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Kang Wang

Publications and source records attributed to Kang Wang.

2 recordsLinked to original sources

CeOx-Induced Spatial and Electronic Modulation for General Direct Oxo Coupling in Transition Metal Hydroxides.

Electrochemical water splitting has emerged as a sustainable paradigm for hydrogen generation, where sluggish kinetics of the oxygen evolution reaction (OER) catalyzed by transition metal-based materials remain the critical bottleneck. Herein, we present a strategy that anchors CeOx nanoparticles (∼2 nm) onto two-dimensional Ni(OH)2 nanosheets, enabling dual modulation of spatial configuration and electronic states to accelerate O-O coupling. Spatially, interfacial lattice distortion between CeOx and Ni(OH)2 optimizes Ni-Ni dual-metal sites with reduced interatomic spacing. Electronically, dynamic modulation through reversible Ce3+/Ce4+ redox cycling positions Ce as an electronic regulation hub, stabilizing Ni species at the catalytically favorable +3 oxidation state through Ce─O─Ni interactions. This synergistic effect shifts the pathway from adsorbate evolution mechanism (AEM) to oxide pathway mechanism (OPM). The prepared CeOx@Ni(OH)2 achieves an overpotential of 152 mV at 10 mA cm-2 and operates continuously over 2000 h with limited performance decay. When integrated into an alkaline anion exchange membrane water electrolyzer (AEMWE), it requires 1.91 V to attain 1 A cm-2 and maintains stable operation for 450 h. This OPM activation strategy shows potential applicability across CeOx-loaded transition metal hydroxides, including Ni(OH)2, Co(OH)2, NiCo, and NiFe layered double hydroxides, offering a promising approach for alkaline OER enhancement.

alkaline water oxidation

Identification and external validation of a prognostic signature based on myeloid-derived suppressor cells-related LncRNAs to evaluate survival prognosis and treatment efficacy in invasive breast carcinoma.

BACKGROUND: Originating in the hematopoietic tissue, myeloid-derived suppressor cells (MDSCs) significantly contribute to tumor-related immunological processes. However, their relationship with long noncoding RNAs (lncRNAs) and breast cancer remains incompletely understood. In this study, we introduced MDSCs-associated lncRNAs as novel prognostic biomarkers to assess outcomes in patients with invasive breast carcinoma (BRCA). METHODS: Information regarding BRCA cases, including clinical and genomic details, was obtained from the TCGA repository. Predictive indicators were discovered, and their reliability underwent thorough verification. A clinically useful nomogram was developed following application-based validation. Additional investigations encompassed functional analysis, TMB assessment, TME profiling, immunotherapy efficacy forecasting, and drug sensitivity testing along with target identification. Long non-coding RNA expression was measured using reverse transcription quantitative PCR. RESULTS: A risk stratification model incorporating eight MDSCs-related lncRNAs effectively predicted patient outcomes. Kaplan-Meier (K-M) survival analysis clearly indicated a much worse prognosis among patients classified as high-risk (p&#xa0;<&#xa0;0.001). The nomogram accurately forecasted overall survival (OS). Analysis of functional enrichment revealed that pathways associated with epithelial cells showed activity among patients at higher risk. Characterization of the tumor microenvironment showed increased immune cell presence in those classified as low-risk. Conversely, individuals with greater risk displayed higher tumor mutational burden. TIDE and IPS analyses indicated superior immunotherapy responsiveness in the low-risk BRCA subgroup. Among 47 drugs with notable IC50 variations, Ribociclib, PD173074, KU-55933, NU7441, and nutlin-3a exhibited lower IC50 values within the low-risk group, whereas Lapatinib demonstrated greater efficacy among the high-risk group. Moreover, 10 potential therapeutic agents and their targets were predicted for high-risk patients. RT-qPCR validation confirmed the robustness of the model. CONCLUSIONS: We successfully verified a new model of molecular markers of MDSCs-related lncRNAs, offering critical insights for predicting outcomes and guiding therapeutic decisions in BRCA cases.

Bioinformatics