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Background:Numerous studies have reported a high incidence and risk of severe illness due to coronavirus disease 2019 (COVID-19) in patients with type 2 diabetes (T2DM). COVID-19 patients may experience elevated or decreased blood sugar levels and may even develop diabetes. However, the molecular mechanisms linking these two diseases remain unclear. This study aimed to identify the common genes and pathways between T2DM and COVID-19.Methods:Two public datasets from the Gene Expression Omnibus (GEO) database (GSE95849 and GSE164805) were analyzed to identify differentially expressed genes (DEGs) in blood between people with and without T2DM and COVID-19. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the common DEGs. A protein-protein interaction (PPI) network was constructed to identify common genes, and their diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. Validation was performed on the GSE213313 and GSE15932 datasets. A gene co-expression network was constructed using the GeneMANIA database to explore interactions among core DEGs and their co-expressed genes. Finally, a microRNA (miRNA)-transcription factor (TF)-messenger RNA (mRNA) regulatory network was constructed based on the common feature genes.Results:In the GSE95849 and GSE164805 datasets, 81 upregulated genes and 140 downregulated genes were identified. GO and KEGG enrichment analyses revealed that these DEGs were closely related to the negative regulation of phosphate metabolic processes, the positive regulation of mitotic nuclear division, T-cell co-stimulation, and lymphocyte co-stimulation. Four upregulated common genes (DHX15, USP14, COPS3, TYK2) and one downregulated common feature gene (RIOK2) were identified and showed good diagnostic accuracy for T2DM and COVID-19. The AUC values of DHX15, USP14, COPS3, TYK2, and RIOK2 in T2DM diagnosis were 0.931, 0.917, 0.986, 0.903, and 0.917, respectively. In COVID-19 diagnosis, the AUC values were 0.960, 0.860, 1.0, 0.9, and 0.90, respectively. Validation in the GSE213313 and GSE15932 datasets confirmed these results. The miRNA-TF-mRNA regulatory network showed that TYH2 was targeted by PITX1, PITX2, CRX, NFYA, SREBF1, RELB, NR1L2, and CEBP, whereas miR-124-3p regulates THK2, RIOK2, and USP14.Conclusion:We identified five common feature genes (DHX15, USP14, COPS3, TYK2, and RIOK2) and their co-regulatory pathways between T2DM and COVID-19, which may provide new insights for further molecular mechanism studies.

Abstract Background This study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients. Methods Firstly, pathway analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were managed to search for possible prognostic mechanisms attached to the OS-specific differentially expressed BMPs-related genes (DEBRGs). Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis. The receiver operating characteristic (ROC) curve weighed the model’s accuracy. Thirdly, the GEO database (GSE21257) was operated for independent validation. The nomogram was initiated using multivariable Cox regression. Immune infiltration of the OS sample was calculated. Finally, the three discovered hallmark genes’ mRNA and protein expressions were verified. Results A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses. Multivariate and univariate Cox regression analysis were devised to forge the OS risk model. Both the TARGET training and validation sets indicated that the prognostic biomarker-based risk score model performed well based on ROC curves. In high- and low-risk groups, immune cells, including memory B, activated mast, resting mast, plasma, and activated memory CD4 + T cells, and the immune, stromal, and ESTIMATE scores showed significant differences. The nomogram that predicts survival was established with good performance according to clinical features of OS patients and risk scores. Finally, the expression of three crucial BMP-related genes in OS cell lines was investigated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB). Conclusion The new BMP-related prognostic signature linked to OS can be a new tool to identify biomarkers to detect the disease early and a potential candidate to better treat OS in the future.

Fracture initiation in plastic metals is attributed to the development of voids. Analyzing the nucleation and growth processes of voids facilitates the study of plastic deformation and fracture mechanisms in metal materials. Uniaxial tensile tests were conducted on two high-quality carbon structural steels, and the microfracture surface morphology of the tensile specimens was observed by using a scanning electron microscope (SEM). From the perspective of vacancy condensation, the nucleation mechanism of voids in the absence of inclusions or particles was analyzed. Based on the continuum damage mechanics theory and the Rice–Tracy (R-T) model, a damage parameter considering the void volume fraction was derived, and a plastic potential function, hardening curve, and constitutive model for the plastic deformation process of the plastic metal material were established. Based on the uniaxial tensile test data of the two sheets of high-quality carbon steel, the strain range data in the hardening stage were converted into true stress–plastic strain data, and the established hardening curve was used to fit the true stress–plastic strain data. The results showed good agreement between the established hardening curve and the experimental results, which effectively reflected the deformation process of ductile fractures in plastic metal materials.

Abstract The goal of this study is to reveal the hub genes and molecular mechanisms of the coronavirus disease 2019 (COVID‐19) acute respiratory distress syndrome (ARDS) based on the genome‐wide RNA sequencing dataset. The RNA sequencing dataset of COVID‐19 ARDS was obtained from GSE163426. A total of 270 differentially expressed genes (DEGs) were identified between COVID‐19 ARDS and control group patients. Functional enrichment analysis of DEGs suggests that these DEGs may be involved in the following biological processes: response to cytokine, G‐protein coupled receptor activity, ionotropic glutamate receptor signalling pathway and G‐protein coupled receptor signalling pathway. By using the weighted correlation network analysis approach to analyse these DEGs, 10 hub DEGs that may play an important role in COVID‐19 ARDS were identified. A total of 67 potential COVID‐19 ARDS targetted drugs were identified by a complement map analysis. Immune cell infiltration analysis revealed that the levels of T cells CD4 naive, T cells follicular helper, macrophages M1 and eosinophils in COVID‐19 ARDS patients were significantly different from those in control group patients. In conclusion, this study identified 10 COVID‐19 ARDS‐related hub DEGs and numerous potential molecular mechanisms through a comprehensive analysis of the RNA sequencing dataset and also revealed the difference in immune cell infiltration of COVID‐19 ARDS.