BackgroundSepsis is organ dysfunction due to the host’s deleterious response to infection, and the kidneys are one of the organs damaged in common sepsis. Sepsis-associated acute kidney injury (SA-AKI) increases the mortality in patients with sepsis. Although a substantial volume of research has improved the prevention and treatment of the disease, SA-SKI is still a significant clinical concern.PurposeAimed to use weighted gene co-expression network analysis (WGCNA) and immunoinfiltration analysis to study SA-AKI-related diagnostic markers and potential therapeutic targets.MethodsImmunoinfiltration analysis was performed on SA-AKI expression datasets from the Gene Expression Synthesis (GEO) database. A weighted gene co-expression network analysis (WGCNA) analysis was performed on immune invasion scores as trait data, and modules associated with immune cells of interest were identified as hub modules. Screening hub geneset in the hub module using protein-protein interaction (PPI) network analysis. The hub gene was identified as a target by intersecting with significantly different genes screened by differential expression analysis and validated using two external datasets. Finally, the correlation between the target gene, SA-AKI, and immune cells was verified experimentally.ResultsGreen modules associated with monocytes were identified using WGCNA and immune infiltration analysis. Differential expression analysis and PPI network analysis identified two hub genes (AFM and GSTA1). Further validation using additional AKI datasets GSE30718 and GSE44925 showed that AFM was significantly downregulated in AKI samples and correlated with the development of AKI. The correlation analysis of hub genes and immune cells showed that AFM was significantly associated with monocyte infiltration and hence, selected as a critical gene. In addition, Gene single-enrichment analysis (GSEA) and PPI analyses results showed that AFM was significantly related to the occurrence and development of SA-AKI.ConclusionsAFM is inversely correlated with the recruitment of monocytes and the release of various inflammatory factors in the kidneys of AKI. AFM can be a potential biomarker and therapeutic target for monocyte infiltration in sepsis-related AKI.
Under the background of the normalization of COVID-19 prevention and control and the rapid development of e-commerce, community group buying has occupied the market by providing low-priced, fast, and green consumer goods, but with it, the logistics and distribution volume of goods has also increased sharply. In order to reduce environmental pollution and the carbon emissions caused by transportation in the community group buying logistics distribution, it is necessary to investigate a suitable method to optimize vehicle distribution routes and reduce carbon emissions. Taking the lowest total costs of logistics and distribution and the smallest carbon emissions, this article introduces soft time window function and carbon emissions parameters, takes the delivery of goods from the community group buying distribution center in Wu’an Town, Hebei Province to customer points in 14 townships as an example, an optimization model for the distribution route of low carbon vehicles for community group buying based on improved genetic algorithm was constructed, AHP-EW fusion technology was used to calculate carbon emissions and cost weights, and compared with the traditional genetic algorithm and ant colony algorithm two typical heuristic algorithms, the feasibility of the proposed model and the advantages of the improved algorithm are verified, and the research results showed that it can reduce the costs and carbon emissions of vehicle distribution, provide decision-making reference for community group buying logistics enterprise distribution, and promote energy conservation and environmental sustainable development.
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