New study reveals ‘droplet’ mechanism behind key drug targets
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Updates every hour. Last Updated: 29-May-2026 13:15 ET (29-May-2026 17:15 GMT/UTC)
Researchers at the Icahn School of Medicine at Mount Sinai have found evidence that people who fall at the extreme high or low ends of certain traits, such as cholesterol, blood glucose, height, and age at menopause, are more likely to have a simple genetic explanation than previously thought. Their findings, reported in the May 27 issue of Nature [https://doi.org/10.1038/s41586-026-10516-5], may lead to new insights into the causes of common diseases. Many traits linked to human health are considered “polygenic,” meaning they are shaped by the combined influence of many common genetic variants, each contributing only a small effect. But the new study explored whether individuals with extreme trait values may instead be influenced by rarer genetic variants that have a much larger impact. The researchers say this possibility could help explain why some individuals develop unusually high or low levels of traits associated with conditions such as diabetes, heart disease, and stroke.
This Cystic Fibrosis Awareness Month, Children’s Hospital Colorado (Children’s Colorado) is highlighting a growing shift in care: starting cystic fibrosis treatment before birth to improve lifelong health outcomes and reduce hospital stays. Through a highly coordinated, multidisciplinary approach, Children’s Colorado’s Breathing Institute and Colorado Fetal Care Center teams have cared for 10 families through prenatal therapy for patients showing signs of cystic fibrosis in utero. To date, no other healthcare system in the United States has cared for this number of patients prenatally, positioning Children’s Colorado among the most experienced of its kind in the world.
The study, published in Communications Medicine, combines magnetic resonance imaging and simulation-based artificial intelligence to speed up brain analysis while maintaining a high level of accuracy.
The method avoids the use of real patient data for training, reduces bias, and could facilitate the implementation of these advanced techniques in hospitals, helping to shorten waiting lists and improve diagnosis.