Unexplained changes in cholesterol may help identify older adults at risk for dementia
Reports and Proceedings
Updates every hour. Last Updated: 1-May-2025 11:08 ET (1-May-2025 15:08 GMT/UTC)
A study of older adults in Australia and the U.S. indicates that cholesterol levels that fluctuate significantly from year to year without a change in medication may someday help to identify those with a higher risk of developing dementia. In a six-year study of almost 10,000 adults in their 70s, researchers found people with stable cholesterol levels had a significantly lower risk of developing dementia or showing cognitive decline compared to those with fluctuating cholesterol levels.
Severe temperature spikes may double or triple the risk of irregular heart rhythm in people with implanted defibrillators. An analysis of health data for more than 2,000 people with implantable cardioverter defibrillators (ICDs) found that temperatures reaching 100°F (38°C) were more likely to lead to atrial fibrillation events.
The application process for the 12th Heidelberg Laureate Forum has begun!
Young researchers in mathematics and computer science from all over the world can apply for one of the 200 exclusive spots to participate in the Heidelberg Laureate Forum (HLF), an annual networking conference. The HLF offers all accepted young researchers the unique opportunity to interact with the laureates of the most prestigious prizes in the fields of mathematics and computer science. Traditionally, the recipients of the Abel Prize, the ACM A.M. Turing Award, the ACM Prize in Computing, the Fields Medal, the IMU Abacus Medal and the Nevanlinna Prize engage in cross-generational scientific dialogue with young researchers in Heidelberg, Germany.
Two quantum information theorists at the University of Sydney have solved a decades-old problem that will free up quantum computing power.
A recent study by researchers from CSIRO and the University of Melbourne has made progress in quantum machine learning, a field aimed at using quantum advantage to outperform classical machine learning. Their work demonstrates that quantum circuits for data encoding in quantum machine learning can be greatly simplified without compromising accuracy or robustness. This research was published Sept.12 in Intelligent Computing, a Science Partner Journal.