Radiomics-Based Machine Learning to Predict Recurrence in Glioma Patients Using Magnetic Resonance Imaging.

Objective: Recurrence is a major factor in the poor prognosis of patients with glioma. The aim of this study was to predict glioma recurrence using machine learning based on radiomic features.

Methods: We recruited 77 glioma patients, consisting of 57 newly diagnosed patients and 20 patients with recurrence.

Down-regulation of microRNA-223 promotes degranulation via the PI3K/Akt pathway by targeting IGF-1R in mast cells.

Background: Mast cells play a central role in allergic and inflammatory disorders by inducing degranulation and inflammatory mediator release. Recent reports have shown that miRNAs play an important role in inflammatory response regulation. Therefore, the role of miR-223 in mast cells was investigated.