Colon-Accumulated Gold Nanoclusters Alleviate Intestinal Inflammation and Prevent Secondary Colorectal Carcinogenesis via Nrf2-Dependent Macrophage Reprogramming.

Inflammatory bowel disease (IBD) is one of the main factors leading to colitis-associated colorectal cancer (CAC). Therefore, it is critical to develop an effective treatment for IBD to prevent secondary colorectal carcinogenesis. M2 macrophages play crucial roles in the resolution phase of intestinal inflammation. However, traditional drugs rarely target intestinal M2 macrophages, and they are not easily cleared. Gold nanoclusters are known for their in vivo safety and intrinsic biomedical activities. In this study, a glutathione-protected gold nanocluster is synthesized and evaluated, namely, GA. Interestingly, GA specifically accumulates in the colon during IBD. Furthermore, GA not only promotes M2 differentiation of IL-4-treated peritoneal macrophages but also reprograms macrophage polarization from M1 to M2 in a pro-inflammatory environment. Mechanistically, this regulatory effect is exerted through activating the antioxidant Nrf2 signaling pathway, but not traditional STAT6. When applied in IBD mice, we found that GA elevates M2 macrophages and alleviates IBD in an Nrf2-dependent manner, evidenced by the abolished therapeutic effect upon Nrf2 inhibitor treatment. Most importantly, GA administration significantly suppresses AOM/DSS-induced CAC, without causing obvious tissue damage, providing critical evidence for the potential application of gold nanoclusters as nanomedicine for the treatment of IBD and CAC.

ReaxFF-MPNN machine learning potential: a combination of reactive force field and message passing neural networks.

Reactive force field (ReaxFF) is a powerful computational tool for exploring material properties. In this work, we proposed an enhanced reactive force field model, which uses message passing neural networks (MPNN) to compute the bond order and bond energies. MPNN are a variation of graph neural networks (GNN), which are derived from graph theory. In MPNN or GNN, molecular structures are treated as a graph and atoms and chemical bonds are represented by nodes and edges. The edge states correspond to the bond order in ReaxFF and are updated by message functions according to the message passing algorithms. The results are very encouraging; the investigation of the potential, such as the potential energy surface, reaction energies and equation of state, are greatly improved by this simple improvement. The new potential model, called reactive force field with message passing neural networks (ReaxFF-MPNN), is provided as an interface in an atomic simulation environment (ASE) with which the original ReaxFF and ReaxFF-MPNN potential models can do MD simulations and geometry optimizations within the ASE. Furthermore, machine learning, based on an active learning algorithm and gradient optimizer, is designed to train the model. We found that the active learning machine not only saves the manual work to collect the training data but is also much more effective than the general optimizer.