Female researchers from Universidad Carlos III de Madrid (UC3M) and Hospital Universitario Severo Ochoa in Leganés, part of the public health network of the Comunidad de Madrid, have developed a methodology that uses artificial intelligence (AI) to analyze brain electrical activity during sleep and facilitate the early diagnosis of Alzheimer's. The study, recently published in the scientific journal GeroScience, demonstrates that analyzing nocturnal brain waves using machine learning techniques makes it possible to non-invasively identify early neural alterations and classify patients into three distinct biological subgroups.

The study tests a chaos-based encryption scheme on real patient scans and report categories, aiming to keep e-healthcare data safe as it disseminates across the internet. Hospitals and clinics increasingly store and share patient information digitally in the form of X-rays, CT scans, and other medical images that deeply reveal personal health details. This convenience comes with risk: once this data moves across the internet, it becomes a target for interception or tampering. A new study, published ahead of print in Wireless and Communication Letters, proposes a way to protect these images using the mathematics of chaos.

A new review in Current Cell Science brings together the science behind BRCA1, PSA, KRAS, and other biomarkers now used to detect cancer earlier and track how well the treatment is working. Cancer remains one of the leading causes of death worldwide, and part of what makes it so difficult to manage is its sheer diversity — more than 100 distinct types can arise, depending on which tissue or organ is affected. A new review, published ahead-of-print in Current Cell Science, takes stock of one of the most important tools researchers and clinicians now rely on to make sense of this complexity: cancer biomarkers.