A Nano-Electro-Platform Enabling Evolutionary Screening and Remodeling of Tumor Cells for Metastasis Inhibition.

Tumor metastasis remains the leading cause of mortality among cancer patients. Addressing this challenge necessitates the development of effective strategies for targeted drug delivery and therapy. Given that metastatic lesions are primarily driven by highly aggressive tumor cell subpopulations, in-depth study of these cells and further guiding design of targeted therapeutics, play deterministic roles in metastasis inhibition.

VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction.

The viruses threats provoke concerns regarding their sustained epidemic transmission, making the development of vaccines particularly important. In the prolonged and costly process of vaccine development, the most important initial step is to identify protective immunogens. Machine learning (ML) approaches are productive in analyzing big data such as microbial proteomes, and can remarkably reduce the cost of experimental work in developing novel vaccine candidates.

Stiff extracellular matrix drives the differentiation of mesenchymal stem cells toward osteogenesis by the multiscale 3D genome reorganization.

Extracellular matrix (ECM) stiffness is a major driver of stem cell fate. However, the involvement of the three-dimensional (3D) genomic reorganization in response to ECM stiffness remains unclear. Here, we generated comprehensive 3D chromatin landscapes of mesenchymal stem cells (MSCs) exposed to various ECM stiffness.

BCEDB: a linear B-cell epitopes database for SARS-CoV-2.

The 2019 Novel Coronavirus (SARS-CoV-2) has infected millions of people worldwide and caused millions of deaths. The virus has gone numerous mutations to replicate faster, which can overwhelm the immune system of the host. Linear B-cell epitopes are becoming promising in prevention of various deadly infectious diseases, breaking the general idea of their low immunogenicity and partial protection.