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. METHODS: We retrospectively collected and evaluated the clinical and pathological data of patients who underwent five-step maneuver of transthoracic single-port assisted laparoscopic lower mediastinal lymphadenectomy for Siewert II AEG at Guangdong Provincial Hospital of Chinese Medicine between May 2017 and February 2022. RESULTS: A total of 49 patients were eligible, including 31 patients in ICB exposed group (group A) and 18 patients in ICB unexposed group (group B). There were no statistically significant differences in baseline characteristics between the two groups. 4 patients (12.9%) had right pleura rupture in group A, while 14 patients (77.8%) in group B, and the difference was statistically significant (p < 0.001). Compared with group B, the extubation time of endotracheal intubation (10.0 (6.0 ~ 12.0) vs. 13.0 (8.0 ~ 15.0) min, p = 0.003) and thoracic drainage tube stay (6.0 (5.0 ~ 7.0) vs. 8.0 (6.0 ~ 10.5) days, p = 0.041) were significantly shorted in the group A. The drainage volume of thorax (351.61 ± 125.00 vs. 418.61 ± 207.86 mL, p = 0.146) was non-significant less and the rate of complications (3.2% vs. 11.1%, p = 0.074) was non-significant lower in group A compared with group B. The postoperative hospital stay (9.0 (8.0,13.0) vs. 9.0 (8.0,12.0) days, p = 0.983) were similar in two groups. No serious adverse event occurred in any patient. CONCLUSIONS: The ICB exposed could protect the right pleura and may promote postoperative recovery, which may be used as an anatomical marker in inferior mediastinal lymphadenectomy.

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. We present a novel Inductive Matrix Completion with Heterogeneous Graph Attention Network approach (IMCHGAN)for predicting DTIs. IMCHGAN first adopts a two-level neural attention mechanism approach to learn drug and target latent feature representations from the DTI heterogeneous network respectively. Then, the learned latent features are fed into the Inductive Matrix Completion (IMC)prediction score model which computes the best projection from drug space onto target space and output DTI score via the inner product of projected drug and target feature representations. IMCHGAN is an end-to-end neural network learning framework where the parameters of both the prediction score model and the feature representation learning model are simultaneously optimized via backpropagation under supervising of the observed known drug-target interactions data. We compare IMCHGAN with other state-of-the-art baselines on two real DTI experimental datasets. The results show that our method is superior to existing methods in term of AUC and AUPR. Moreover, IMCHGAN also shows it has strong predictive power for novel (unknown)DTIs. All datasets and code can be obtained from https://github.com/ljatynu/IMCHGAN/.

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. NIMCGCN first uses graph convolutional networks to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The area under the receiver operating characteristic curve results showed that our method is significantly superior to existing methods. Furthermore, 50, 47 and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs. AVAILABILITY AND IMPLEMENTATION: https://github.com/ljatynu/NIMCGCN/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.