Interleukin-6 and granulocyte colony-stimulating factor as predictors of the prognosis of influenza-associated pneumonia.

BACKGROUND: Pneumonia is a common complication of influenza and closely related to mortality in influenza patients. The present study examines cytokines as predictors of the prognosis of influenza-associated pneumonia. METHODS: This study included 101 inpatients with influenza (64 pneumonia and 37 non-pneumonia patients). 48 cytokines were detected in the serum samples of the patients and the clinical characteristics were analyzed. The correlation between them was analyzed to identify predictive biomarkers for the prognosis of influenza-associated pneumonia. RESULTS: Seventeen patients had poor prognosis and developed pneumonia. Among patients with influenza-associated pneumonia, the levels of 8 cytokines were significantly higher in those who had a poor prognosis: interleukin-6 (IL-6), interferon-γ (IFN-γ), granulocyte colony-stimulating factor (G-CSF), monocyte colony-stimulating factor (M-CSF), monocyte chemoattractant protein-1 (MCP-1), monocyte chemoattractant protein-3, Interleukin-2 receptor subunit alpha and Hepatocyte growth factor. Correlation analysis showed that the IL-6, G-CSF, M-CSF, IFN-γ, and MCP-1 levels had positive correlations with the severity of pneumonia. IL-6 and G-CSF showed a strong and positive correlation with poor prognosis in influenza-associated pneumonia patients. The combined effect of the two cytokines resulted in the largest area (0.926) under the receiver-operating characteristic curve. CONCLUSION: The results indicate that the probability of poor prognosis in influenza patients with pneumonia is significantly increased. IL-6, G-CSF, M-CSF, IFN-γ, and MCP-1 levels had a positive correlation with the severity of pneumonia. Importantly, IL-6 and G-CSF were identified as significant predictors of the severity of influenza-associated pneumonia.

Establishment and Evaluation of a Noninvasive Metabolism-Related Fatty Liver Screening and Dynamic Monitoring Model: Cross-Sectional Study.

BACKGROUND: Metabolically associated fatty liver disease (MAFLD) insidiously affects people's health, and many models have been proposed for the evaluation of liver fibrosis. However, there is still a lack of noninvasive and sensitive models to screen MAFLD in high-risk populations. OBJECTIVE: The purpose of this study was to explore a new method for early screening of the public and establish a home-based tool for regular self-assessment and monitoring of MAFLD. METHODS: In this cross-sectional study, there were 1758 eligible participants in the training set and 200 eligible participants in the testing set. Routine blood, blood biochemistry, and FibroScan tests were performed, and body composition was analyzed using a body composition instrument. Additionally, we recorded multiple factors including disease-related risk factors, the Forns index score, the hepatic steatosis index (HSI), the triglyceride glucose index, total body water (TBW), body fat mass (BFM), visceral fat area, waist-height ratio (WHtR), and basal metabolic rate. Binary logistic regression analysis was performed to explore the potential anthropometric indicators that have a predictive ability to screen for MAFLD. A new model, named the MAFLD Screening Index (MFSI), was established using binary logistic regression analysis, and BFM, WHtR, and TBW were included. A simple rating table, named the MAFLD Rating Table (MRT), was also established using these indicators. RESULTS: The performance of the HSI (area under the curve [AUC]=0.873, specificity=76.8%, sensitivity=81.4%), WHtR (AUC=0.866, specificity=79.8%, sensitivity=80.8%), and BFM (AUC=0.842, specificity=76.9%, sensitivity=76.2%) in discriminating between the MAFLD group and non-fatty liver group was evaluated (P<.001). The AUC of the combined model including WHtR, HSI, and BFM values was 0.900 (specificity=81.8%, sensitivity=85.6%; P<.001). The MFSI was established based on better performance at screening MAFLD patients in the training set (AUC=0.896, specificity=83.8%, sensitivity=82.1%) and was confirmed in the testing set (AUC=0.917, specificity=89.8%, sensitivity=84.4%; P<.001). CONCLUSIONS: The novel MFSI model was built using WHtR, BFM, and TBW to screen for early MAFLD. These body parameters can be easily obtained using a body fat scale at home, and the mobile device software can record specific values and perform calculations. MFSI had better performance than other models for early MAFLD screening. The new model showed strong power and stability and shows promise in the area of MAFLD detection and self-assessment. The MRT was a practical tool to assess disease alterations in real time.

Epigallocatechin-3-Gallate Improves Intestinal Gut Microbiota Homeostasis and Ameliorates Clostridioides difficile Infection.

Clostridioides difficile infection is closely related to the intestinal flora disorders induced by antibiotics, and changes in the intestinal flora may cause the occurrence and development of Clostridioides difficile infection. Epigallocatechin-3-gallate (EGCG) is one of the major bioactive ingredients of green tea and has been suggested to alleviate the growth of C. difficile in vitro. EGCG can ameliorate several diseases, such as obesity, by regulating the gut microbiota. However, whether EGCG can attenuate C. difficile infection by improving the gut microbiota is unknown. After establishing a mouse model of C. difficile infection, mice were administered EGCG (25 or 50 mg/kg/day) or PBS intragastrically for 2 weeks to assess the benefits of EGCG. Colonic pathology, inflammation, the intestinal barrier, gut microbiota composition, metabolomics, and the transcriptome were evaluated in the different groups. Compared with those of the mice in the CDI group, EGCG improved survival rates after infection, improved inflammatory markers, and restored the damage to the intestinal barrier. Furthermore, EGCG could improve the intestinal microbial community caused by C. difficile infection, such as by reducing the relative abundance of Enterococcaceae and Enterobacteriaceae. Moreover, EGCG can increase short-chain fatty acids, improve amino acid metabolism, and downregulate pathways related to intestinal inflammation. EGCG alters the microbiota and alleviates C. difficile infection, which provides new insights into potential therapies.

Short- and Long-Term Effects of Different Antibiotics on the Gut Microbiota and Cytokines Level in Mice.

Background: Antibiotics are the first line of treatment for infectious diseases. However, their overuse can increase the spread of drug-resistant bacteria. The present study analyzed the impact of different types of antibiotics on the gut microbiome and cytokines level of mice. Methods: A total of five groups of 8-week-old male BALB/c mice (n = 35) were treated with piperacillin-tazobactam (TZP), ceftriaxone (CRO), tigecycline (TGC), levofloxacin (LEV) or normal saline (Ctrl), respectively, for up to 4 weeks. Fecal samples were analyzed by bacterial 16S rRNA gene sequencing for bacterial identification. Blood samples were used for the determination of 23 serum cytokines using multiplex immunoassay. Results: Exposure to antibiotics was shown to affect the normal weight gain of mice. Significant changes in gut composition caused by TZP, CRO and TGC treatment included the decreased abundance of Bacteroidetes (p < 0.01), Muribaculaceae (p < 0.01) and Lachnospiraceae (p < 0.01), and the increased abundance of Proteobacteria (p < 0.05), Enterobacteriaceae (including Klebsiella and Enterobacter) (p < 0.01) and Enterococcaceae (including Enterococcus) (p < 0.01). After 4-week treatment, the TZP, CRO and LEV groups had significantly lower concentrations of several serum cytokines. Correlation analysis of the top 30 bacterial genera and cytokines showed that Enterococcus and Klebsiella were strongly positively correlated with tumor necrosis factor-α (TNF-α), interleukins (IL) IL-12p70 and IL-1ß. Desulfovibrio, Candidatus Saccharimonas, norank_f__norank_o__Clostridia_UCG-014, Lactobacillus, and Roseburia were strongly negatively correlated with these cytokines. Conclusion: This study demonstrates the effects of various antibiotics on the intestinal microflora and immune status of mice. Compared with TZP, CRO and TGC, LEV had minimal impact on the gut microbiota. In addition to TGC, long-term TZP, CRO and LEV intervention can lead to a decrease in serum cytokine levels, which may depend on the intestinal microflora, antibiotic used and the duration of treatment.