Ribosomes—the tiny factories that build proteins in our cells—don’t all work with the same efficiency. Researchers from Japan have discovered that ribosomes actually compete with one another, and those that perform poorly are selectively broken down when more efficient ones are present. This built-in “survival of the fittest” mechanism keeps protein synthesis accurate and efficient, shedding new light on how cells maintain quality control and prevent ribosome-related diseases.

The Hong Kong University of Science and Technology (HKUST), in collaboration with the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), has launched the world's first Deep Ocean Omics (DOO) database (https://DeepOceanOmics.org/). As the largest platform of its kind, DOO integrates and analyzes multi-omics data from organisms thriving in the ocean's most extreme environments, alongside customized analytical tools to support cross-species comparative and evolutionary studies. By facilitating the utilization of deep-sea biological resources, the platform aims to advance scientific understanding of deep-sea biodiversity and ecosystems, and to foster global research and applications related to biological adaptation in extreme environments.

In a study published in Nature Communications, researchers from the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine), together with international collaborators, examined whether methylene blue could mitigate brain injury during severe malaria, and whether a practical set of blood biomarkers could help clinicians identify cerebral malaria early and track how patients respond to treatment.

Researchers at the University of Navarra in Spain, have developed RNACOREX, a new open-source software tool that reveals hidden genetic regulatory networks involved in cancer and helps predict patient survival. Tested across 13 different tumor types using data from The Cancer Genome Atlas (TCGA), RNACOREX identifies key interactions between microRNAs and messenger RNAs—molecular relationships that are often missed by conventional analyses.

Unlike many artificial intelligence models that function as “black boxes,” RNACOREX produces interpretable molecular maps that show how genes interact within tumors. These networks can stratify patients according to survival probability with accuracy comparable to advanced AI approaches, while clearly explaining the biological mechanisms behind the predictions.

Published in PLOS Computational Biology, the study demonstrates how RNACOREX can uncover shared molecular patterns across cancers, highlight individual molecules of biomedical interest, and generate new hypotheses about tumor progression. Freely available via GitHub and PyPI, the tool is designed to be accessible for research laboratories worldwide and represents a step forward in explainable AI for precision oncology.