UT System investments of over $470 million accelerate UT San Antonio’s rise as a world-class research university
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Updates every hour. Last Updated: 12-Jun-2026 20:16 ET (13-Jun-2026 00:16 GMT/UTC)
The University of Texas System Board of Regents has authorized approximately $470 million in capital investments supporting UT San Antonio’s continued growth as one of the nation’s fastest-rising public research universities and academic health institutions.
A new skin-like computing patch developed at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) can analyze health data using artificial intelligence in an unprecedented way. Unlike today’s wearable devices, it carries out its AI computations directly on the body, in mere milliseconds, without relying on a wireless connection. Their findings were published in Nature Communications.
Physicians at the Icahn School of Medicine at Mount Sinai are calling for updates to a widely used system that grades side effects from cancer treatments, warning that current criteria may misclassify the severity of skin-related toxicities and limit consistency across clinical trials.
In exploring how serotonin affects how people learn and adapt to changes, researchers found that the neurotransmitter helps reduce "belief stickiness" — the tendency to get stuck on an old idea despite new contradicting evidence. According to the researchers, the discovery, detailed in Nature Mental Health, holds important implications for the understanding and treatment of obsessive-compulsive disorder (OCD).
Scientists at the Icahn School of Medicine at Mount Sinai have created a new artificial intelligence (AI) model that helps reveal how genes function together inside human cells, offering a powerful new way to understand biology and disease. The study, published in the May 21 online issue of Patterns, a Cell Press Journal [DOI: https://doi.org/10.1016/j.patter.2026.101565], introduces a gene set foundation model (GSFM) designed to learn patterns in how genes are grouped and function across thousands of biological contexts. The work draws inspiration from advances in large language models (LLMs) such as ChatGPT, which learn how words gain meaning depending on their context. In a similar way, a GSFM learns how genes behave differently depending on their cellular “context.”