A lab-designed molecule developed and extensively studied by scientists with Virginia Tech’s Fralin Biomedical Research Institute at VTC could represent a breakthrough in slowing tumor recurrence in glioblastoma, an aggressive and deadly form of brain cancer.

Insilico Medicine recently unveiled a groundbreaking study developing small molecule inhibitors targeting ENPP1 to effectively modulate the STING pathway and enhance tumor immunity. Published in Nature Communications, the study showcases Insilico's advanced generative AI platform and integrated workflow which identified and validated ENPP1 as a critical immune checkpoint among multiple solid tumors and assisted in developing a highly specific oral ENPP1 inhibitor, ISM5939.

Carnegie Mellon University researchers have developed a new way to help doctors make better, personalized decisions and predict how a disease or treatment might play out in the future. Researchers from CMU’s School of Computer Science developed a new approach to bridge the gap between available data and actionable insight, creating personalized models to help doctors better understand individual patients and improve their prognosis. The researchers published their work in the Proceedings of the National Academy of Sciences. The team introduced contextualized modeling, a family of ultra-personalized machine learning methods, to build individualized gene network models for nearly 8,000 tumors across 25 cancer types simultaneously. These networks helped identify new cancer biology, revealing hidden cancer subtypes and improving survival predictions, especially for rare cancers. This development opens the door to more precise, individualized cancer treatment.

A research paper by scientists at Chinese Academy of Sciences proposed a dual-task learning framework, the “Twin Brother” model, which fuses convolutional neural network (CNN), long short-term memory (LSTM), neural networks (NNs), and the squeezing-elicited attention mechanism to classify the lateral gait stage and estimate the hip angle from electromyography (EMG) signals.

The new research paper, published on May. 1 in the journal Cyborg and Bionic Systems, provide a “Twin Brother” model. The model is a dual-task learning framework designed for simultaneous gait phases recognition for lateral walking and continuous hip angle prediction.