Characteristics of T-Cell Receptor Repertoire for Differential Response to Methotrexate Treatment for Rheumatoid Arthritis.

Background: Methotrexate (MTX) serves as the initial treatment for rheumatoid arthritis (RA). However, a substantial proportion of RA patients, estimated between 30% and 50%, do not respond positively to MTX. While the T-cell receptor (TCR) is crucial for the immune response during RA, its role in differentiating MTX responsiveness has not been thoroughly investigated.

Extracellular Vesicle Spherical Nucleic Acids.

Extracellular vesicles (EVs) are naturally occurring vesicles secreted by cells that can transport cargo between cells, making them promising bioactive nanomaterials. However, due to the complex and heterogeneous biological characteristics, a method for robust EV manipulation and efficient EV delivery is still lacking. Here, we developed a novel class of extracellular vesicle spherical nucleic acid (EV-SNA) nanostructures with scalability, programmability, and efficient cellular delivery.

Identification of novel protein biomarkers from the blood and urine for the early diagnosis of bladder cancer via proximity extension analysis.

Background: Bladder cancer (BC) is a very common urinary tract malignancy that has a high incidence and lethality. In this study, we identified BC biomarkers and described a new noninvasive detection method using serum and urine samples for the early detection of BC.

Methods: Serum and urine samples were retrospectively collected from patients with BC (n = 99) and healthy controls (HC) (n = 50), and the expression levels of 92 inflammation-related proteins were examined via the proximity extension analysis (PEA) technique.

DNA Nanomaterial-Empowered Surface Engineering of Extracellular Vesicles.

Extracellular vesicles (EVs) are cell-secreted biological nanoparticles that are critical mediators of intercellular communication. They contain diverse bioactive components, which are promising diagnostic biomarkers and therapeutic agents. Their nanosized membrane-bound structures and innate ability to transport functional cargo across major biological barriers make them promising candidates as drug delivery vehicles.

Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.

Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by related methods based on protein language models, which are trained on a large number of unlabeled protein sequences to capture the generally hidden evolutionary rules in protein sequences, and are therefore able to predict their fitness from protein sequences. Although numerous similar models and methods have been successfully employed in practical protein engineering processes, the majority of the studies have been limited to how to construct more complex language models to capture richer protein sequence feature information and utilize this feature information for unsupervised protein fitness prediction.