Applied Sciences, Quellgasse 21, Biel, 2502, Switzerland.
Background: Hospital at home (HaH) care models have gained significant attention due to their potential to reduce healthcare costs, improve patient satisfaction, and lower readmission rates. However, the lack of a standardized classification system has hindered systematic evaluation and comparison of these models. Taxonomies serve as classification systems that simplify complexity and enhance understanding within a specific domain.
View Article and Find Full Text PDFComparison of physico-chemical properties of different types of orthopedic acrylic cement.
J Biomater Sci Polym Ed
January 2025
Novel Drug Delivery Systems Laboratory, Faculty of Pharmacy, Medical Sciences/University of Tehran, Tehran, Iran.
Analyzing the chemical composition of different kinds of acrylic cement is necessary to understand their properties and suitability for curing bone defects. Conducting various chemical tests can give valuable insight into the composition, viscosity, and performance characteristics of each kind of cement, Therefore, our study aimed to find safety standards and the effectiveness of these products for medical applications. The polymeric characterization was determined by Nuclear Magnetic Resonance (H-NMR) spectroscopy and Fourier-transform infrared spectroscopy (FTIR).
View Article and Find Full Text PDFA Multi-Source drug combination and Omnidirectional feature fusion approach for predicting Drug-Drug interaction events.
J Biomed Inform
January 2025
Northwest Normal University, College of Computer Science and Engineering, Lanzhou, China. Electronic address:
Background: In the medical context where polypharmacy is increasingly common, accurately predicting drug-drug interactions (DDIs) is necessary for enhancing clinical medication safety and personalized treatment. Despite progress in identifying potential DDIs, a deep understanding of the underlying mechanisms of DDIs remains limited, constraining the rapid development and clinical application of new drugs.
Methods: This study introduces a novel multimodal drug-drug interaction (MMDDI) model based on multi-source drug data and comprehensive feature fusion techniques, aiming to improve the accuracy and depth of DDI prediction.
Analysis of Method Performance for Quantitative Assessment of Listeria monocytogenes in Queso Fresco Cheese.
J Food Prot
January 2025
QuoData GmbH, Dresden, Germany.
A Proficiency Test (PT) was conducted for Food Emergency Response Network (FERN) laboratories for quantitative assessment of Listeria monocytogenes (L. monocytogenes) in queso fresco cheese. The Moffett Proficiency Test Laboratory: MPTL (organizer) prepared test samples for each participating laboratory with 10 CFU/g of L.
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