Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine.

The high rate of early recurrence in hepatocellular carcinoma (HCC) post curative surgical intervention poses a substantial clinical hurdle, impacting patient outcomes and complicating postoperative management. The advent of machine learning provides a unique opportunity to harness vast datasets, identifying subtle patterns and factors that elude conventional prognostic methods. Machine learning models, equipped with the ability to analyse intricate relationships within datasets, have shown promise in predicting outcomes in various medical disciplines.

The role of social closeness during tape stripping to facilitate skin barrier recovery: Preliminary findings.

Objective: Social support is known to reduce the negative effects of stress on health, but there is mixed evidence for the effects of social support on wound healing. This study aimed to investigate whether undergoing a task designed to promote social closeness with a fellow participant and being paired with that person during a tape-stripping procedure could reduce stress and improve skin barrier recovery compared to going through tape stripping alone.

Method: Seventy-two healthy adults were randomized to either a social closeness condition where participants completed a relationship-building task and tape stripping in pairs or a control condition where they completed tape stripping alone.