The Alliance for Clinical Trials in Oncology has launched a randomized phase III clinical trial called RECIPROCAL (Alliance A032304) to explore whether doctors can optimize the timing of targeted radiation therapy to minimize side effects while preserving efficacy in men with advanced prostate cancer. The Alliance for Clinical Trials in Oncology has launched a randomized phase III clinical trial called RECIPROCAL (Alliance A032304) to explore whether doctors can optimize the timing of targeted radiation therapy to minimize side effects while preserving efficacy in men with advanced prostate cancer.

This study in neonatal rats revealed that the common inhaled pediatric anesthetic, sevoflurane, impairs neurogenesis and causes cognitive deficits by suppressing fatty acid β-oxidation (FAO) within neural stem/progenitor cells. The research pinpointed that enhancing FAO activity can significantly mitigate the neuronal damage induced by sevoflurane. This discovery provides a novel perspective on anesthetic-related neurodevelopmental toxicity and establishes a foundational theory for future strategies aimed at protecting brain function in infants and children undergoing anesthesia.

A large-scale international study, led by researchers from the Gray Faculty of Medical and Health Sciences at Tel Aviv University, has uncovered a mechanism that allows breast cancer to send metastases to the brain — a highly lethal occurrence for which there is currently no effective treatment. The findings could enable the development of new drugs and personalized monitoring for early detection and treatment of brain metastases.

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, and if it is accurately predicted, severe fibrosis and cirrhosis can be prevented. While liver biopsies, the gold standard for NAFLD diagnosis, is intrusive, expensive, and prone to sample errors, noninvasive studies are extremely promising but are still in their infancy due to a dearth of comprehensive study data and sophisticated multimodal data methodologies. This paper proposes a novel approach for diagnosing NAFLD by integrating a comprehensive clinical dataset with a multimodal learning-based prediction method. The dataset comprises physical examinations, laboratory and imaging studies, detailed questionnaires, and facial photographs of a substantial number of participants, totaling more than 6000. This comprehensive collection of data holds significant value for clinical studies. The dataset is subjected to quantitative analysis to identify which clinical metadata, such as metadata and facial images, has the greatest impact on the prediction of NAFLD. Furthermore, a multimodal learning-based prediction method (DeepFLD) is proposed that incorporates several modalities and demonstrates superior performance compared to the methodology that relies only on metadata. Additionally, satisfactory performance is assessed through verification of the results using other unseen data. Inspiringly, the proposed DeepFLD prediction method can achieve competitive results by solely utilizing facial images as input rather than relying on metadata, paving the way for a more robust and simpler noninvasive NAFLD diagnosis.