Establishment and validation of a prognostic scoring model based on disulfidptosis-related long non-coding RNAs in stomach adenocarcinoma.

Background: Stomach adenocarcinoma (STAD), a frequently occurring gastrointestinal tumour, is often detected late and has a poor prognosis. Long non-coding RNAs (lncRNAs) significantly affect tumour development. Recent studies have identified disulfidptosis as a previously unexplained form of cell death. Herein, we aimed to examine the predictive value of disulfidptosis-related lncRNA models for the clinical prognosis and immunotherapy of STAD. Methods: STAD-related transcriptomic data were obtained from The Cancer Genome Atlas (TCGA), whereas genes associated with disulfidptosis were identified from previously published papers. A risk prediction model for disulfidptosis-related lncRNAs was developed using the Cox regression and least absolute shrinkage selection algorithm methods. The accuracy of the model was confirmed using calibration curves, and the biological functions were analysed using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). Finally, the tumour mutation burden (TMB) and tumour immune dysfunction and exclusion (TIDE) algorithms were used to screen drugs that are sensitive to STAD. Results: The risk prediction models were constructed using seven disulfidptosis-related lncRNAs. The validated results were consistent with the predicted ones, with significant survival differences. When combined with clinical data, the risk scores were used as independent prognostic markers. Based on the tumour mutation load, the high-risk patient group had a poorer survival rate as compared with the low-risk patient group. Further studies were conducted to understand the different groups' inconsistent responses to immune status; subsequently, relatively sensitive drugs were identified. Conclusions: Overall, seven markers of disulfidptosis-related lncRNAs associated with STAD were found to facilitate prognostic prediction, suggesting new ideas for immunotherapy and clinical applications.

Heteroatom-doped carbon materials with interconnected channels as ultrastable anodes for lithium/sodium ion batteries.

Carbon materials have been extensively investigated as promising negative electrode materials for lithium/sodium ion batteries. However, most common carbon materials always suffer from limitations in regards to high reversible capacity and long-term cycling stability because of their low theoretical specific capacities and sluggish kinetics. Herein, we report a facile MOF-derived strategy for the synthesis of nitrogen/oxygen co-doped porous carbon polyhedra (NOPCP) with abundant channel-connected cavities with their inner surface decorated with a large number of N and O atoms, which can provide a large number of active sites (defects and edge doping sites) for the sorption of Li+/Na+. These cavities can also be considered as "ponds" where the electrolyte is stored, which shortens the diffusion distance of ions during the discharge/charge process. When evaluated as an anode material for LIBs, NOPCP-600 delivers a high reversible capacity of 1663 mA h g-1 at 0.1 A g-1 after 120 cycles and superior cycling stability with a capacity of 667 mA h g-1 after 1000 cycles at 2 A g-1. For SIBs, NOPCP-600 delivers a high reversible capacity of 313 mA h g-1 at 0.1 A g-1 after 100 cycles and an excellent long-term cycling stability of 228 mA h g-1 at 1 A g-1 after 2000 cycles.

Exploring and quantifying the relationship between instantaneous wind speed and turbidity in a large shallow lake: case study of Lake Taihu in China.

Sediment resuspension is critical to the internal nutrient loading in aquatic systems. Turbidity is commonly used as an indicator for sediment resuspension and is proved to be highly correlated to wind speed in large shallow lakes. A field observation of wind speed and turbidity was conducted using a portable weather station and a YSI 6600V2-2, and an observation lasting for 39 days was evaluated in this study (the data points with wind speed > 4 m/s account for 75%). The daily average values (DA dataset) as well as daily maximum (MX dataset) and minimum values (MI dataset) were calculated from the instantaneous observations (IN dataset). Correlations in IN dataset were deduced based on machine learning methods and were compared to those obtained from DA, MI, and MX datasets. Furthermore, the correlation in IN dataset was analyzed by using two statistical methods, and from the view of statistical the turbidity is regarded as a variable. Results indicate that the correlations in IN datasets follow the exponential function or power function pattern with a critical wind speed of 6 m/s, Regression on IN dataset revealed that linear regression model had the best performance on predicting the turbidity in test dataset and no significant differences are observed between exponential function and power function pattern. Correlations in DA and MX datasets exhibit higher maximal information coefficient (MIC) than IN dataset and error of turbidity prediction introduced by using these correlations in IN dataset is within the tolerance level. Statistical analysis on the IN dataset shows that a strong relationship exists among the wind speed and expectation of turbidity with a MIC over 0.99, and follows the exponential function or the power function as well with a different critical wind speed of 4 m/s. Over 95% data points fall in the predicted intervals of turbidity for both methods, suggesting a high predicting accuracy.

Synergistic Tuning of the Electronic Structure of Mo2C by P and Ni Codoping for Optimizing Electrocatalytic Hydrogen Evolution.

Developing earth-abundant and highly efficient nonprecious metal catalysts for hydrogen evolution reaction (HER) is critical for the storage and conversion of renewable energy sources. Molybdenum carbide (Mo2C) has been extensively investigated as one of the most promising nonprecious electrocatalysts for boosting HER because of its low cost, high electrical conductivity, good chemical structure, and similar electronic structure to that of Pt. However, Mo2C always exhibits the negative hydrogen-binding energy, which can largely prevent adsorbed H desorption during the HER process. Herein, we report P- and Ni-dual-doped Mo2C in porous nitrogen-doped carbon (P/Ni-Mo2C) as an electrocatalyst for the HER, exhibiting excellent activity and durability with a low overpotential of 165 mV at 10 mA cm-2 in alkaline electrolyte. Density functional theory (DFT) calculations proved that P and Ni acted as the anion and cation, respectively, to synergistically tune the electronic properties of Mo2C to decrease the negative hydrogen-binding energy, endowing the catalyst with excellent catalytic performance for the HER.

NiRu nanoparticles encapsulated in a nitrogen-doped carbon matrix as a highly efficient electrocatalyst for the hydrogen evolution reaction.

The design and fabrication of low-cost, efficient, and robust electrocatalysts for the hydrogen evolution reaction (HER) is of great importance in accelerating the development of water electrolysis technology. Herein, NiRu alloy nanostructures embedded in a nitrogen-doped carbon matrix (NiRu@NC) have been fabricated through a facile metal-organic framework-derived (MOF-derived) strategy. Benefiting from their advantages in the unique structures and components, the resulting NiRu@NC possesses excellent activity and durability towards the HER, which exhibits low overpotentials of 85 and 53 mV at a current density of 10 mA cm-2 in acidic and alkaline electrolytes, respectively. Furthermore, NiRu2@NC-600 also exhibits excellent hydrogen oxidation reaction (HOR) activity in an alkaline electrolyte. Therefore, this work provides a facile MOF-derived strategy for the design and synthesis of low-cost and efficient electrocatalysts for the HER.

Porous CuO@C composite as high-performance anode materials for lithium-ion batteries.

Though carbon matrices could effectively improve the electrical conductivity and accommodate the volume expansion of CuO-based anode materials for lithium ion batteries (LIBs), achieving an optimized utilization ratio of the active CuO component remains a big challenge. In this work, we developed a metal-organic framework (MOF)-derived strategy to synthesize ultrafine CuO nanoparticles embedded in a porous carbon matrix (CuO@C). Benefiting from its unique structure, the resulting CuO@C exhibits a high reversible capacity of 1024 mA h g-1 at 100 mA g-1 after 100 cycles and a long-term cycling stability with a reversible capacity of 613 mA h g-1 at 500 mA g-1 over 700 cycles. The outstanding Li-storage performances can be attributed to its porous carbon matrix and ultrafine CuO nanoparticles with more exposed active sites for electrochemical reactions.

Synergistic modification of bentonite by acid activation and hydroxyl iron pillaring for enhanced dye adsorption capacity.

Despite the fact of natural abundance, low cost and environmental friendliness, the far-from-sufficient adsorption capacity of natural bentonite (BT) has limited such a promising application to remove dye pollutants. In this paper, we proposed a facile modification strategy to enhance adsorption performance of bentonite utilizing synergistic acid activation and hydroxyl iron pillaring, by which the adsorbent (abbreviated as S-Fe-BT) exhibited the highest adsorption capacity (246.06 mg/g) and a high rapid adsorption rate for a typical organic dye, Rhodamine B (RhB). This could be ascribed to the increased interlayer spacing, the increased specific surface area, and the optimized OH/Fe ratio after the synthetic modification of the pristine BT. The adsorption behavior of RhB onto S-Fe-BT was well described by the pseudo-second-order kinetic model, indicating a chemical-adsorption-controlled process. Furthermore, its adsorption isotherm matched well with the Langmuir model due to a monolayer adsorption process. This paper opens a promising direction to remove the dye pollution using low cost bentonite adsorbents treated by such a convenient modification strategy.

MOF-derived hollow NiCo2O4 nanowires as stable Li-ion battery anodes.

Although binary metal oxides with high theoretical specific capacities and power densities are widely investigated as promising anode materials for lithium-ion batteries (LIBs), their poor cycling stability and huge volume expansion largely limit their extensive application in practical electrode materials. Herein, we report a facile strategy to synthesize hollow NiCo2O4 nanowires through direct calcination of binary metal-organic frameworks (MOFs) in air. When evaluated as an anode material for LIBs, NiCo2O4 nanowires deliver a reversible capacity of 1310 mA h g-1 at a current density of 100 mA g-1 after 100 cycles. Even at a high current density of 1 A g-1, NiCo2O4 nanowires exhibit long-term cycling stability with a capacity of 720 mA h g-1 after 1000 cycles. The outstanding lithium-storage performance can be attributed to the unique structures with 1D porous channels, which are beneficial for the fast transfer of Li+ ions and electrolyte and alleviate the strain caused by the volume expansion during cycling processes.