Using Targeted Next-Generation Sequencing to Diagnose Severe Pneumonia Due to Tropheryma Whipplei and Human Metapneumovirus: A Case Report and Literature Review.

Background: In addition to the well-known Whipple's disease (WD), Tropheryma Whipplei (TW) can also lead to acute pneumonia. There is no unified consensus on the susceptible population, pathogenesis, clinical manifestations, diagnostic criteria, and treatment options for TW pneumonia. Clinical Presentation and Intervention: This is an elderly patient with multiple injuries caused by falling from a building, and was transferred to intensive care unit (ICU) for mechanical ventilation and empirical anti-infection treatment due to severe pneumonia, and then the results of targeted next-generation sequencing (tNGS) in patient's bronchoalveolar lavage fluid (BALF) suggested TW and human metapneumovirus (HMPV) infection, and after switching to anti-infective therapy for TW, the patient was successfully extubated and transferred out of the ICU. Conclusion: This is the first case of using tNGS to diagnose severe pneumonia caused by TW and HMPV. We hope that our study can serve as a reference for the diagnosis and treatment of related cases in the future.

Identification of key genes with prognostic value in gastric cancer by bioinformatics analysis.

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality. It is urgently required to identify genes to elucidate the underlying molecular mechanisms. The aim of this study is to identify the key genes which may affect the prognosis of GC patients and be a therapeutic strategy for GC patients by bioinformatic analysis. Methods: The significant prognostic differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. The protein-protein interaction (PPI) network was established by STRING and screening key genes by MCODE and CytoNCA plug-ins in Cytoscape. Functional enrichment analysis, construction of a prognostic risk model, and nomograms verify key genes as potential therapeutic targets. Results: In total, 997 genes and 805 genes were related to the prognosis of GC in the GSE84437 and TCGA datasets, respectively. We define the 128 genes shared by the two datasets as prognostic DEGs (P-DEGs). Then, the first four genes (MYLK, MYL9, LUM, and CAV1) with great node importance in the PPI network of P-DEGs were identified as key genes. Independent prognostic risk analysis found that patients with high key gene expression had a poor prognosis, excluding their age, gender, and TNM stage. GO and KEGG enrichment analyses showed that key genes may exert influence through the PI3K-Akt pathway, in which extracellular matrix organization and focal adhesion may play important roles in key genes influencing the prognosis of GC patients. Conclusion: We found that MYLK, MYL9, LUM, and CAV1 are potential and reliable prognostic key genes that affect the invasion and migration of gastric cancer.

Clinical evaluation of prone position ventilation in the treatment of acute respiratory distress syndrome induced by sepsis.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is an acute, diffuse, inflammatory lung injury. Previous studies have shown prone position ventilation (PPV) to be associated with improvement in oxygenation. However, its role in patients with ARDS caused by sepsis remains unknown. AIM: To analyze the clinical effects of PPV in patients with ARDS caused by sepsis. METHODS: One hundred and two patients with ARDS were identified and divided into a control group (n = 55) and a PPV treatment group (n = 47). Outcomes included oxygenation index, lung compliance (Cst) and platform pressure (Pplat), which were compared between the two groups after ventilation. Other outcomes included heart rate (HR), mean arterial pressure (MAP), central venous pressure (CVP), left ventricular ejection fraction (LVEF), the length of mechanical ventilation time and intensive care unit (ICU) stay, and levels of C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6) after ventilation. Finally, mortality rate was also compared between the two groups. RESULTS: On the first day after ventilation, the oxygenation index and Cst were higher and Pplat level was lower in the PPV group than in the conventional treatment group (P < 0.05). There were no significant differences in oxygenation index, Cst, and Pplat levels between the two groups on the 2nd, 4th, and 7th day after ventilation (P > 0.05). There were no significant differences in HR, MAP, CVP, LVEF, duration of mechanical ventilation and ICU stay, and the levels of CRP, PCT, and IL-6 between the two groups on the first day after ventilation (all P > 0.05). The mortality rates on days 28 and 90 in the PPV and control groups were 12.77% and 29.09%, and 25.53% and 45.45%, respectively (P < 0.05). CONCLUSION: PPV may improve respiratory mechanics indices and may also have mortality benefit in patients with ARDS caused by sepsis. Finally, PPV was not shown to cause any adverse effects on hemodynamics and inflammation indices.

Identification of an Immune-Related Prognostic Risk Model in Glioblastoma.

Background: Glioblastoma (GBM) is the most common and malignant type of brain tumor. A large number of studies have shown that the immunotherapy of tumors is effective, but the immunotherapy effect of GBM is not poor. Thus, further research on the immune-related hub genes of GBM is extremely important. Methods: The GBM highly correlated gene clusters were screened out by differential expression, mutation analysis, and weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) and proportional hazards model (COX) regressions were implemented to construct prognostic risk models. Survival, receiver operating characteristic (ROC) curve, and compound difference analyses of tumor mutation burden were used to further verify the prognostic risk model. Then, we predicted GBM patient responses to immunotherapy using the ESTIMATE algorithm, GSEA, and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Results: A total of 834 immune-related differentially expressed genes (DEGs) were identified. The five hub genes (STAT3, SEMA4F, GREM2, MDK, and SREBF1) were identified as the prognostic risk model (PRM) screened out by WGCNA and LASSO analysis of DEGs. In addition, the PRM has a significant positive correlation with immune cell infiltration of the tumor microenvironment (TME) and expression of critical immune checkpoints, indicating that the poor prognosis of patients is due to TIDE. Conclusion: We constructed the PRM composed of five hub genes, which provided a new strategy for developing tumor immunotherapy.