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The secret to psychedelic drugs’ links to greater empathy and insight may lie in their ability to coax the right hemisphere of the brain into a position of dominance over the left, according to a proposed new theory.
Scientists are developing artificial intelligence and machine learning tools for improving the operations of particle accelerators. Here, the results of three related research studies with recent peer-reviewed journal articles are discussed.
UC Davis Health is now offering an FDA-approved test that can predict Alzheimer's disease before the onset of major symptoms. A negative test can rule out Alzheimer's disease with 96.2% accuracy. A positive test can allow patients to receive a diagnosis and potentially access new drugs to slow the disease's progression while in a very early stage.
A study in The FASEB Journal reveals PME-1 protein regulates tumor suppressor PP2A through two distinct mechanisms: methyl group removal and direct binding. Using genetically modified mice, researchers found each function affects different developmental aspects—demethylation impacts brain development while binding affects olfaction.
When it comes to Meta, particularly its platform Facebook, news matters—not the fake stuff but the real kind generated by working journalists. That’s the takeaway of new research by ...
This study introduces a deep-learning system for rapid, automated detection and classification of tiny calcium deposits (microcalcifications) in mammograms to aid early breast cancer diagnosis. Leveraging a multi-center dataset of 4,810 biopsy-confirmed mammograms, our pipeline uses a Faster RCNN model with a feature-pyramid backbone to detect and classify microcalcifications—the pipeline requires no hand-tuned rules and provides both the overall cancer risk and highlighted lesion regions in seconds per image. On unseen test data, it achieved overall classification accuracy of 72% for discriminating between benign and malignant breasts and 78% sensitivity of malignant breast cancer prediction, marking a significant step toward AI-assisted, cost-effective breast-cancer screening that can run on standard radiology workstations.
Under a $50,000 1-year grant from the Additional Ventures Foundation and a Cardiac Imaging Suite Pilot Award, Eamon Doyle, PhD, Data Engineer, Data Science and AI at Children’s Hospital Los Angeles, will use novel magnetic resonance imaging (MRI) technologies to scan the hearts and lungs of children between 12-18 years old born with single ventricle hearts, as well as those of healthy control patients. His goal is to assess methods of determining blood flow, ventilation patterns, and oxygen perfusion in the lungs.
This review summarizes the research progress on flexible wearable sensors based on different plant signals and classifies them according to their functions: physical sensors, chemical sensors and electrophysiological sensors. Furthermore, the challenges currently faced by wearable plant sensors are presented and we propose a design framework for next-generation plant wearable sensors enabling continuous real-time plant health monitoring under field conditions.
A Rutgers Health review of 151 studies finds that minimizing unexplained symptoms sparks shame, trauma and avoidance of care.
A landmark review, now published in the Journal of Experimental & Clinical Cancer Research, offers a sweeping and authoritative synthesis on the use of liquid biopsy in gynecological oncology placing this emerging tool at the forefront of precision medicine for women.
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