Whether the infracardiac bursa protect right pleura during laparoscopic radical operation of Siewert type II adenocarcinoma of esophagogastric junction?

Background: Transthoracic single-port assisted laparoscopic five-step maneuver inferior mediastinal lymphadenectomy for Siewert type II adenocarcinoma of esophagogastric junction (AEG) has superiority in lower mediastinal lymph nodes dissection and digestive tract reconstruction. However, the right pleura was probably ruptured in this surgical technique. The aim of this study was to explore whether the infracardiac bursa (ICB) exposed could protect right pleura.

IMCHGAN: Inductive Matrix Completion With Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction.

Identification of targets among known drugs plays an important role in drug repurposing and discovery. Computational approaches for prediction of drug-target interactions (DTIs)are highly desired in comparison to traditional biological experiments as its fast and low price. Moreover, recent advances of systems biology approaches have generated large-scale heterogeneous, biological information networks data, which offer opportunities for machine learning-based identification of DTIs.

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction.

Motivation: Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA.

Results: We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association.