Researchers at the Champalimaud Foundation have developed a label-free optical imaging method that can distinguish between benign and malignant colorectal tissue with high accuracy. Using autofluorescence lifetime measurements and artificial intelligence, the team analyzed surgical specimens from 117 patients and trained a machine learning model to detect cancer-related biochemical signatures. The approach achieved 85 percent accuracy on unseen test data, highlighting its potential for real-time cancer detection during colonoscopy or surgery. This technique could improve early diagnosis and reduce the need for invasive biopsies.

Scientists at St. Jude Children’s Research Hospital found that droplets within cells, called condensates, are required for ependymoma formation, creating new opportunities for drug development.