Biomarkers prediction and immune landscape in ulcerative colitis: Findings based on bioinformatics and machine learning.

BACKGROUND: Ulcerative colitis (UC) presents diagnostic and therapeutic difficulties. The primary objective of this study is to identify efficacious biomarkers for diagnosis and treatment, as well as acquire a deeper understanding of the immuneological characteristics associated with the disease. METHODS: Datasets relating to UC were obtained from GEO database. Among these, three datasets were merged to create a metadata for bioinformatics analysis and machine learning. Additionally, one dataset specifically utilized for external validation. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) were employed to screen signature genes. The artificial neural network (ANN) model and receiver operating characteristic (ROC) curve were used to assess the diagnostic performance of signature genes. The single sample gene set enrichment analysis (ssGSEA) was applied to reveal the immune landscape. Finally, the relationship between the signature genes, immune infiltration, and clinical characteristics was investigated through correlation analysis. RESULT: By intersecting the result of LASSO, RF and WGCNA, 8 signature genes were identified, including S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14. The biological progress of this gene mostly encompasses acute inflammatory response, aggregation and chemotaxis of leukocyte, and response to lipopolysaccharide by mediating IL-17 signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway. Immune infiltration analysis shows 25 immune cells are significantly elevated in UC samples. Moreover, these signature genes exhibit a strong correlation with various immune cells and a mild to moderate correlation with the Mayo score. CONCLUSION: S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14 have been identified as credible potential biomarkers for the diagnosis and therapy of UC. The immune response mediated by these signature biomarkers plays a crucial role in the occurrence and advancement of UC by means of the reciprocal interaction between the signature biomarkers and immune-infiltrated cells.

The Threshold Effect of Swine Epidemics on the Pig Supply in China.

The pig industry is the pillar industry of animal husbandry in China, and epidemics can lead to drastic changes in pig supply, affecting the healthy development of the pig industry and residents' quality of life. This study analyzed the mechanism of the effect of swine epidemics on nonlinear shocks to pig supply, and monthly data on pig supply from January 2012 to June 2020 were applied to study the threshold effect of swine epidemics on pig stock and slaughter in China empirically, using the index of swine epidemics' width (ISEW) as the threshold variable. The results of this study were as follows: (1) The influence of the ISEW over 7 months on pig stock in China was divided into two ranges, and the pig stock did not change significantly when the ISEW was less than 0.25. Swine epidemics had a significantly negative impact on the pig stock when the ISEW was larger than 0.25. (2) The influence of the ISEW over 8 months on pig slaughter was also divided into two ranges. When the ISEW was less than 0.33, epidemics had a positive and significant effect on pig slaughter, while epidemics had a marked negative impact on pig slaughter when the ISEW was greater than 0.33. Based on these conclusions, this study proposed relevant measures for the prevention and control of swine epidemics.