In science, the suffix “phage” means to devour. Thus, the name bacteriophage might conjure images of tiny creatures that “eat” bacteria. While that conception isn’t 100 percent scientifically accurate, bacteriophages are lethal bacteria killers nonetheless, and scientists are excited about engineering phage DNA as a path to new antibiotics.

Mayo Clinic researchers have developed an experimental drug with the help of artificial intelligence (AI) to target the "undruggable" PDZ-domain of GIPC1 protein that helps different types of cancer, including pancreatic cancer, to grow and resist treatment. In laboratory studies, the drug slowed tumor growth, improved survival and enhanced the effects of the chemotherapy drug gemcitabine.

AI companions are becoming increasingly popular, with millions of users forming deep connections with persona-based chatbots and simulated AI partners. But if you’re considering leaning on an AI companion for emotional support, a study by researchers in the lab of Diyi Yang, an assistant professor in the Computer Science Department at Stanford, suggests you might want to think twice: As reported in Nature Human Behavior, the researchers found that opening up to chatbots about personal issues made some people feel not better but worse.

Artificial intelligence (AI) is becoming increasingly powerful at reading pathology slides. But a new Perspective in Science Bulletin argues that accuracy alone is not enough. For AI to truly help pathologists, it must work safely and transparently in real clinical settings.

The research article discusses how computational pathology is moving from technical innovation toward clinical integration.

A new artificial intelligence (AI) tool developed by La Trobe University researchers could help predict which stage-two bowel cancer patients are at risk of relapse, in an advance that could ultimately lead to thousands getting life-saving treatment earlier.

Published in the journal Gastroenterology, the research documents the creation of SÉMIL (Semantically-Enhanced Multiple Instance Learning),

Biomedical engineers at Duke University have demonstrated a method for systematically developing novel, complex combinations of probiotics and prebiotics to more effectively maintain gut health and treat various gastrointestinal diseases.

By tactically designing experiments and robotically automating thousands of parallel experiments to fill knowledge gaps that could make the model more accurate,

Penn Engineers have developed PeptiVerse, an AI-powered platform that predicts key chemical and biological properties of peptides, the strings of amino acids whose medical potential has been demonstrated by the success of GLP-1 drugs, the widely used weight-loss treatments.

In Nature Communications, the researchers describe how they trained PeptiVerse using a wide range of data sets, allowing it to predict properties that can help determine whether a peptide is worth pursuing as a potential drug,