Revisiting drug-protein interaction prediction: a novel global-local perspective.

Motivation: Accurate inference of potential drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance.

Results: We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference.

An efficient model for predicting human diseases through miRNA based on multiple-types of contrastive learning.

Multiple studies have demonstrated that microRNA (miRNA) can be deeply involved in the regulatory mechanism of human microbiota, thereby inducing disease. Developing effective methods to infer potential associations between microRNAs (miRNAs) and diseases can aid early diagnosis and treatment. Recent methods utilize machine learning or deep learning to predict miRNA-disease associations (MDAs), achieving state-of-the-art performance.

Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders.

MicroRNAs (miRNAs) are short RNA molecular fragments that regulate gene expression by targeting and inhibiting the expression of specific RNAs. Due to the fact that microRNAs affect many diseases in microbial ecology, it is necessary to predict microRNAs' association with diseases at the microbial level. To this end, we propose a novel model, termed as GCNA-MDA, where dual-autoencoder and graph convolutional network (GCN) are integrated to predict miRNA-disease association.

A valve-based microfluidic device for on-chip single cell treatments.

Assays toward single-cell analysis have attracted the attention in biological and biomedical researches to reveal cellular mechanisms as well as heterogeneity. Yet nowadays microfluidic devices for single-cell analysis have several drawbacks: some would cause cell damage due to the hydraulic forces directly acting on cells, while others could not implement biological assays since they could not immobilize cells while manipulating the reagents at the same time. In this work, we presented a two-layer pneumatic valve-based platform to implement cell immobilization and treatment on-chip simultaneously, and cells after treatment could be collected non-destructively for further analysis.

Erythrocyte membrane-coated gold nanocages for targeted photothermal and chemical cancer therapy.

Recently, red blood cell (RBC) membrane-coated nanoparticles have attracted much attention because of their excellent immune escapability; meanwhile, gold nanocages (AuNs) have been extensively used for cancer therapy due to their photothermal effect and drug delivery capability. The combination of the RBC membrane coating and AuNs may provide an effective approach for targeted cancer therapy. However, few reports have shown the utilization of combining these two technologies.

Microfluidic Electroporation-Facilitated Synthesis of Erythrocyte Membrane-Coated Magnetic Nanoparticles for Enhanced Imaging-Guided Cancer Therapy.

Biomimetic cell membrane-coated nanoparticles (CM-NPs) with superior biochemical properties have been broadly utilized for various biomedical applications. Currently, researchers primarily focus on using ultrasonic treatment and mechanical extrusion to improve the synthesis of CM-NPs. In this work, we demonstrate that microfluidic electroporation can effectively facilitate the synthesis of CM-NPs.