Scientists at Auburn University have made an important discovery that could improve cancer treatments. Just like a coach places players in the best spots on a football field, researchers found that carefully positioning protein molecules can make them more effective in delivering cancer therapies. By attaching these proteins at specific points on a structure that targets cancer cells, the team was able to increase the proteins' effectiveness by up to four times. This approach could lead to treatments that are stronger, more focused, and better equipped to target cancer cells directly. Auburn’s biophysicists are advancing this research to create better tools in the fight against cancer.

The detection of cancer-associated micro-ribonucleic acids (miRNAs) in urine through a combination of nanowire-based miRNA extraction and machine learning (ML) analysis by researchers from Institute of Science Tokyo (Science Tokyo) can fuel the development of early-stage cancer diagnostic tools. The nanowire-based capture and extraction of miRNAs in urine enabled the detection of more than 2,000 distinct miRNA species, while the ML-based classifier accurately distinguished between cancerous and noncancerous samples.

Anglia Ruskin University in England has received $100,000 of funding from the US-based Colorectal Cancer Alliance to investigate the link between fat and cancer. The study is examining how fat tissue impacts the development and treatment of colorectal cancer in both healthy and obese individuals.

A “deep learning” artificial intelligence model developed at Washington State University can identify pathology, or signs of disease, in images of animal and human tissue much faster, and often more accurately, than people. The development could dramatically speed up the pace of disease-related research. It also holds potential for improved medical diagnosis, such as detecting cancer from a biopsy image in a matter of minutes, a process that typically takes a human pathologist several hours.