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Unveiling the ghost: machine learning's impact on the landscape of virology.

J Gen Virol

January 2025

Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.

The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for addressing this challenge, as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded, providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction, phylogenetic analysis, drug discovery and more.

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Aims: The gastrointestinal (GI) tract is composed of distinct sub-regions, which exhibit segment-specific differences in microbial colonization and (patho)physiological characteristics. Gut microbes can be collectively considered as an active endocrine organ. Microbes produce metabolites, which can be taken up by the host and can actively communicate with the immune cells in the gut lamina propria with consequences for cardiovascular health.

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The application of nanotechnology in medical biology has seen a significant rise in recent years because of the introduction of novel tools that include supramolecular systems, complexes, and composites. Dendrimers are one of the remarkable examples of such tools. These spherical, regularly branching structures with enhanced cell compatibility and bioavailability have shown to be an excellent option for gene or drug administration.

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Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems.

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This study aims to explore if the dimensions of anger can be risk factors for the development of social media addiction and an internet gaming disorder, considering that the correlation between anger and these specific subcategories of internet addiction can represent a core intervention in their prevention and treatment. 477 subjects, recruited among the general population, were assessed on-line by the following tools: STAXI-2; BSMAS, and IGDS9-SF. A correlation analyses showed a significantly positive relationship between the total score of the BSMAS and the STAXI-2 scales SANG ( < 0.

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