It is well known that tumors have an acidic pH microenvironment and contain a high content of hydrogen peroxide (H 2 O 2 ). These features of the tumor microenvironment may provide physiochemical conditions that are suitable for selective tumor therapy and recognition. Here, for the first time, we demonstrate that a type of graphene oxide nanoparticle (N-GO) can exhibit peroxidase-like activities (i.e., can increase the levels of reactive oxygen species (ROS)) under acidic conditions and catalyze the conversion of H 2 O 2 to ROShydroxyl radicals (HO • ) in the acidic microenvironment in Hela tumors. The concentrated and highly toxic HO • can then trigger necrosis of tumor cells. In the microenvironment of normal tissues, which has a neutral pH and low levels of H 2 O 2 , N-GOs exhibit catalase-like activity (scavenge ROS) that splits H 2 O 2 into O 2 and water (H 2 O), leaving normal cells unharmed. In the recognition of tumors, an inherent redox characteristic of dopamine is that it oxidizes to form dopamine− quinine under neutral (pH 7.4) conditions, quenching the fluorescence of N-GOs; however, this characteristic has no effect on the fluorescence of N-GOs in an acidic (pH 6.0) medium. This pH-controlled response provides an active targeting strategy for the diagnostic recognition of tumor cells. Our current work demonstrates that nanocatalytic N-GOs in an acidic and high-H 2 O 2 tumor microenvironment can provide novel benefits that can reduce drug resistance, minimize side effects on normal tissues, improve antitumor efficacy, and offer good biocompatibility for tumor selective therapeutics and specific recognition.

Background Stem cell therapies have gained great attention for providing novel solutions for treatment of various injuries and diseases due to stem cells’ self-renewal, ability to differentiate into various cell types, and favorite paracrine function. Nevertheless, the low retention of transplanted stem cell still limits their clinical applications such as in wound healing in view of an induced harsh microenvironment rich in reactive oxygen species (ROS) during inflammatory reactions. Methods Herein, a novel chitosan/acellular dermal matrix (CHS/ADM) stem cell delivery system is developed, which is of great ROS scavenging activity and significantly attenuates inflammatory response. Result Under ROS microenvironment, this stem cell delivery system acts as a barrier, effectively scavenging an amount of ROS and protecting mesenchymal stem cells (MSCs) from the oxidative stress. It notably regulates intracellular ROS level in MSCs and reduces ROS-induced cellular death. Most importantly, such MSCs delivery system significantly enhances in vivo transplanted stem cell retention, promotes the vessel growth, and accelerates wound healing. Conclusions This novel delivery system, which overcomes the limitations of conventional plain collagen-based delivery system in lacking of ROS-environmental responsive mechanisms, demonstrates a great potential use in stem cell therapies in wound healing.

Runoff forecasting is one of the important non-engineering measures for flood prevention and disaster reduction. The accurate and reliable runoff forecasting mainly depends on the development of science and technology, many machine learning models have been proposed for runoff forecasting in recent years. Considering the non-linearity and real-time of hourly rainfall and runoff data. In this study, two runoff forecasting models were proposed, which were the combination of the bidirectional gated recurrent unit and backpropagation (BGRU-BP) neural network and the bidirectional long short-term memory and backpropagation (BLSTM-BP) neural network. The two models were compared with the gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional gated recurrent unit (BGRU), and bidirectional long short-term memory (BLSTM) models. The research methods were applied to simulate runoff in the Yanglou hydrological station, Northern Anhui Province, China. The results show that the bidirectional models were superior to the unidirectional model, and the backpropagation (BP) based bidirectional models were superior to the bidirectional models. The bidirectional propagation was conducive to improving the generalization ability of the model, and BP neural network could better guide the model to find the optimal nonlinear relationship. The results also show that the BGRU-BP model performs equally well as the BLSTM-BP model. The BGRU-BP model has few parameters and a short training time, so it may be the preferred method for short-term runoff forecasting.

Nanozymes are emerging as a promising strategy for the treatment of tumors. Herein, to cope with the tumor microenvironment (TME), weak acidity (pH 5.6 to 6.8) and trace amounts of overexpressed hydrogen peroxide (H2O2) (100 µM–1 mM), we report nitrogen-doped graphene nanomaterials (N-GNMs), which act as highly efficient catalytic peroxidase (POD)-mimicking nanozymes in the TME for tumor-specific treatment. N-GNMs exhibit POD catalytic properties triggered by a weakly acidic TME and convert H2O2 into highly toxic hydroxyl radicals (•OH) thus causing the death of tumor cells while in the neutral pH surroundings of normal tissues, such catalysis is restrained and leaves normal cells undamaged thereby achieving a tumor-specific treatment. N-GNMs also display a high catalytic activity and can respond to the trace endogenous H2O2 in the TME resulting in a high efficiency of tumor therapy. Our in vitro chemical and cell experiments illustrated the POD-like activity of N-GNMs and in vivo tumor model experiments confirmed the significant inhibitory effect of N-GNMs on tumor growth.

Accurate and reliable discharge estimation plays an important role in water resource management as well as downstream applications such as ecosystem conservation and flood control. Recently, data-driven machine learning (ML) techniques showed seemingly insurmountable performance in runoff forecasting and other geophysical domains, but they still need to be improved in terms of reliability and interpretability. In this study, focusing on discharge estimation and management, we developed an ML-based framework and applied it to the Huitanggou sluice hydrological station in Anhui Province, China. The framework contains two ML algorithms, the ensemble learning random forest (ELRF) and the ensemble learning gradient boosting decision tree (ELGBDT). The SHapley Additive exPlanation (SHAP) was introduced into our framework to interpret the impact of the model features. In our framework, the correlation analysis of the dataset can provide feature information for modeling, and the quartile method was utilized to solve the outlier problem of the dataset. The Bayesian optimization algorithm was adopted to optimize the hyperparameters of the ensemble ML models. The ensemble ML models are further compared with the traditional stage–discharge rating curve (SDRC) method and the single ML model. The results show that the estimation performance of the ensemble ML models is superior to that of the SDRC and the single ML model. In addition, an analysis of the discharge estimation without considering the flow state was performed. This analysis reveals that the ensemble ML models have strong adaptability. The ensemble ML models accurately estimate the discharge, with a coefficient of determination of 0.963, a root mean squared error of 31.268, and a coefficient of correlation of 0.984. Our framework can prove helpful to improve the efficiency of short-term hydrological estimation and simultaneously provide the interpretation of the impact of the hydrological features on estimation results.