Kyoto, Japan -- Whether you practice ballet or prefer the tango, the benefits of dancing are self-evident. It's good exercise both physically and mentally due to the complexity of the movements, and it's also a fun social activity. But the benefits of dancing may extend beyond this: the mental activity and social interaction involved in dancing may also help prevent cognitive decline.

Previous research indicates that dance practice can improve the cognitive test scores of older adults with mild cognitive impairment, or MCI, an intermediate state of cognitive decline between normal aging and dementia. This inspired a team of researchers from Kyoto University to extend this research to older adults in an earlier stage of cognitive decline called subjective cognitive decline, or SCD. This refers to an individual's self-reported worsening memory or increased confusion that cannot yet be verified by tests.

"We focused on SCD because earlier intervention is more important from the viewpoint of dementia prevention," says first author Masatoshi Yamashita.

Endoplasmic reticulum (ER) stress and the unfolded protein response are often implicated in the progression of metabolic dysfunction-associated steatotic liver disease (MASLD), from simple steatosis to inflammation-driven metabolic dysfunction-associated steatohepatitis and, ultimately, hepatocellular carcinoma. A new review paper from Hanyang University ERICA sheds light on the mechanisms underlying these processes, advocating for precise hepatic ER stress control to maintain normal hepatocyte function and develop therapeutic strategies against MASLD.

Ammonia gas, a popular industrial chemical, is dangerous to human health. A new study by Hanbat National University researchers presents a wearable ammonia gas sensor that detects harmful ammonia levels visually and electronically. The sensor is flexible, stretchable, and works reliably when attached to human skin and exposed to high humidity. By combining two sensing methods in one device, the platform remains accurate even if one sensing mode fails, making it suitable for real-world use.

Finely dispersed particulate matter with a diameter of ≤2.5 μm (PM_2.5) poses a significant health- and climate-risk, yet tracking its chemical composition remains a challenge. Now, researchers have developed a deep-learning model that accurately estimates hourly concentrations of five key PM_2.5 chemical components, without chemical analysis. Using air-quality and meteorological data, the model achieved high accuracy outperforming existing methods, and may strengthen air-pollution monitoring, fill data gaps, and support targeted emission control strategies worldwide.