Oxygen vacancy-rich nickel oxide nanoplatforms for enhanced photothermal and chemodynamic therapy combat methicillin-resistant Staphylococcus aureus.

Bacterial infections pose a global concern due to high fatality rates, particularly with the rise of drug-resistant bacteria and biofilm formation. There is an urgent need for innovative strategies to combat this issue. A study on chemodynamic therapy (CDT) using nanozymes in conjunction with photothermal therapy (PTT) has displayed potential in addressing drug-resistant bacterial infections. However, the effectiveness of this combined approach is limited by inadequate light absorption. This work suggests the NiOx nanoparticles enriched with oxygen vacancies enhance CDT and PTT to overcome this challenge. The presence of oxygen vacancies in NiOx can reduce the energy gap between its valence band and conduction band, facilitating oxygen adsorption. NiOx has exhibited notable antibacterial properties and complete eradication of biofilms in both laboratory and animal trials. In animal abscess models, NiOx demonstrated antibacterial and anti-inflammatory effects in the initial stages, while also promoting wound healing and tissue regeneration by influencing immune factors and encouraging collagen deposition and neovascularization. With positive biosafety and biocompatibility profiles, the oxygen vacancy-enhanced CDT and PTT therapy proposed in this article hold promise for effective sterilization, deep biofilm removal, and treatment of infections caused by drug-resistant bacteria. STATEMENT OF SIGNIFICANCE: This study constructs oxygen vacancies NiOx nanoparticles (NiOx NPs) to improve the efficacy of photothermal therapy and chemodynamic therapy. The presence of oxygen vacancies in NiOx NPs helps bridge the energy gap between its valence band and conduction band, facilitating oxygen adsorption and improving catalytic efficiency. In both in vivo and in vitro antibacterial experiments, NiOx NPs demonstrate effective antibacterial and anti-inflammatory properties. Furthermore, it aids in wound healing and tissue regeneration by modulating immune factors, collagen deposition, and angiogenesis. This approach presents a promising collaborative strategy for utilizing nickel-based defective nanomaterials in combating deep drug-resistant bacterial infections.

Integrative analysis and risk model construction for super­enhancer­related immune genes in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer associated with poor prognosis, and accounts for the majority of RCC-related deaths. The lack of comprehensive diagnostic and prognostic biomarkers has limited further understanding of the pathophysiology of ccRCC. Super-enhancers (SEs) are congregated enhancer clusters that have a key role in tumor processes such as epithelial-mesenchymal transition, metabolic reprogramming, immune escape and resistance to apoptosis. RCC may also be immunogenic and sensitive to immunotherapy. In the present study, an Arraystar human SE-long non-coding RNA (lncRNA) microarray was first employed to profile the differentially expressed SE-lncRNAs and mRNAs in 5 paired ccRCC and peritumoral tissues and to identify SE-related genes. The overlap of these genes with immune genes was then determined to identify SE-related immune genes. A model for predicting clinical prognosis and response to immunotherapy was built following the comprehensive analysis of a ccRCC gene expression dataset from The Cancer Genome Atlas (TCGA) database. The patients from TCGA were divided into high- and low-risk groups based on the median score derived from the risk model, and the Kaplan-Meier survival analysis showed that the low-risk group had a higher survival probability. In addition, according to the receiver operating characteristic curve analysis, the risk model had more advantages than other clinical factors in predicting the overall survival (OS) rate of patients with ccRCC. Using this model, it was demonstrated that the high-risk group had a more robust immune response. Furthermore, 61 potential drugs with half-maximal inhibitory concentration values that differed significantly between the two patient groups were screened to investigate potential drug treatment of ccRCC. In summary, the present study provided a novel index for predicting the survival probability of patients with ccRCC and may provide some insights into the mechanisms through which SE-related immune genes influence the diagnosis, prognosis and potential treatment drugs of ccRCC.

2D TiVCT x layered nanosheets grown on nickel foam as highly efficient electrocatalysts for the hydrogen evolution reaction.

Exploring highly efficient and durable catalysts for the hydrogen evolution reaction (HER) is crucial for the hydrogen economy and environmental protection issues. Numerous studies have now found that transition metal carbide MXenes are ideal candidates as catalysts for the hydrogen evolution reaction. However, MXenes are inclined to easily undergo lamellar structure agglomeration and stacking, which impedes their further applications. Besides, most of the extant research has focused on single transition metal carbides, and the investigation of double transition metal carbide MXenes is rather rare. In this research work, a three-dimensional (3D) TiVCT x -based conductive electrode was constructed by depositing 2D TiVCT x nanosheets on 3D network structured nickel foam (NF) to synthesize a hybrid electrode material (abbreviated as TiVCT x @NF). TiVCT x @NF exhibits efficient electrochemical properties with a low overpotential of 151 mV at 10 mA cm-2 and a small Tafel slope of 116 mV dec-1. Benefitting from the open layer structure and strong interfacial coupling effect, compared to the pristine structure, the resulting TiVCT x @NF has greatly increased active sites for the hydrogen evolution reaction (HER) and encounters less resistance for charge transfer. In addition, TiVCT x @NF exhibits better stability in long-term acidic electrolytes. This work provides a tactic to prepare three-dimensional network electrode materials and broadens the application of single transition metal carbide MXenes as water splitting electrodes in the HER, which is beneficial to the application of noble metal-free electrocatalysts.

Robotic Manipulation Planning for Automatic Peeling of Glass Substrate Based on Online Learning Model Predictive Path Integral.

Autonomous planning robotic contact-rich manipulation has long been a challenging problem. Automatic peeling of glass substrates of LCD flat panel displays is a typical contact-rich manipulation task, which requires extremely high safe handling through the manipulation process. To this end of peeling glass substrates automatically, the system model is established from data and is used for the online planning of the robot motion in this paper. A simulation environment is designed to pretrain the process model with deep learning-based neural network structure to avoid expensive and time-consuming collection of real-time data. Then, an online learning algorithm is introduced to tune the pretrained model according to the real-time data from the peeling process experiments to cover the uncertainties of the real process. Finally, an Online Learning Model Predictive Path Integral (OL-MPPI) algorithm is proposed for the optimal trajectory planning of the robot. The performance of our algorithm was validated through glass substrate peeling tasks of experiments.