Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma.

Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncRNAs) in the pathobiology of lung adenocarcinoma (LUAD), research into lncRNAs specifically associated with disulfidptosis in LUAD, termed disulfidptosis-related lncRNAs (DRLs), remains insufficiently explored. Using The Cancer Genome Atlas (TCGA)-LUAD dataset, we implemented ten machine learning techniques, resulting in 101 distinct model configurations. To assess the predictive accuracy of our model, we employed both the concordance index (C-index) and receiver operating characteristic (ROC) curve analyses. For a deeper understanding of the underlying biological pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) for functional enrichment analysis. Moreover, we explored differences in the tumor microenvironment between high-risk and low-risk patient cohorts. Additionally, we thoroughly assessed the prognostic value of the DRLs signatures in predicting treatment outcomes. The Kaplan-Meier (KM) survival analysis demonstrated a significant difference in overall survival (OS) between the high-risk and low-risk cohorts (p < 0.001). The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups. Further research identified variations in tumor mutational burden (TMB) and differential responses to immunotherapies and chemotherapies. Our validation, using three GEO datasets (GSE31210, GSE30219, and GSE50081), revealed that the C-index exceeded 0.67 for GSE31210 and GSE30219. Significant differences in disease-free survival (DFS) and OS were observed across all validation cohorts among different risk groups. The prognostic model offers potential as a molecular biomarker for LUAD prognosis.

Polyaniline Induced Trivalent Ni in Laser-Fabricated Nickel Oxides for Efficient Oxygen Evolution Reaction.

Although it is generally acknowledged that transition metals at high oxidation states represent superior oxygen evolution reaction (OER) activity, the preparation and stability of such a high-valence state are still a challenge, which requires relatively harsh reaction conditions and is unstable under ambient conditions. Herein, we report the formation of trivalent nickel (Ni3+) in laser-fabricated nickel oxides induced by polyaniline (PANI) under electrochemical activation via a significant charge transfer between Ni and N, as confirmed by X-ray photoelectron spectroscopy and density functional theory calculations. Thereafter, the presence of Ni3+ and the improved conductivity by PANI effectively increase the electrochemical OER activity of the samples together with excellent long-term stability. This work provides new insights for the rational manufacture of high-valence metal for electrochemical reactions.

[Impact of Rainfall-Runoff Events on Methane Emission from Xiangxi Bay of the Three Gorges Reservoir].

Methane is an important greenhouse gas and whether reservoirs act as a source or sink of methane has attracted great attention worldwide. However, unrepresentative sampling periods and a lack of consideration of unfavorable weather conditions have limited the accurate estimation of CH4 emission from reservoirs. This study focused on the middle reach of Xiangxi Bay in the Three Gorges Reservoir to track an entire rainfall-runoff event via on-site measurements in the summer of 2019, and initiatively investigated the impact of rainfall and inflow processes on methane concentration and emission. Results showed that from before to after the rainfall event, methane flux at the air-water interface ranged between 0.011 and 0.326 mg·(m2·h)-1, indicating a net source of methane to the atmosphere. Both wind velocity and rainfall affected methane evasion from the surface by altering the gas transfer velocity, with the effect of wind being more prominent. Methane concentrations at the bottom layer significantly increased when rainfall-induced density flow from the watershed arrived at the sampling section. This was likely due to methane export from upstream and along the flow path. During this event, discharge was too small to destratify the water column, and methane was strongly oxidized as it diffused upwards, having little impact on surface methane concentrations and air-water methane flux.