Yanan Zhou

Background: Type 1 diabetes is a chronic autoimmune disease featured by insulin deprivation caused by pancreatic β-cell loss, followed by hyperglycaemia. Objective: Currently, there is no cure for this disease in clinical treatment, and patients have to accept a lifelong injection of insulin. The exploration of potential diagnosis biomarkers through analysis of mass data by bioinformatic tools and machine learning is important for Type 1 diabetes. Methods: We collected two mRNA expression datasets of Type 1 diabetes peripheral blood samples from GEO, screened out differentially expressed genes (DEGs) by R software, conducted GO and KEGG pathway enrichment using the DEGs. And the STRING database and Cytoscape were used to build PPI network and predict hub genes. We constructed a Logistic regression model by using the hub genes to assess sample type. Results: Bioinformatic analysis of GEO dataset revealed 92 and 75 DEGs in GSE50098 and GSE9006 datasets, separately, and 10 overlapping DEGs. PPI network of these 10 DEGs showed 7 hub genes, namely EGR1, LTF, CXCL1, TNFAIP6, PGLYRP1, CHI3L1 and CAMP. We built a Logistic regression basing on these hub genes and optimized the model to 3 genes (LTF, CAMP and PGLYRP1) based Logistic model. The values of area under curve (AUC) of training set GSE50098 and testing set GSE9006 were 0.8452 and 0.8083, indicating the efficacy of this model. Conclusion: Integrated bioinformatic analysis of gene expression in Type 1 diabetes and the effective Logistic regression model built in our study may provide promising diagnostic methods for Type 1 diabetes.

This review summarizes the recent theoretical and experimental progress in two-dimensional material-based single-atom catalysts for the electrochemical OER. The remaining challenges and an outlook on future directions are highlighted.

In this paper, we grew the vanadium-doped nickel nitride porous nanosheet arrays supported on NF, V-Ni₃N/NF, which displays efficient catalytic performance for urea electrolysis with energy-saving H₂production.

The best catalyst for OER is Co@V_Bwithη^OERof 0.43 V followed by Ni@V_Cwithη^OER= 0.47 V. For the ORR process, the best catalyst is Rh@V_Bwithη^ORRof 0.40 V followed by Pd@V_Band Pd@V_C(η^ORR= 0.45 V).

The best catalyst for the OER is Ir@Pc with an η ^OER of 0.41 V followed by Rh@Pc with an η ^OER = 0.44 V. For the ORR process, the best catalyst is Rh@Pc with an η ^ORR of 0.44 V followed by Ir@Pc ( η ^ORR = 0.55 V).

Ni@C₉N₄ performs as a promising bifunctional electrocatalyst with N and Ni atoms as the catalytic active sites for HER and OER, with calculated hydrogen adsorption Gibbs free energy (ΔG_H*) of −0.04 eV and OER overpotential (η^OER) of 0.31 V.

The best catalyst for OER is Rh-V _CC with η ^OER of 0.35 V, followed by Co-V _CC (0.43 V). For ORR, Rh-V _CC exhibits the lowest η ^ORR value of 0.27 V, followed by Co-V _CC (0.42 V).