The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.
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http://dx.doi.org/10.1007/s11356-023-26220-0 | DOI Listing |
Addict Sci Clin Pract
January 2025
Department of Medicine, Division of General Internal Medicine, University of Washington/Harborview Medical Center, 325 9Th Avenue, Box 359780, Seattle, WA, 98104, USA.
Background: Initiation of buprenorphine for treatment of opioid use disorder (OUD) in acute care settings improves access and outcomes, however patients who use methamphetamine are less likely to link to ongoing treatment. We describe the intervention and design from a pilot randomized controlled trial of an intervention to increase linkage to and retention in outpatient buprenorphine services for patients with OUD and methamphetamine use who initiate buprenorphine in the hospital.
Methods: The study is a two-arm pilot randomized controlled trial (N = 40) comparing the mHealth Incentivized Adherence Plus Patient Navigation (MIAPP) intervention to treatment as usual.
Trials
January 2025
Université Côte d'Azur, CNRS, LP2M, Nice, France.
Background: /aims. Pseudoxanthoma Elasticum (PXE, OMIM 264800) is an autosomal, recessive, metabolic disorder characterized by progressive ectopic calcification in the skin, the vasculature and Bruch's membrane. Variants in the ABCC6 gene are associated with low plasma pyrophosphate (PPi) concentration.
View Article and Find Full Text PDFTrials
January 2025
Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA.
Background: Phase-3 clinical trials provide the highest level of evidence on drug safety and effectiveness needed for market approval by implementing large randomized controlled trials (RCTs). However, 30-40% of these trials fail mainly because such studies have inadequate sample sizes, stemming from the inability to obtain accurate initial estimates of average treatment effect parameters.
Methods: To remove this obstacle from the drug development cycle, we present a new algorithm called Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE) that powers a parallel-group trial, a standard RCT design, by leveraging a state-of-the-art hypothesis testing strategy and a novel trend-adaptive design (TAD).
Sci Rep
January 2025
Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, 11461, Riyadh, Saudi Arabia.
Quantitative structure-property relationship (QSPR) modeling has emerged as a pivotal tool in the field of medicinal chemistry and drug design, offering a predictive framework for understanding the correlation between chemical structure and physicochemical properties. Topological indices are mathematical descriptors derived from the molecular graphs that capture structural features and connectivity, playing a crucial role in QSPR analysis by quantitatively relating chemical structures to their physicochemical properties and biological activities. Lung cancer is characterized by its aggressive nature and late-stage diagnosis, often limiting treatment options and significantly impacting patient survival rates.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Continuous and consistent access to quality medical imaging data stimulates innovations in artificial intelligence (AI) technologies for patient care. Breakthrough innovations in data-driven AI technologies are founded on seamless communication between data providers, data managers, data users and regulators or other evaluators to determine the standards for quality data. However, the complexity in imaging data quality and heterogeneous nature of AI-enabled medical devices and their intended uses presents several challenges limiting the clinical translation of novel AI technologies.
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