A deep learning model using longitudinal mammograms and clinical data predicts breast cancer recurrence, enabling imaging-based risk stratification beyond single-timepoint assessment.

Longitudinal GPS data can enhance the precision of spatial exposure assessment, yet few spatial epidemiologic studies incorporate such data and even fewer do so in the context of health equity. We ...

Advancements in in vivo stable isotope tracing have revolutionized our understanding of cancer metabolism, transitioning the field from static metabolomic snapshots to dynamic metabolic pathway activities. However, interpreting raw fractional enrichment data presents significant biological and analytical challenges. This review systematically examines the methodological landscape of in vivo tracing, addressing the interpretive gap created by clinical constraints, such as single-timepoint surgical sampling. We dissect how secondary circulating tracers and the inherent heterogeneity of the tumor microenvironment can confound metabolic signals. We highlight the critical transition to metabolic flux analysis as the requisite computational step to overcome these challenges and to identify targetable metabolic vulnerabilities in precision oncology.

Researchers combined Structure-from-Motion photogrammetry with 3D Gaussian Splatting to build a 12-timepoint 4D fern growth sequence and an interactive 14-section digital twin of a tropical greenhouse

Ketamine’s robust antidepressant effects often appear twenty-four hours after a single dose. A new study now provides evidence suggesting that this timepoint coincides with a quieting of habenula activation and reductions in aversive learning in healthy subjects.

Ketamine’s robust antidepressant effects often appear twenty-four hours after a single dose. A new study now provides evidence suggesting that this timepoint coincides with a quieting of habenula activation and reductions in aversive learning in healthy subjects.