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Science News for December 24, 2025


Researchers identify Rb1 as a predictive biomarker for a new therapeutic strategy in some breast cancers

Researchers have identified Rb1 as a predictive biomarker for a new therapeutic strategy in some breast cancers. The study found that simultaneous inhibition of ATR and PKMYT1 triggers cell death in Rb1-deficient breast cancer models, leading to tumor shrinkage and improved survival.

SourceUniversity of Texas M. D. Anderson Cancer Center·JournalScience Translational Medicine·TypeRandomized controlled/clinical trial·DateDec 24, 2025

Heart-brain connection: international study reveals the role of the vagus nerve in keeping the heart young

A recent international study coordinated by the Sant'Anna School of Advanced Studies found that preserving bilateral cardiac vagal innervation is an anti-aging factor. The right cardiac vagus nerve helps preserve cardiomyocyte health independently of heart rate, suggesting a new strategy for long-term heart protection.

SourceSant’Anna School of Advanced Studies, Pisa·JournalScience Translational Medicine·TypeExperimental study·DateDec 24, 2025

An AI-based blueprint for designing catalysts across materials

Researchers developed a computational framework to identify effective catalysts for producing hydrogen peroxide from water and electricity. The approach successfully predicted key reaction properties across diverse materials, leading to the discovery of promising candidate lithium scandium oxide.

SourceAdvanced Institute for Materials Research (AIMR), Tohoku University·JournalAngewandte Chemie International Edition·DateDec 24, 2025

AI overestimates how smart people are, according to HSE economists

HSE economists found that AI models like ChatGPT and Claude tend to play 'too smart' and lose in strategic thinking games by assuming a higher level of logic in people than is actually present. The study replicated results from previous human participant experiments, showing LLMs adapt to opponents with varying levels of sophistication.

SourceNational Research University Higher School of Economics·JournalJournal of Economic Behavior & Organization·DateDec 24, 2025

Magnetic robotization in clinical medicine: A review

Researchers reviewed magnetic materials preparation, structural design and actuation systems for medical applications, highlighting potential in targeted drug delivery, minimally invasive surgery and disease diagnosis. Despite challenges, the authors emphasize magnetic soft robots' potential to reshape future healthcare practices.

SourceKeAi Communications Co., Ltd.·JournalMagnetic Medicine·TypeLiterature review·DateDec 24, 2025

Neuromorphic Spike-Based Large Language Model (NSLLM): The next-generation AI inference architecture for enhanced efficiency and interpretability

The NSLLM framework transforms conventional LLMs into neuromorphic models by performing integer spike counting and binary spike conversion, enabling analysis of information processing. This approach achieves significant energy efficiency and improved interpretability through the use of neural dynamical representations and biologically-...

SourceScience China Press·JournalNational Science Review·TypeComputational simulation/modeling·DateDec 24, 2025

Analyzing corporate ESG reporting through data mining: Evolutionary trends and strategic model

A study analyzing corporate ESG reporting through data mining reveals a significant trend toward the homogenization of reports, with greater emphasis on social and governance issues. The findings suggest that companies are adopting similar ESG frameworks, which can enhance legitimacy but limit innovation.

SourceShanghai Jiao Tong University Journal Center·JournalJournal of Management Analytics·TypeNews article·DateDec 24, 2025

Triboluminescence of metal halide perovskite films

Researchers discovered that MHP films exhibit triboluminescence when scraped with metals like copper, gold, or platinum, due to friction-induced charge transfer. This phenomenon is universally observed across commonly studied MHP films. The enhancement of PL is attributed to the accumulation of positive charges on the perovskite surfac...

Automatic label checking: The missing step in making reliable medical AI

A team of researchers at Osaka Metropolitan University has created two models to detect and fix common labeling errors in large radiographic collections. The models achieved high accuracy rates for body-part classification and projection/orientation detection, showing promising results for improving deep-learning models in medical AI.

SourceOsaka Metropolitan University·JournalEuropean Radiology·TypeObservational study·DateDec 24, 2025