Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning.

The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.

PD-1/PD-L1 Blockade Accelerates the Progression of Atherosclerosis in Cancer Patients.

PD-1(programed death-1)/PD-L1(programed death-1 ligand) blockade represents a major breakthrough of anti-cancer therapies, however, it may come with increased risk of cardiovascular morbidity, such as myocarditis, acute coronary syndrome, arrhythmias, etc. Although the PD-1/PD-L1-blockade-related acute coronary syndrome (ACS) is rare, it can be fatal. Previous studies have implicated a role of the PD-1/PD-L1 axis in the development of atherosclerosis. This review explores a hypothesis that PD-1/PD-L1 blockade accelerates the progression of atherosclerosis and promotes plaque rupture, by synthesizing the evidence of vascular inflammation, as well as plaque progression, destabilization and rupture via T-cell activation and effector function. In order to improve the prognosis of cancer patients and decrease the cardiotoxicity of PD-1/PD-L1 blockade therapy, early recognition of PD-1/PD-L1-blockade-related ACS is important.