Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma.

Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patients. The efficiency of the model was then evaluated in independent multicenter cohorts.

Raptin, a sleep-induced hypothalamic hormone, suppresses appetite and obesity.

Sleep deficiency is associated with obesity, but the mechanisms underlying this connection remain unclear. Here, we identify a sleep-inducible hypothalamic protein hormone in humans and mice that suppresses obesity. This hormone is cleaved from reticulocalbin-2 (RCN2), and we name it Raptin.

Hierarchical Porous Aggregate-Enabled Chromatography-Inspired Single-Sensor E-Nose for Volatile Monitoring.

Monitoring volatile organic compounds (VOCs) is crucial for ensuring safety and health. In this study, we introduce a strategy to engineer a chromatography-inspired single-sensor (CISS) e-nose tailored for VOC monitoring. This approach overcomes the limitations of traditional methodologies and conventional e-noses.

Association Between Liver Fibrosis and Risk of Incident Stroke and Mortality: A Large Prospective Cohort Study.

Background: There is a well-established relationship between liver conditions and cardiovascular diseases. However, uncertainty persists regarding the contribution of liver fibrosis to major stroke types including ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage at the population level.

Methods: In this large prospective cohort study, participants without previous stroke or coronary heart disease at baseline from the UK Biobank were included.

Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning.

Background: Head and neck free flap reconstruction presents challenges in managing intraoperative circulation, potentially leading to prolonged length of stay (PLOS). Limited research exists on the associations between intraoperative circulation and PLOS given the difficulty of manual quantification of intraoperative circulation time-series data. Therefore, this study aimed to quantify intraoperative circulation data and investigate its association with PLOS after free flap reconstruction utilizing machine learning algorithms.