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SP-DTI: Subpocket-Informed Transformer for Drug-Target Interaction Prediction.
Bioinformatics
January 2025
School of Engineering, Westlake University, Hangzhou, 310024, China.
Motivation: Drug-target interaction (DTI) prediction is crucial for drug discovery, significantly reducing costs and time in experimental searches across vast drug compound spaces. While deep learning has advanced DTI prediction accuracy, challenges remain: (i) existing methods often lack generalizability, with performance dropping significantly on unseen proteins and cross-domain settings; (ii) current molecular relational learning often overlooks subpocket-level interactions, which are vital for a detailed understanding of binding sites.
Results: We introduce SP-DTI, a subpocket-informed transformer model designed to address these challenges through: (i) detailed subpocket analysis using the Cavity Identification and Analysis Routine (CAVIAR) for interaction modeling at both global and local levels, and (ii) integration of pre-trained language models into graph neural networks to encode drugs and proteins, enhancing generalizability to unlabeled data.
Common Questions and Misconceptions About Energy Drinks: What Does the Scientific Evidence Really Show?
Nutrients
December 2024
College of Health Professions and Sciences, University of Central Florida, Orlando, FL 32816, USA.
Energy drinks are a commonly consumed beverage, and studies suggest a possible performance-enhancing effect. A Google Scholar search using the keywords "energy drinks" and "exercise" yields numerous results, underscoring the voluminous research on this topic. However, there are questions regarding the effectiveness and safety of energy drinks.
View Article and Find Full Text PDFA novel machine learning-based immune prognostic signature for improving clinical outcomes and guiding therapy in colorectal cancer: an integrated bioinformatics and experimental study.
BMC Cancer
January 2025
School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, 215011, China.
Immune cells are pivotal components in the tumor microenvironment (TME), which can interact with tumor cells and significantly influence cancer progression and therapeutic outcomes. Therefore, classifying cancer patients based on the status of immune cells within the TME is increasingly recognized as an effective approach to identify prognostic biomarkers, paving the way for more effective and personalized cancer treatments. Considering the high incidence and mortality of colorectal cancer (CRC), in this study, an integrated machine learning survival framework incorporating 93 different algorithmic combinations was utilized to determine the optimal strategy for developing an immune-related prognostic signature (IRPS) based on the average C-index across the four CRC cohorts.
View Article and Find Full Text PDFCaffeine Responsiveness Before and After Pilates Training in Healthy Older Adults.
J Chiropr Med
December 2024
Post-Graduate Nutrition Program, Faculty of Nutrition, Federal University of Alagoas, Maceió, Brazil.
Objective: The study aimed to assess responsiveness to the effects of acute caffeine intake after 8 weeks of Pilates intervention in healthy older adults.
Methods: Fifteen healthy older adults performed physical performance regarding daily practice, strength, and balance tests after ingestion of acute 5 mg/kg of caffeine or placebo before and after Pilates training.
Results: The caffeine intake reduced, regardless of Pilates training, the time in 10-m walk test (before placebo vs caffeine, 6.
Twin Screw Melt Granulation of Simvastatin: Drug Solubility and Dissolution Rate Enhancement Using Polymer Blends.
Pharmaceutics
December 2024
Department of Pharmaceutics and Drug Delivery, School of Pharmacy, The University of Mississippi, Oxford, MS 38677, USA.
This study evaluates the efficacy of twin screw melt granulation (TSMG), and hot-melt extrusion (HME) techniques in enhancing the solubility and dissolution of simvastatin (SIM), a poorly water-soluble drug with low bioavailability. Additionally, the study explores the impact of binary polymer blends on the drug's miscibility, solubility, and in vitro release profile. SIM was processed with various polymeric combinations at a 30% / drug load, and a 1:1 ratio of binary polymer blends, including Soluplus (SOP), Kollidon K12 (K12), Kollidon VA64 (KVA), and Kollicoat IR (KIR).
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