Robust deep learning from incomplete annotation for accurate lung nodule detection.

Deep learning plays a significant role in the detection of pulmonary nodules in low-dose computed tomography (LDCT) scans, contributing to the diagnosis and treatment of lung cancer. Nevertheless, its effectiveness often relies on the availability of extensive, meticulously annotated dataset. In this paper, we explore the utilization of an incompletely annotated dataset for pulmonary nodules detection and introduce the FULFIL (Forecasting Uncompleted Labels For Inexpensive Lung nodule detection) algorithm as an innovative approach.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

Background: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course.

Methods: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data.

Long non‑coding RNA MEG3 contributes to cisplatin‑induced apoptosis via inhibition of autophagy in human glioma cells.

Long non-coding RNAs (lncRNAs) function as oncogenes or tumor suppressors, and are involved in mediating tumorigenesis and resistance to chemotherapy by altering the expression of genes at various levels. Accumulating evidence suggests that the maternally expressed gene 3 (MEG3) lncRNA serves an important role in a number of cancers. However, its functional role in mediating cisplatin‑induced apoptosis of glioma cells is unknown.