Researchers at Colorado State University have determined how to use artificial intelligence (AI) to modify antibodies so they act as lightbulbs, enabling scientists to better see inside living cells to track errors in gene expression that can lead to cancer and other disorders.

The findings, published in Science Advances, outline an approach that is significantly faster than existing manual testing and development methods to address an ongoing challenge to see activity in tiny cells continuously and clearly.

Researchers at Bielefeld University have analyzed how reliably AI methods can detect pulse rates from simple video recordings. The technique, known as rPPG - remote photoplethysmography, an optical form of contactless pulse measurement - is considered a future tool of telemedicine. But a new study in the journal npj Digital Medicine reveals clear weaknesses as soon as heart rates rise.

A poor night’s sleep portends a bleary-eyed next day, but it could also hint at diseases that will strike years down the road. A new artificial intelligence model developed by Stanford Medicine researchers and their colleagues can use physiological recordings from one night’s sleep to predict a person’s risk of developing more than 100 health conditions.

Obtaining clearer functional MRI data about the brain and its disorders is possible using artificial intelligence (AI), according to Boston College researchers who reported recently in Nature Methods that they developed an AI-assisted method to remove “noise”, or image distortions, caused by movement, heartbeat, and other factors.

Using machine learning and a large volume of data on genes and existing drugs, researchers at Lund University in Sweden have identified a combination of statins and phenothiazines that is particularly promising in the treatment of the aggressive form of neuroblastoma. The results from experimental trials showed slowing of tumour growth and higher survival rates.

A team led by investigators at Mass General Brigham and Dana-Farber Cancer Institute has developed and validated an artificial intelligence (AI)-based noninvasive tool that can predict the likelihood that a patient’s oropharyngeal cancer - a type of head and neck cancer that develops in the throat - will spread, thereby signaling which patients should receive aggressive treatment.

Researchers from IRTA and IrsiCaixa, partners in the EPIVINF project, have identified the golden Syrian hamster as a potential relevant model for studying the biology of long COVID, also known as post-COVID-19 condition (PCC).

Long COVID is not a single disease but a complex syndrome involving a range of persistent symptoms. People suffering from it report overwhelming fatigue, memory lapses, "brain fog,"