Researchers used Google DeepMind’s AlphaFold2 and ProteinMPNN to speed development of antibody-based probes that can be used to see key functions and chemical changes inside living cells as they happen. This AI-driven method is significantly faster than previous manual testing and development approaches, allowing the CSU team to rapidly create and test 19 new potential probes. The work enables continuous imaging of living cells, which may help researchers better understand errors in genetic expression that can lead to cancer and other disorders.  

Scientists at Pacific Northwest Research Institute (PNRI) report that pairs of genetic variants previously classified as damaging can frequently restore normal protein function when present together, overturning a core assumption in human genetics. In a large experimental study of a rare-disease gene, more than 60% of damaging variant pairs rescued enzyme activity. The findings could improve genetic diagnosis and risk prediction for rare disorders by revealing when standard interpretations overestimate disease severity.

Key Study Highlights: 

Demonstrates how dual-purpose therapeutic targets may address both hepatocellular carcinoma progression and cellular senescence, supporting emerging strategies that link disease treatment with aging biology

Identifies PRPF19 and MAPK9 as targets that suppress tumor cell proliferation while reducing senescence-associated signaling in relevant cellular models

Provides evidence of senomorphic activity, reducing harmful senescence-associated secretory phenotype (SASP) signaling without marked cytotoxicity

Illustrates the effectiveness of integrating AI-driven target discovery, multi-omic human datasets, and experimental validation to prioritize biologically relevant and translationally promising targets

Reinforces Insilico’s broader AI-guided discovery approach for uncovering shared mechanisms across disease and aging

A UC Davis study shows a deadly cat coronavirus infects multiple immune cells, helping explain persistent inflammation and long COVID–like illness.

Pennington Biomedical Research Center’s Dr. Gang Hu of the Center’s Chronic Disease Epidemiology Lab was recently appointed to serve as a member of the Population Sciences and Epidemiology Integrated Review Group (IRG) for the NIH Reproductive, Perinatal and Pediatric Health (RPPH) study section. The IRG for the RPPH previews applications submitted to the NIH, makes recommendations on the applications to the appropriate national advisory council, and surveys the status of research in the field of population science and epidemiology. 

Scientists built a robot to help explain how a tropical bat spots insects perched on leaves using echolocation, a highly sophisticated behavior that requires precise, split-second decision-making on the part of the hunting bat. In a study published today in the Journal of Experimental Biology, a bat researcher at the Smithsonian Tropical Research Institute teamed up with two robotics engineers from the University of Cincinnati and the University of Antwerp, respectively, to provide the first plausible explanation for how the common big-eared bat (Micronycteris microtis) can efficiently determine whether a leaf is occupied by a silent insect amidst the dense, cluttered understory of the tropical rainforest using only sound.