Aim: To investigate the potential microbiome profile of a mouse model with heart failure (HF) during dapagliflozin treatment. Method: An HF model was constructed in 8-week-old male mice, and cardiac tissues were analyzed using histological staining. Hemodynamic indexes were measured, and fecal samples were collected for 16S rDNA sequencing. Chao1, Shannon, and Simpson were used for α-diversity analysis. b-Diversity analysis was conducted using principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) based on the Bray–Curtis distance. Linear discriminant analysis coupled with effect size measurements (LEfSe) was used to identify signature gut microbiota, and phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) was used to predict the function of altered gut microbiota. Result: Dapagliflozin treatment reduced inflammation, infarction area, and cardiac fibrosis in HF mice. It also increased endothelial-dependent dilation and inflammation in mice with HF. Dapagliflozin decreased the ratio of Firmicutes/Bacteroidetes, which was increased in HF mice. There was no significant statistical difference in α-diversity among the control, HF, and HF+dapagliflozin groups. Desulfovibrio, AF12, and Paraprevotella were enriched in HF+dapagliflozin, while Rikenella and Mucispirillum were enriched in HF based on LEfSe. KEGG analysis revealed that altered gut microbiota was associated with fermentation, amino acid biosynthesis, nucleoside and nucleotide biosynthesis/degradation, fatty acid and lipid biosynthesis, carbohydrate biosynthesis/degradation, and cofactor/prosthetic group/electron carrier/vitamin biosynthesis. Conclusion: Understanding the microbiome profile helps elucidate the mechanism of dapagliflozin for HF. The signature genera identified in this study could be used as a convenient method to distinguish between HF patients and healthy individuals.

Background: Non-small cell lung cancer (NSCLC) has high incidence and mortality rates. The discovery of an effective biomarker for predicting prognosis and treatment response in patients with NSCLC is of great significance. Bacterial lipopolysaccharide-related genes (LRGs) play a critical role in tumor development and the formation of an immunosuppressive microenvironment; however, their relevance in NSCLC prognosis and immune features is yet to be discovered.Methods: Differentially expressed LRGs associated with NSCLC prognosis were identified in the TCGA dataset. Prognostic LRG scoring and nomogram models were established using single-variable Cox regression, Least Absolute Shrinkage, and Selection Operator (LASSO) regression. The prognostic value of the scoring and nomogram models was evaluated using Kaplan-Meier (KM) analysis and further validated using an external dataset. Patients were stratified into high- and low-risk groups based on the nomogram score, and drug sensitivity analysis was performed. Additionally, clinical characteristics, mutation features, immune infiltration characteristics, and responses to immunotherapy were compared between the two groups.Results: We identified 15 differentially expressed LRGs associated with NSCLC prognosis. A prognostic prediction model consisting of 6 genes (VIPR1, NEK2, HMGA1, FERMT1, SLC7A, and TNS4) was established. Higher LRG scores were associated with worse clinical prognosis and were independent prognostic factors for NSCLC. Subsequently, a clinical risk prediction nomogram model for NSCLC was constructed, incorporating the status of patients with tumor burden, tumor T-stage, and LRG scores. The nomogram model demonstrated good predictive performance upon validation. Additionally, NSCLC patients classified as high risk based on the model’s predictions exhibited not only a poorer prognosis but also a more pronounced inflammatory immune microenvironment phenotype than low-risk patients. Furthermore, high-risk patients showed disparate predicted responses to various drugs and immunotherapies compared with low-risk patients.Conclusion: The LRGs scoring model can serve as a biomarker that contributes to the establishment of a reliable prognostic risk-prediction model, potentially facilitating the development of personalized treatment strategies for patients with NSCLC.

Background. Critical illness in the intensive care unit (ICU) has been a global health priority. Systemic nutritional status has turned out to be related to the prognosis of critically ill patients. The albumin-globulin ratio (AGR) has been reported to be a novel prognostic factor of many diseases. This study is aimed at investigating whether the AGR could predict the mortality risk in critically ill patients. Methods. We enrolled 582 adult patients admitted to the respiratory intensive care unit (RICU). We collected the clinical and laboratory data. X-tile software was used to determine the optimal cut-off values for the AGR. Patients were divided into three groups according to the AGR (low AGR group with , medium AGR group with AGR ranging from 0.8 to 1.1, and high AGR group with ). Kaplan-Meier analysis was used for survival analysis. A Cox proportional hazard model was applied to the univariate and multivariate analyses for the potential predictors associated with survival. Results. Our present study showed that the AGR was related to the 28-day survival of critically ill patients in the RICU. The rate of pneumonia in the low AGR group was significantly higher than that in the other groups. Patients with a lower AGR present an increased risk of 28-day mortality compared to patients with a higher AGR. Cox regression analysis showed that the AGR might be an independent predictor of prognosis to 28-day survival in critically ill patients in the RICU. Medium and high AGR values remained independently associated with better 28-day survival than low AGR values (HR: 0.484 (0.263-0.892) ( ); HR: 0.332 (0.166-0.665) ( )) . Conclusion. The AGR might be an independent predictor of prognosis in critically ill patients.

Diffuse alveolar hemorrhage (DAH) secondary to anti-neutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV) often results in severe respiratory failure which requires emergent management. In patients who are resistant to traditional mechanical respiratory support, extracorporeal membrane oxygenation (ECMO) can be used to maintain gas exchange, thereby providing time for the administration of immunosuppressive therapy to control the inflammation. Herein, we report the application of ECMO to support an adult patient with AAV complicated by severe respiratory failure due to DAH. Similar cases in the literature were identified and discussed. The patient in our case study was successfully treated with ECMO in the acute phase and relieved by immunosuppressive therapy after withdrawal of ECMO. A search in the PubMed database revealed 32 similar cases with DAH, of which 11 cases were microscopic polyangiitis (MPA), 2 cases were eosinophilic granulomatosis with polyangiitis (EGPA), and 19 cases were granulomatosis with polyangiitis (GPA). These patients were all treated with ECMO. Therefore, to date, we identified 33 patients who were effectively treated with ECMO, including 13 (39.4%) males and 20 (60.6%) females, with a ratio of 1:1.54. The average age was 32.4±17.5 and 36.0±16.1 years for males and females, respectively (t=0.610, P=0.547). Most patients received ECMO on the first day of admission to the intensive care unit (ICU) and it appeared that early initiation of ECMO was associated with a shorter duration of ECMO. In general, complications of ECMO in these patients were mild and were not often seen in the clinical setting. This study suggested that early recognition of respiratory failure and referral for ECMO are vital to achieve a satisfactory outcome in AAV patients with DAH.

Background: Some patients discharged automatically are classified as terminal discharge, while their clinical outcome is survival, disrupting the results of clinical research. Methods: The data of this study were taken from inpatients admitted to the ICU of the First Medical Center of the People's Liberation Army General Hospital, Beijing, China from 2008-2017. We collected the data regarding medications used over the three days before discharge from the group of patients who survived and the group of patients who died, and the outcomes of all patients were recalculated by three classification algorithms (AdaBoosting, Pearson correlation coefficient, observed to expected ratio-weighted cosine similarity). Our basic assumption is that if the classification result is death but the actual in-hospital outcome is survival, the associated patient was likely terminally discharged. Results: The coincidence rate of the outcomes calculated by the AdaBoosting algorithm was 98.1%, the coincidence rate calculated by the Pearson correlation coefficient was 61.1%, and the coincidence rate calculated by the observed to expected ratio-weighted cosine similarity was 93.4%. When the three classification methods were combined, the accuracy reached 98.56%. Conclusion: The combination of clinical rules and classification methods has a synergistic effect on judgments of patients’ discharge outcomes, greatly saving time on manual retrieval and reducing the negative influence of statistics or rules.