Crystalline-dependent surface reconstruction at low applied potential region for enhanced oxygen evolution reaction.

Alkaline water electrolysis is apreferred technology for large-scale green hydrogen production. For most active transition metal-based catalysts during anodic oxygen evolution reaction (OER), the atomic structure of the anodic catalysts' surface often undergoes reconstruction to optimize the reaction path and enhance their catalytic activity. The design and maintenance of highly active sites during this reconstruction process remain critical and challenging for most OER catalysts. In this study, we explored the effects of crystal structures in pre-catalysts on surface reconstruction at low applied potential. Through experimental observation and theoretical calculation, we found out that catalysts with specific crystal structures exhibit superior surface remodeling ability, which enables them to better adapt to the conditions of the oxygen evolution reaction and achieve efficient catalysis. The discharge process enables the formation of abundant phosphorus vacancies on the surface, which in turn affects the efficiency of the entire oxygen evolution reaction. The optimized crystal structure of the catalyst results in an increase as high as 58.5 mA/cm2 for Ni5P4, which is twice as high as that observed for Ni2P. These results provide essential theoretical foundations and technical guidance for designing more efficient catalysts for oxygen evolution reactions.

Predictive ability of traditional and novel anthropometric measurement indices for cardio-metabolic diseases in Chinese adults: China Health and Nutrition Survey (CHNS) cohort study.

BACKGROUND AND AIMS: Cardio-metabolic diseases has been shown to be strongly associated with obesity. The aim of this study was to compare the predictive value of traditional and novel anthropometric measurement indices for cardio-metabolic diseases risk and evaluate whether new indicators can provide important information in addition to traditional indicators. METHODS AND RESULTS: China Health and Nutrition Survey (CHNS) data were obtained for this study. Baseline information for healthy participants was gathered from 1997 to 2004. The incidence of cardio-metabolic diseases was collected from 2009 to 2015 for cohort analysis. The predictive ability of each index for the risk of cardio-metabolic diseases was evaluated with time-dependent ROC analysis. Body mass index (BMI) showed the greatest predictive ability for cardio-metabolic disease incidence among all traditional and novel indices (Harrell's C statistic (95% CI): 0.7386 (0.7266-0.7507) for hypertension, 0.7496 (0.7285-0.7706) for diabetes, 0.7895 (0.7593-0.8196) for stroke and 0.7581 (0.7193-0.7969) for myocardial infarction). The addition of novel indices separately into the BMI model did not improve the predictive ability. Novel anthropometric measurement indices such as a body shape index (ABSI), abdominal volume index (AVI) and triponderal mass index (TMI), had a certain prediction ability for adults with BMI <24 kg/m2 compared to those with BMI ≥24 kg/m2. CONCLUSION: No strong evidence supports novel anthropometric measurement indices were better than BMI in the prediction of cardio-metabolic diseases incidence among Chinese adults. Novel anthropometric measurement indices, mainly for abdominal obesity, may have a high predictive effect for adults with BMI <24 kg/m2.

The mechanism study of Eag1 potassium channel in gastric cancer.

Background: Heredity factors may play a vital role in gastric cancer (GC) progression. This study is aimed to explore and validate the influence and the role of Eag1 on the susceptibility to GC. Methods: The successfully constructed Ad5-Eag1-shRNA vector was transfected into GC cells [SGC-7901 and BGC-823, short hairpin RNA (shRNA) group]. Reverse transcription polymerase chain reaction (RT-PCR) and western blotting were conducted for assessment of Eag1 messenger RNA (mRNA) and protein expression levels. Cell proliferation and cell colony formation was measured by Cell Counting Kit-8 (CCK-8) assays. Flow cytometry was performed for cell cycle progression assessment. Bioinformatic analysis was analyzed for Eag1 validation with multiple public databases. Results: The expression of Eag1 was significantly down-regulated in the shRNA group in comparison with the empty vector and control groups (P<0.05). Cell proliferation rate and clone formation number were lower in the shRNA group, and a decreased cell proportion in G2-S phase and an increased proportion in G1-G0 were observed in the shRNA group (P<0.05). When transfected with Ad5-Eag1-shRNA, cyclin D1 and cyclin E protein expression were inhibited. Bioinformatic analysis showed that Eag1 expression was strongly associated with the prognosis and immune infiltration of GC. Conclusions: The Eag1 gene may affect occurrence and development of GC through regulating cyclin D1 and cyclin E expression.

Exploration and validation of hub genes in lung adenocarcinoma based on bioinformatics analysis.

Background: Genomic abnormality is a crucial factor for lung cancer development. This study used bioinformatics analysis to explore the hub genes involved in lung adenocarcinoma. Methods: The GeneCards, Comparative Toxicogenomics Database (CTD), and DISEASES databases were used to screen the genes associated with lung adenocarcinoma. The hub genes were then identified using WebGestalt. The Cancer Genome Atlas (TCGA), UALCAN, and the Human Protein Atlas (HPA) were used to validate the expression of hub genes. The predictive effects of hub genes on the risk of lung adenocarcinoma were evaluated using receiver operating characteristic (ROC) curve analysis. The Tumor-Immune System Interaction Database (TISIDB) was used to estimate the correlation between hub genes and immune infiltration. Results: A total of 21 genes were defined as common genes associated with lung adenocarcinoma, and from these, AKT1, CD44, and CDKN2A were identified as hub genes. Significant differences in the hub gene mRNA and protein expression were observed between lung adenocarcinoma samples and normal samples derived from the TCGA and UALCAN databases. The area under the ROC curve (AUC) for AKT1, CD44, and CDKN2A in predicting lung adenocarcinoma risk was 0.847, 0.880, and 0.805, respectively, with sensitivity of 89.8%, 93.2%, and 94.9%, respectively. TISIDB analysis indicated that AKT1, CD44, and CDKN2A expression had a strong relationship with immune infiltration in lung adenocarcinoma. Conclusions: These hub genes, AKT1, CD44, and CDKN2A, may represent tumor biomarkers that may contribute to the understanding, diagnosis, and treatment of lung adenocarcinoma.