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Bacterial biofilm infection is a serious obstacle to clinical therapeutics. Photodynamic therapy (PDT) plays a dynamic role in combating biofilm infection by utilizing reactive oxygen species (ROS)-induced bacterial oxidation injury, showing advantages of mild side effects, spatiotemporal controllability and little drug resistance. However, superfluous glutathione (GSH) present in biofilm and bacteria corporately reduces ROS levels and seriously affects PDT efficiency. Herein, we have constructed a Cu2+-infused porphyrin metal–organic framework (MOF@Cu2+) for the enhanced photodynamic combating of biofilm infection by the maximum depletion of GSH. Our results show that the released Cu2+ from porphyrin MOF@Cu2+ could not only oxidize GSH in biofilm but also consume GSH leaked from ROS-destroyed bacteria, thus greatly weakening the antioxidant system in biofilm and bacteria and dramatically improving the ROS levels. As expected, our dual-enhanced PDT nanoplatform exhibits a strong biofilm eradication ability both in vitro and in an in vivo biofilm-infected mouse model. In addition, Cu2+ can promote biofilm-infected wound closing by provoking cell immigration, collagen sediment and angiogenesis. Besides, no apparent toxicity was detected after treatment with MOF@Cu2+. Overall, our design offers a new paradigm for photodynamic combating biofilm infection. [ABSTRACT FROM AUTHOR]

Conventional topical medications for recurrent aphthous ulcers (RAU) are limited due to the washing effects of saliva and activities of masticatory muscles decreasing the local effective drug concentration. To overcome this drawback, a dissolvable and layered hyaluronate (HA) microneedle (MN) patch loading recombinant bovine basic fibroblast growth factor (rbFGF) and cetylpyridinium chloride (CPC) was prepared and used for the treatment of RAU. The MN patch was prepared via a two-step casting method and subsequently characterized using fluorescence microscopy, scanning electron microscopy (SEM), and stereomicroscopy. Moreover, its mechanical properties, penetrating ability, dissolution behavior, antibacterial performance, cytotoxicity as well as ulcer healing efficacy on the rat model were determined. The results revealed that the prepared MNs possessed uniformly arranged conical needles with the drug centrally distributed in the tips. The rbFGF/CPC-loaded HA MN patch presented favorable mechanical, dissolving, and antibacterial properties and biocompatibility, increasing the growth and proliferation of fibroblasts. More importantly, this MN patch showed a higher healing effect in promoting the healing of chemical-induced RAU within five days than the control group and the conventional treatment group. The novel MN patch will have potential application prospects in the treatment of oral ulcer. [ABSTRACT FROM AUTHOR]

The pollution of the surrounding soil can be caused by the development of heavy metal mining areas, which can do a significant harm to the ecological environment and human health. The classification of soil in different mining areas is of great significance for soil management and environmental pollution control. Due to the complex physical property and composition of soil, it is easily affected by matrix effects, which can directly affect the collection and processing of spectral data. It is thus challenging to accurately classify soil. In this article, laser-induced breakdown spectroscopy (LIBS) combined with a convolutional neural network with a two-dimensional (2D-CNN) algorithm, which was based on principal component analysis (PCA), was used to accurately identify and classify standard soil samples from ten different mining areas. The sample data were first standardized and dimensionlessly pre-processed by PCA to remove redundant information and to improve the signal-to-noise ratio (SNR) of the data. Then, the structure of 2D-CNN was designed, and the features of the input data were deeply extracted twice by convolution and pooling operations, so that more feature information could be extracted from the data, which was conducive to improving the classification accuracy of the model. The experiment results show that the classification accuracy of the 2D-CNN test set could reach 99.60% and the loss could be minimized at 0.012. In order to verify the superior performance of the 2D-CNN-based PCA classification model, the 2D-CNN algorithm was compared with other models, including back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). The results show that 2D-CNN has great potential in the field of soil recognition and classification, and provides a new and reliable data processing method for LIBS to classify materials with similar chemical properties. [ABSTRACT FROM AUTHOR]