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Category: biotech/medical – Page 393

AI model reads brain MRIs in seconds, hitting up to 97.5% accuracy

An AI-powered model developed at the University of Michigan can read a brain MRI and diagnose a person in seconds, a study suggests. The model detected neurological conditions with up to 97.5% accuracy and predicted how urgently a patient required treatment.

Researchers say the first-of-its-kind technology could transform neuroimaging at health systems across the United States. The results are published in Nature Biomedical Engineering.

“As the global demand for MRI rises and places significant strain on our physicians and health systems, our AI model has the potential to reduce burden by improving diagnosis and treatment with fast, accurate information,” said senior author Todd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.

A new robotic system could perform delicate eye surgery

Retinal vein occlusion (RVO) is a severe disease that occurs when a vein in the light-sensitive layer at the back of the eye (i.e., the retina) becomes blocked, which results in a loss of vision. There are currently a few medical interventions that address RVO, including the periodic injection of medications that block the abnormal growth of blood vessels or of steroids, which reduce swelling and inflammation.

A promising procedure for the treatment of RVO is retinal vein cannulation (RVC). This is a very delicate surgical intervention that requires surgeons to insert a tiny needle into the blocked vein with high precision, delivering clot-dissolving drugs or medications that control the abnormal growth of blood vessels.

Given that retinal veins targeted for cannulation are similar in thickness to a human hair, manually inserting a needle inside them with high precision is very challenging. Robots could potentially assist surgeons in performing RVO procedures, ensuring that needles are inserted correctly and without damaging the patients’ retina.

AI tool predicts brain age, cancer survival and other disease signals from unlabeled brain MRIs

Mass General Brigham investigators have developed a robust new artificial intelligence (AI) foundation model that is capable of analyzing brain MRI datasets to perform numerous medical tasks, including identifying brain age, predicting dementia risk, detecting brain tumor mutations and predicting brain cancer survival. The tool, known as BrainIAC, outperformed other, more task-specific AI models and was especially efficient when limited training data were available.

Results are published in Nature Neuroscience.

“BrainIAC has the potential to accelerate biomarker discovery, enhance diagnostic tools and speed the adoption of AI in clinical practice,” said corresponding author Benjamin Kann, MD, of the Artificial Intelligence in Medicine (AIM) Program at Mass General Brigham. “Integrating BrainIAC into imaging protocols could help clinicians better personalize and improve patient care.”

Prevalence of Illicit Drug Detection in Out-of-Treatment People Who Inject Drugs

Among people injecting drugs and not engaged in medical care, nearly all tested positive for fentanyl and multiple other substances, with polysubstance and xylazine detection rates highest among unhoused and recently incarcerated participants.

This cross-sectional study used data from HPTN 094. Participants who met eligibility criteria were invited to participate in a baseline interview and were enrolled between June 2021 and September 2023. All participants completed written informed consent prior to participating in study procedures, and a single institutional review board (Advarra) provided ethical approval for HPTN 094; this cross-sectional analysis was exempt from additional IRB approval. The current study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.23

Participants were required to meet the following criteria: be at least 18 years of age, have a urine test positive for recent opioid use and evidence of recent injection drug use (visible venipuncture marks), meet diagnostic criteria for opioid use disorder, be able to give informed consent, be willing to start MOUD treatment, complete an assessment of understanding, have confirmed HIV seropositivity or self-reported sharing of injection equipment and/or condomless sex in the past 3 months with partners living with HIV or with unknown HIV status, and provide locator information. Participants were excluded if they self-reported being prescribed MOUD in the 30 days prior to screening, had a urine test positive for methadone (with the exception of verified hospitalization), or were enrolled in another study.

Abstract: Unraveling TIME: CD8+ T cell-and CXCL11-driven endocrine resistance in BreastCancer:

Unraveling TIME: CD8+ T cell-and CXCL11-driven endocrine resistance in BreastCancer:

Tim Kong & Cynthia X. Ma provide a Commentary on Fabiana Napolitano: https://doi.org/10.1172/JCI188458

1Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.

2Department of Medicine, Weill Cornell Medicine, New York, New York, USA.

Address correspondence to: Cynthia X. Ma, Division of Oncology, Department of Medicine, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, Missouri, 63,110, USA. Email: [email protected].

AI tool can predict which trauma patients need blood transfusions before they reach the hospital

Severe bleeding is one of the most common and preventable causes of death after traumatic injury, yet currently available tools have poor ability to determine which patients urgently need blood transfusions. A new multinational study, just published in Lancet Digital Health, suggests artificial intelligence (AI) may help close that gap.

Researchers have developed and validated machine-learning models that can accurately predict whether trauma patients will require blood transfusions, using only information available before they reach the hospital such as vital signs, injury patterns, and medication history.

Co-author Prof Patricia Maguire from University College Dublin (UCD), Director of UCD AI Healthcare Hub and UCD Institute for Discovery, said, “These findings show that AI-driven decision support could enable earlier and more precise identification of patients at highest risk of hemorrhagic shock, using data already available to emergency services. This has clear potential to support more timely transfusion decisions, although prospective evaluation will be needed before clinical implementation.”

Live-cell tracking reveals dynamic interaction between protein folding helpers and newly produced proteins

Proteins are the molecular machines of cells. They are produced in protein factories called ribosomes based on their blueprint—the genetic information. Here, the basic building blocks of proteins, amino acids, are assembled into long protein chains. Like the building blocks of a machine, individual proteins must have a specific three-dimensional structure to properly fulfill their functions.

To achieve this, the newly produced protein chains in human cells are folded into their stable and functional form with the help of various protein folding helper proteins, known as chaperones, such as TRiC/PFD, or HSP70/40. The protein folding helpers isolate the amino acid chains, which have different chemical properties depending on the amino acid, from the cellular environment. This prevents the newly produced protein chains from clumping together and causing disease.

F.-Ulrich Hartl, a director at the Max Planck Institute of Biochemistry, has spent decades studying the mechanisms of protein folding. Niko Dalheimer, a scientist in Hartl’s department and one of the two lead authors of a new study published in Nature, explains: Much of what we know about protein folding has been learned from studies conducted in test tubes. However, it is virtually impossible to faithfully replicate the cellular environment in vitro.

Faster enzyme screening could cut biocatalysis bottlenecks in drug development

A team of biochemists at the University of California, Santa Cruz, has developed a faster way to identify molecules in the lab that could lead to more effective pharmaceuticals. The discovery advances the rapidly growing field of biocatalysis, which depends on generating large, genetically diverse libraries of enzymes, and then screening those variants to find ones that perform a desired chemical task best.

This strategy has attracted major investment, particularly from drugmakers, because it promises quicker routes to complex, high-value molecules. However, traditional approaches to finding new biologically beneficial molecules often require “lots of shots on goal,” where researchers test enormous numbers of candidates through slow and inefficient workflows.

The method developed by the UC Santa Cruz team aims to significantly shorten that process by introducing smarter and faster decision-making tools that help researchers identify promising enzyme variants much earlier. The researchers detail their new approach in the journal Cell Reports Physical Science.

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