Charge-reversal biodegradable MSNs for tumor synergetic chemo/photothermal and visualized therapy.

Given the difficulties of biodegradation of mesoporous silica nanoparticles (NPs), enrichment and penetration of tumor sites, and real-time monitoring of the treatment process, we developed a kind of mannose-doping doxorubicin-loading mesoporous silica nanoparticle (MSN-Man-DOX) and coated by polydopamine-Gd3+ (PDAGd) metal-phenolic networks, as well as modified by poly (2-Ethyl-2-Oxazoline) (PEOz), constructing a novel nanomedicine MSN-Man-DOX@PDA-Gd-PEOz. Its pH-responsive charge reversal, photothermal, biodegradation, drug release, and magnetic resonance imaging (MRI) properties were evaluated in vitro. Cellular uptake, tumor penetration, lysosomal escape properties, as well as cell safety and toxicity of the nanoplatform were investigated through cell experiments. Finally, the MRI, organ distribution, photothermal condition, and comprehensive anti-tumor therapy in vivo were evaluated comprehensively through animal experiments. Research results showed that MSN-Man-DOX@PDA-Gd-PEOz had outstanding tumor enrichment and penetration abilities, which can produce excellent treatment effects through the synergistic effect of chemotherapy and photothermal therapy (PTT) with the function of magnetic resonance imaging contrast agent for disease monitoring. Besides, after finishing the therapeutic effect MSN-Man-DOX@PDA-Gd-PEOz can be biodegraded, so it had a good prospect of clinical application.

Insight into potent leads for alzheimer's disease by using several artificial intelligence algorithms.

Several proteins including S-nitrosoglutathione reductase (GSNOR), complement Factor D, complement 3b (C3b) and Protein Kinase R-like Endoplasmic Reticulum Kinase (PERK), have been demonstrated to be involved in pathogenesis pathways for Alzheimer's disease (AD) and considered as potential treatment targets to AD. Based on the concept of multitargets, a network pharmacology-based approach was employed to investigate potential Traditional Chinese Medicine (TCM) candidates that can dock well with GSNOR, C3b, Factor D and PERK proteins. To predict the bioactivities of candidates, Artificial Intelligence (AI) algorithms composed of seven machine learning algorithms and a deep learning model were performed to validate the docking results. Furthermore, in this study, we propose a novel combined method for efficiently exploring the predicted results of AI algorithms. Besides, Comparative force field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) were performed to construct predicted models. The results show that the square correlation coefficients (R2) of all models are almost higher than 0.75, which also acquire good achievements on the test set. Moreover, the binding stability of the potential inhibitors were evaluated using 100 ns of MD simulation. Collectively, this study elucidate that the herbs Ardisia japonica, Ligusticum chuanxiong, Lippia nodiflora and Mirabilis jalapa containing 2,2'-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid), Glyasperin B, Nodifloridin A, Miraxanthin III and l-Valine-l-valine anhydride might be a potential medicine formula for AD.