RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders.

RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome replication, transcription and translation, and signaling. Predicting RPI can guide the exploration of cellular biological functions, intervening in diseases, and designing drugs. Given this, this study proposes the RPI-gated graph convolutional network (RPI-GGCN) method for predicting RPI based on the gated graph convolutional neural network (GGCN) and co-regularized variational autoencoder (Co-VAE). First, different types of feature information were extracted from RNA and protein sequences by nine feature extraction methods. Second, Co-VAEs are used to eliminate the redundancy of fused features and generate optimal features. Finally, this study introduces gated cyclic units into graph convolutional networks (GCNs) to construct a model for RPI prediction, which efficiently extracts topological information and improves the model's interpretable feature learning and expression capabilities. In the fivefold cross-validation test, the RPI-GGCN method achieved prediction accuracies of 97.27%, 97.32%, 96.54%, 95.76%, and 94.98% on the RPI369, RPI488, RPI1446, RPI1807, and RPI2241 datasets. To test the generalization performance of the model, we used the model trained on RPI369 to predict the independent NPInter v3.0 dataset and achieved excellent performance in all six independent validation sets. By visualizing the RPI network graph based on the prediction results, we aim to provide a new perspective and reference for studying RPI mechanisms and exploring new RPIs. Extensive experimental results demonstrate that RPI-GGCN can provide an efficient, accurate, and stable RPI prediction method.

Gut Microbiome-Based Therapeutics in Critically Ill Adult Patients-A Narrative Review.

The gut microbiota plays a crucial role in the human microenvironment. Dysbiosis of the gut microbiota is a common pathophysiological phenomenon in critically ill patients. Therefore, utilizing intestinal microbiota to prevent complications and improve the prognosis of critically ill patients is a possible therapeutic direction. The gut microbiome-based therapeutics approach focuses on improving intestinal microbiota homeostasis by modulating its diversity, or treating critical illness by altering the metabolites of intestinal microbiota. There is growing evidence that fecal microbiota transplantation (FMT), selective digestive decontamination (SDD), and microbiota-derived therapies are all effective treatments for critical illness. However, different treatments are appropriate for different conditions, and more evidence is needed to support the selection of optimal gut microbiota-related treatments for different diseases. This narrative review summarizes the curative effects and limitations of microbiome-based therapeutics in different critically ill adult patients, aiming to provide possible directions for gut microbiome-based therapeutics for critically ill patients such as ventilator-associated pneumonia, sepsis, acute respiratory distress syndrome, and COVID-19, etc.