Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach.

Protein phase transitions (PPTs) from the soluble state to a dense liquid phase (forming droplets via liquid-liquid phase separation) or to solid aggregates (such as amyloids) play key roles in pathological processes associated with age-related diseases such as Alzheimer's disease. Several computational frameworks are capable of separately predicting the formation of droplets or amyloid aggregates based on protein sequences, yet none have tackled the prediction of both within a unified framework. Recently, large language models (LLMs) have exhibited great success in protein structure prediction; however, they have not yet been used for PPTs.

More than bad luck: Cancer and aging are linked to replication-driven changes to the epigenome.

Aging is a leading risk factor for cancer. While it is proposed that age-related accumulation of somatic mutations drives this relationship, it is likely not the full story. We show that aging and cancer share a common epigenetic replication signature, which we modeled using DNA methylation from extensively passaged immortalized human cells in vitro and tested on clinical tissues.

The biomechanical efficacy of dressings in preventing heel ulcers.

The heels are the most common site for facility-acquired pressure ulcers (PUs), and are also the most susceptible location for deep tissue injuries. The use of multilayer prophylactic dressings to prevent heel PUs is a relatively new prevention concept, generally aimed at minimizing the risk for heel ulcers (HUs) through mechanical cushioning and reduction of friction at the dressing-support interface. We used 9 finite element model variants of the posterior heel in order to evaluate the biomechanical performance of a multilayer dressing in prevention of HUs during supine lying.