BackgroundWe analyzed the factors related to AAD to inform the rational use of antibiotics in critically ill patients and to reduce the incidence of AAD by providing a reference for antibiotic use in the clinical setting.Material/MethodsThis study was a retrospective analysis of the clinical data of patients who were hospitalized in the ICU of the First Teaching Hospital of Xi’an Jiaotong University from January 1, 2015 to December 31, 2016. Patients with AAD were assigned to the case group, and all others were assigned to the control group. Basic data were collected for all the selected patients. All the relevant data were analyzed with univariate or multivariate regression analyses, and P<0.05 was considered statistical significance.ResultsA total of 293 patients were enrolled. Statistical analyses showed that gender (OR 1.915; 95% [CI] 1.061–3.455; P=0.031), parenteral nutrition (OR 1.877; 95% [CI] 1.043–3.377; P=0.036), preventive use of probiotics (OR 0.497; 95% [CI] 0.285–0.866; P=0.014), APACHE II score upon admission to the ICU (OR 0.961; 95% [CI] 0.927–0.998; P=0.037) and use of enzyme-inhibitor antibiotics (OR 1.899; 95% [CI] 1.044–3.420; P=0.016) were associated with AAD. Further subgroup analysis by gender showed that parenteral nutrition (OR 2.144; 95% [CI] 1.064–4.322; P=0.033), preventive use of probiotics (OR 0.367; 95% [CI] 0.186–0.722; P=0.004), and APACHE II score upon admission to the ICU (OR 1.055; 95% [CI] 1.011–1.101; P=0.014) were associated with AAD in critically ill male patients. Age (OR 0.975; 95% [CI] 0.951–0.999; P=0.041) and use of carbapenem antibiotics (OR 4.826; 95% [CI] 1.011–23.030; P=0.048) were associated with AAD in critically ill female patients.ConclusionsParenteral nutrition, prophylactic use of probiotics, use of enzyme-inhibitor antibiotics, and use of combinations of antibiotics were associated with AAD in critically ill patients. The prophylactic use of probiotics may be a protective factor in AAD.

Compared to other types of lung cancer, lung adenocarcinoma patients with a history of smoking have a poor prognosis during the treatment of lung cancer. How lung adenocarcinoma-related genes are differentially expressed between smoker and non-smoker patients has yet to be fully elucidated. We performed a meta-analysis of four publicly available microarray datasets related to lung adenocarcinoma tissue in patients with a history of smoking using R statistical software. The top 50 differentially expressed genes (DEGs) in smoking vs. non-smoking patients are shown using heat maps. Additionally, we conducted KEGG and GO analyses. In addition, we performed a PPI network analysis for 8 genes that were selected during a previous analysis. We identified a total of 2,932 DEGs (1,806 upregulated, 1,126 downregulated) and five genes (CDC45, CDC20, ANAPC7, CDC6, ESPL1) that may link lung adenocarcinoma to smoking history. Our study may provide new insights into the complex mechanisms of lung adenocarcinoma in smoking patients, and our novel gene expression signatures will be useful for future clinical studies.

Background Glioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice. Material and Methods Using the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS and differentially expressed between GBM tissues and the adjacent normal tissues. A robust likelihood‐based survival modeling approach was applied to select the best genes for modeling. After the prognostic nomogram was generated, an independent dataset on different platform was used to evaluate its effectiveness. Results We identified 168 differentially expressed genes associated with the OS. Five of these genes were selected to generate a gene prognostic nomogram. The external validation demonstrated that 5‐gene prognostic nomogram has the capability of predicting the OS of GBM patients. Conclusion We developed a novel and convenient prognostic tool based on five genes that exhibited clinical value in predicting the survival probability for newly diagnosed GBM patients, and all of these five genes could represent potential target genes for the treatment of GBM. The development of this model will provide a good reference for cancer researchers.

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