Shenqing Wang

TL;DR: This work advocates greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability.

TL;DR: The findings indicate that only certain pollutants in the payloads of PM2.5 are responsible for causing cellular oxidative stress, cell apoptosis, and cytotoxicity while the particle carriers are much less toxic, and reductionism principle and an ultrafine particle library approach are very effective in search for a better understanding of PM1.5-induced health effects.

TL;DR: A novel computational approach was developed to precisely predict the hydrophobicity of gold nanoparticles (GNPs) and this approach and resulted model is an efficient and effective universal tool to visualize and predict critical physicochemical properties of newnanomaterials before synthesis, guiding nanomaterial design.

TL;DR: In this article , the authors used computer-aided molecule design and machine learning to discover two new, broad-spectrum adjuvants that can boost vaccine responses, including 46 toll-like receptor (TLR)-targeting ligands.