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.

New bioinformatics study screens three diabetes-linked genes and builds PCR primers that could support low-cost genetic risk testing, particularly in resource-limited settings. Diabetes is one of the fastest-growing health concerns worldwide, and a large share of that burden falls on developing countries where early screening tools are often too expensive or too complex for routine use. A new study, published ahead of print in Current Science, Engineering and Technology, tackles this problem from the perspective of genetics, i.e., by designing and computationally testing molecular tools that could one day help identify people at higher genetic risk of type 2 diabetes before the disease develops.New bioinformatics study screens three diabetes-linked genes and builds PCR primers that could support low-cost genetic risk testing, particularly in resource-limited settings. Diabetes is one of the fastest-growing health concerns worldwide, and a large share of that burden falls on developing countries where early screening tools are often too expensive or too complex for routine use. A new study, published ahead of print in Current Science, Engineering and Technology, tackles this problem from the perspective of genetics, i.e., by designing and computationally testing molecular tools that could one day help identify people at higher genetic risk of type 2 diabetes before the disease develops.