BackgroundMethicillin-resistant Staphylococcus aureus (MRSA) pneumonia (MP) and MRSA bloodstream infections (MRSA-BSI) are relatively often described, but MP with secondary MRSA-BSI (termed as MP-BSI) is few reported. Herein, we attempted to investigate the clinical features, risk factors and outcomes of MP-BSI in comparison with MP alone.MethodsClinical data from patients with MP was retrospectively collected. The cases were divided into groups of MP alone and MP-BSI. Determination of independent risk factors for MP-BSI relied on binomial logistic regression analysis. In addition, the outcomes were also compared.ResultsA total of 435 patients with MP were recruited, 18.9% (82/435) of whom was MP-BSI. The median age was 62 (interquartile range,51,72) years, and 74.5% were male. Multivariate analysis revealed that high SOFA score, immunosuppression, community-acquired MRSA pneumonia (CA-MP), time from initial to targeted antibiotics, accelerated respiratory rate, elevated γ-GT (all p<0.05) were independent risk factors for MP-BSI, while targeted treatment with linezolid was a protective factor. The median length of hospitalization, 28-day mortality, and in-hospital mortality among total patients were 26 days, 13.6%, and 17.0%, respectively. Patients with MP-BSI had longer length of hospitalization, higher 28-day mortality and in-hospital mortality (all p<0.05).ConclusionsSecondary MRSA-BSI among MRSA pneumonia is not uncommon. High SOFA score, immunosuppression, CA-MP, time from initial to targeted antibiotics, accelerated respiratory rate and elevated γ-GT are independent risk factors for MP with secondary MRSA-BSI; importantly, linezolid as targeted antibiotic is a protective factor. In addition, patients with MP have worse clinical outcomes when they are developed with MRSA-BSI.
Background Acute kidney injury is a common clinical problem with no sensitive and specific diagnostic biomarkers and definitive treatments. The underlying molecular mechanisms of acute kidney injury are unclear. Therefore, it is pivotal and urgent to explore the underlying mechanisms and screen for novel diagnostic biomarkers, as well as therapeutic targets. Methods The present study constructed scale-free network using WGCNA analysis. LASSO logistic regression analysis was used to explore the optimal diagnostic model of AKI. In addition, GO and KEGG pathway enrichment analysis were performed and TF-mRNA and miRNA-mRNA network analysis and immune infiltration analysis of hub genes were performed to reveal the underlying mechanisms of AKI. Results Fifteen hub genes were uncovered and constructed a diagnostic model by LASSO logistic regression analysis. GO and KEGG analysis revealed that the genes were enriched in oxidation-reduction process, cell adhesion, proliferation, migration, metabolic process, mitochondria and iron ion binding. The enriched TFs were BRD2, EP300, ETS1, MYC, SPI1 and ZNF263. The enriched miRNAs were miR-181c-5p, miR-218-5p, miR-485-5p, miR-532-5p and miR-6884-5p. The immune infiltration analysis showed that Macrophages M2 was decreasing significantly revealing a protective factor for further AKI treatment. Conclusions The present study identified fifteen hub genes, a diagnostic model, transcriptional factors, miRNAs, immune infiltration and pathways by analyzing gene expression profiles of AKI, which provides some basis for further experimental studies.
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