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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].

Scientists just mapped the brain architecture that underlies human intelligence

For decades, researchers have attempted to pinpoint the specific areas of the brain responsible for human intelligence. A new analysis suggests that general intelligence involves the coordination of the entire brain rather than the superior function of any single region. By mapping the connections within the human brain, or connectome, scientists found that distinct patterns of global communication predict cognitive ability.

The research indicates that intelligent thought relies on a system-wide architecture optimized for efficiency and flexibility. These findings were published in the journal Nature Communications.

General intelligence represents the capacity to reason, learn, and solve problems across a variety of different contexts. In the past, theories often attributed this capacity to specific networks, such as the areas in the frontal and parietal lobes involved in attention and working memory. While these regions are involved in cognitive tasks, newer perspectives suggest they are part of a larger story.

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.”

A Hidden Cellular Defense May Protect the Brain From Alzheimer’s

Scientists discovered why some neurons resist tau toxicity, identifying CRL5SOCS4 as a crucial defense and linking mitochondrial stress to harmful tau fragments. New research by UCLA Health and UC San Francisco has uncovered why certain brain cells are more resilient than others to the buildup of

This ultra-thin surface controls light in two completely different ways

The team then fine-tuned the resonant strength of the meta-atoms to independently adjust the group delay for each spin. At the same time, frequency tuning and local structural rotation were used to set the phase while keeping unwanted crosstalk low. The PB phase, added through global rotation, extends the available phase range toward a full 2π without significantly altering the group delay design. Together, these elements create a practical single-layer design strategy for dual-spin achromatic control.

Experimental Proof Across Multiple Frequency Bands

The researchers demonstrated their approach experimentally using two types of devices operating in the 8–12 GHz range. One class consisted of spin-unlocked achromatic beam deflectors that maintained stable, spin-dependent steering across the band. The other involved achromatic metalenses that assigned different focusing functions to RCP and LCP light while preserving strong performance over a broad frequency range.

Quantum computers will finally be useful: what’s behind the revolution

Mikhail Lukin’s team at Harvard presented a “universal” design for neutral-atom processors with robust error-correction capabilities using just 448 qubits, alongside a 3,000-qubit processor that can run for hours.

As Lukin notes: “These are really new kinds of instruments—by some measures, they’re not even computers… What’s really exciting is that these systems are now working already at a reasonable scale and we can start experimenting with them to figure out what we can do with them.”

A string of surprising advances suggests usable quantum computers could be here in a decade.

Decoding the shadows: Vehicle recognition software uncovers unusual traffic behavior

Researchers at the Department of Energy’s Oak Ridge National Laboratory have developed a deep learning algorithm that analyzes drone, camera, and sensor data to reveal unusual vehicle patterns that may indicate illicit activity, including the movement of nuclear materials. The work is published in the journal Future Transportation.

The software monitors routine traffic over time to establish a baseline for “patterns of life,” enabling detection of deviations that could signal something out of place. For example, a surge in overnight truck traffic at a facility which is normally only visited during the day could reveal illegal shipments.

The research builds on a previous ORNL-developed technology for recognizing specific vehicles from side views. Researchers improved the structure of this software’s deep learning network to provide much broader capabilities than any existing recognition systems, said ORNL’s Sally Ghanem, lead researcher.

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