Deep Eutectic Solvent-Assisted Corrosion Boosting Bulk FeCoNiCrMo High-Entropy Alloys as Highly Efficient Oxygen Evolution Reaction Catalyst.

The key to enhancing water electrolysis efficiency lies in selecting highly efficient catalysts. Currently, high-entropy alloys (HEAs) are utilized in electrocatalysis applications owing to their diverse elemental composition, disordered elemental distribution, and the high solubility of each element, endowing them with excellent catalytic performance. The experiments were conducted using isoatomic FeNiCrMo HEA as a precursor, with a high-activity three-dimensional nanoporous structure rapidly synthesized via electrochemical one-step dealloying in a choline chloride-thiourea (ChCl-TU) deep eutectic solvent (DES). The results indicate that the dealloyed Fe20Co20Ni20Cr20Mo20 HEA mainly consists of two phases: face-centered cubic and σ phases. The imbalance in the distribution of elements in these two phases leads to quite different corrosion speeds with the FCC phase being preferentially corroded. Furthermore, synergistic electron coupling between surface atoms in the three-dimensional nanoporous structure strengthens the behavior of the oxygen evolution reaction (OER). At a current density of 40 mA cm-2, the overpotential after dealloying decreased to 370 mV, demonstrating excellent stability. The technique demonstrated in this work provides a novel approach to improve the catalytic activity of OER.

Based on systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for diabetes.

Purpose: Diabetes and its complications cause a heavy burden of disease worldwide. In recent years, Mendelian randomization (MR) has been widely used to discover the pathogenesis and epidemiology of diseases, as well as to discover new therapeutic targets. Therefore, based on systematic "druggable" genomics, we aim to identify new therapeutic targets for diabetes and analyze its pathophysiological mechanisms to promote its new therapeutic strategies. Material and method: We used double sample MR to integrate the identified druggable genomics to evaluate the causal effect of quantitative trait loci (eQTLs) expressed by druggable genes in blood on type 1 and 2 diabetes (T1DM and T2DM). Repeat the study using different data sources on diabetes and its complications to verify the identified genes. Not only that, we also use Bayesian co-localization analysis to evaluate the posterior probabilities of different causal variations, shared causal variations, and co-localization probabilities to examine the possibility of genetic confounding. Finally, using diabetes markers with available genome-wide association studies data, we evaluated the causal relationship between established diabetes markers to explore possible mechanisms. Result: Overall, a total of 4,477 unique druggable genes have been gathered. After filtering using methods such as Bonferroni significance (P<1.90e-05), the MR Steiger directionality test, Bayesian co-localization analysis, and validation with different datasets, Finally, 7 potential druggable genes that may affect the results of T1DM and 7 potential druggable genes that may affect the results of T2DM were identified. Reverse MR suggests that C4B may play a bidirectional role in the pathogenesis of T1DM, and none of the other 13 target genes have a reverse causal relationship. And the 7 target genes in T2DM may each affect the biomarkers of T2DM to mediate the pathogenesis of T2DM. Conclusion: This study provides genetic evidence supporting the potential therapeutic benefits of targeting seven druggable genes, namely MAP3K13, KCNJ11, REG4, KIF11, CCNE2, PEAK1, and NRBP1, for T2DM treatment. Similarly, targeting seven druggable genes, namely ERBB3, C4B, CD69, PTPN22, IL27, ATP2A1, and LT-ß, has The potential therapeutic benefits of T1DM treatment. This will provide new ideas for the treatment of diabetes and also help to determine the priority of drug development for diabetes.

Performance evaluation on vaccination rates monitoring report system of Shenzhen, China.

To evaluate the performance of "Vaccination Rates Monitoring Report System" implemented by Shenzhen CDC, we conducted an analysis of the data quality and identify key areas for system improvement. Following evaluation guidelines provided by WHO and United States CDC, we established six evaluation attributes: representativeness, simplicity, acceptability, data reliability, stability and timeliness. In eastern, central and western regions of Shenzhen, we selected one district from each region, of which the local CDC and ten CHSCs under jurisdiction were chosen for evaluation. On-site inspections, questionnaires survey and interviews were utilized for data collection, while the Likert scale method was used for attributes rating evaluation. A total of 70 participants were surveyed, consisting of 60 CHSCs and 10 CDCs staff. The gender ratio was 1:2.5 (males to females), with the majority falling within the 25-34 age range (46%). Most participants held full-time positions (80%) and had more than 5 years of work experience (62%). The system achieved 100% coverage of all CHSCs and CDCs (100%). The cumulative percentage scores for the overall favorable options of simplicity, acceptability, data reliability, stability, and timeliness were 79%, 85%, 73%, 50%, and 71% respectively. The system operates normally with strong representativeness. Acceptability was rated as "good." Simplicity, data reliability, and system timeliness were rated as "average," while system stability was rated as "poor." Based on these survey results, developers should urgently investigate reasons for poor stability, particularly addressing concerns from CHSCs users. Additionally, the issues and shortcomings identified in other attributes should also be gradually improved.