Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

Objective: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms.

Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis.

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study.

Objective: Acute ischemic stroke (AIS) brings an increasingly heavier economic burden nowadays. Prolonged length of stay (LOS) is a vital factor in healthcare expenditures. The aim of this study was to predict prolonged LOS in AIS patients based on an interpretable machine learning algorithm.

Emerging role of substance and energy metabolism associated with neuroendocrine regulation in tumor cells.

Cancer is the second most common cause of mortality in the world. One of the unresolved difficult pathological mechanism issues in malignant tumors is the imbalance of substance and energy metabolism of tumor cells. Cells maintain life through energy metabolism, and normal cells provide energy through mitochondrial oxidative phosphorylation to generate ATP, while tumor cells demonstrate different energy metabolism.