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Finding has implications for treating common neuropsychiatric disorders

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Mount Sinai-led research can transform how hospitals triage, risk-stratify, and counsel patients to save lives

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Cardiology faculty practice is the only heart center in New York to receive the Pinnacle of Excellence Award

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Mount Sinai-led research can transform how hospitals triage, risk-stratify, and counsel patients to save lives

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A new multicenter study by researchers at the Icahn School of Medicine at Mount Sinai, in collaboration with the National Cancer Institute-funded Clinical Proteomic Tumor Analysis Consortium (CPTAC) and colleagues around the world, has discovered that the genes we are born with鈥攌nown as germline genetic variants鈥攑lay a powerful, underappreciated role in how cancer develops and behaves.

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A seminal study from researchers at the Icahn School of Medicine at Mount Sinai and their collaborators in the United Kingdom, Belgium, Spain, the Netherlands, and Iceland has uncovered a new genetic cause of neurodevelopmental disorders (NDDs). The discovery offers both closure and hope to potentially thousands of families worldwide who have long been searching for answers. The study, published in the April 10 online issue of Nature Genetics [DOI: 10.1038/s41588-025-02159-5], reveals that mutations in a small, previously overlooked non-coding gene called RNU2-2 are responsible for relatively common NDD. Non-coding genes are genes that don鈥檛 produce proteins but may still play critical roles in regulating cell functions.

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As artificial intelligence (AI) rapidly integrates into health care, a new study by researchers at the Icahn School of Medicine at Mount Sinai reveals that all generative AI models may recommend different treatments for the same medical condition based solely on a patient鈥檚 socioeconomic and demographic background.

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A powerful new software platform called the Playbook Workflow Builder is set to transform biomedical research by allowing scientists to conduct complex and customized data analyses without advanced programming skills. An article that describes the new platform was published in the April 3 online issue of the journal PLOS Computational Biology. Developed by a multi-institutional team that was led by Icahn School of Medicine at Mount Sinai investigators as part of the National Institutes of Health Common Fund Data Ecosystem (CFDE) program, researchers from across the United States developed the web-based platform that enables scientists to analyze and visualize their own data independently through an intuitive, interactive interface.

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The Icahn School of Medicine at Mount Sinai has launched the AI Small Molecule Drug Discovery Center, a bold endeavor that harnesses artificial intelligence (AI) to revolutionize drug development. The new Center will integrate AI with traditional drug discovery methods to identify and design new small-molecule therapeutics with unprecedented speed and precision. Unlike conventional drug discovery, which can take years and cost billions, AI-driven approaches enable researchers to rapidly navigate a vast chemical landscape, including natural products, to pinpoint promising drug candidates. By leveraging Mount Sinai鈥檚 world-leading expertise in machine learning, chemical biology, and biomedical data science, the Center aims to bring innovative treatments to patients faster鈥攑articularly for diseases with urgent unmet needs, including cancer, metabolic disorders, and neurodegenerative diseases.

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Brian Brown, PhD, Director of the Icahn Genomics Institute at the Icahn School of Medicine at Mount Sinai, has been elected to the College of Fellows of the American Institute for Medical and Biological Engineering (AIMBE).

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Study led by Mount Sinai researcher could lead to more widespread use of imaging technique to improve survival and prevent complications

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Trial led by Mount Sinai researcher could save lives for patients in need of urgent surgery, invasive procedures, or experiencing major bleeding

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New Center Aims to Accelerate AI Research and Personalized Treatment in Children鈥檚 Health

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Researchers at the Icahn School of Medicine have developed a powerful AI tool, built on the same transformer architecture used by large language models like ChatGPT, to process an entire night鈥檚 sleep. To date, it is one of the largest studies, analyzing 1,011,192 hours of sleep. Details on their findings were reported in the March 13 online issue of the journal Sleep [https://doi.org/10.1093/sleep/zsaf061]. The model, called patch foundational transformer for sleep (PFTSleep), analyzes brain waves, muscle activity, heart rate, and breathing patterns to classify sleep stages more effectively than traditional methods, streamlining sleep analysis, reducing variability, and supporting future clinical tools to detect sleep disorders and other health risks.