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Predicting phage-host interaction via hyperbolic Poincaré graph embedding and large-scale protein language technique.

iScience

January 2025

Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China.

Bacteriophages (phages) are increasingly viewed as a promising alternative for the treatment of antibiotic-resistant bacterial infections. However, the diversity of host ranges complicates the identification of target phages. Existing computational tools often fail to accurately identify phages across different bacterial species.

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In recent years, global trends indicate consumer interest in functional foods. Thus, there is a trend to replace the use of artificial colors with natural colors that, in addition to being attractive to consumers, provide benefits to the biological functions of the human organism. The objective of this research was the solvent extraction of a natural dye from the roselle flower, its identification and evaluation of its behavior at different pH and temperatures.

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Developing disease models and screening for effective drugs are key areas of modern medical research. Traditional methodologies frequently fall short in precisely replicating the intricate architecture of bodily tissues and organs. Nevertheless, recent advancements in biomaterial-assisted organoid technology have ushered in a paradigm shift in biomedical research.

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Background: The aging population is increasingly affected by periodontal disease, a condition often overlooked due to its asymptomatic nature. Despite its silent onset, periodontitis is linked to various systemic conditions, contributing to severe complications and a reduced quality of life. With over a billion people globally affected, periodontal diseases present a significant public health challenge.

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In the rapidly evolving field of healthcare research, Artificial Intelligence (AI) and conversational models like ChatGPT (Conversational Generative Pre-trained Transformer) offer promising tools for data analysis. The aim of this study was to: 1) apply ChatGPT methodology alongside human coding to analyze qualitative health services feedback, and 2) examine healthcare experiences among lesbian, gay, bisexual, transgender, and queer (LGBTQ+) patients ( = 41) to inform future intervention. The hybrid approach facilitated the identification of themes related to affirming care practices, provider education, communicative challenges and successes, and environmental cues.

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