Purpose: It has been reported that approximately 40% of ALI (acute lung injury) incidence resulted from sepsis. Paclitaxel, as a classic anti-cancer drug, plays an important role in the regulation of inflammation. However, we do not know whether it has a protective effect against CLP (cecal ligation and puncture)-induced septic ALI. Our study aims to illuminate the mitigative effects of paclitaxel on sepsis-induced ALI and its relevant mechanisms. Materials and methods: The survival rates and organ injuries were used to evaluate the effects of paclitaxel on CLP mice. The levels of inflammatory cytokines were tested by ELISA. MUC1 siRNA pre-treatment was used to knockdown MUC1 expression in vitro. GO203 was used to inhibit the homodimerization of MUC1-C in vivo. The expression levels of MUC1, TLR 4 and p-NF-κB/p65 were detected by Western blot. Results: Our results showed that paclitaxel improved the survival rates and ameliorated organ injuries especially lung injury in CLP-induced septic mice. These were accompanied by reduced inflammatory cytokines in sera and BALF (bronchoalveolar lavage fluid). We also found paclitaxel could attenuate TLR 4-NF-κB/p65 activation both in lung tissues of septic mice and LPS-stimulated lung type II epithelial cell line A549. At the upstream level, paclitaxel-upregulated expression levels of MUC1 in both in vivo and in vitro experiments. The inhibitory effects of paclitaxel on TLR 4-NF-κB/p65 activation were reversed in lung tissues of septic mice pre-treated with MUC1 inhibitor and in MUC1-knockdown A549 cells. Protection of paclitaxel on sepsis-induced ALI and decrease of inflammatory cytokines were also abolished by inhibition of MUC1. Conclusion: Collectively, these results indicated paclitaxel could significantly alleviate acute lung injury in CLP-induced septic mice and LPS-stimulated lung type II epithelial cell line A549 by activating MUC1 and suppressing TLR-4/NF-κB pathway.

COVID-19 is a public health emergency that has rapidly spread to over 200 countries and regions, and no effective treatment has been established to date. Severe and critical cases have been associated with higher mortality due to acute respiratory distress syndrome (ARDS) and cytokine storm. Based on the novelty and recent emergence of COVID-19, no effective treatment regimen has been identified, thus prompting clinicians to engage in drug repurposing to address the immediate therapeutic need. This study focused on the molecular target angiotensin-converting enzyme 2 (ACE2) of SARS-CoV-2 and screened a group of ACE2 agonists by bioinformatics. Glucocorticoids are a type of ACE2 activator. We verified the efficacy of nine chemicals on regulating ACE2 expression in human GES-1, an upper digestive tract epithelial cell line, and THP-1, a human monocyte cell line, and found that several glucocorticoids imparted activating effects on ACE2 in both cell lines. The drugs triciribine and kinetin riboside activate ACE2 expression or inhibit IL-6 production in macrophages to some extent. In addition, we compared the efficacies of several glucocorticoids. Hydrocortisone showed the strongest effect on ACE2 activation, followed by prednisolone, dexamethasone, and methylprednisolone. We retrospectively analyzed the therapeutic efficacy of nine severe or critical patients from a cohort of 90 COVID-19 cases, who received medium to small doses of glucocorticoids from our integrated medical team in Wuhan. Seven out of nine patients revealed significant improvement in clinical parameters and chest CT images. This study provides experimental and clinical evidence that medium-to-low-dose glucocorticoids may play a protective role in the respiratory and digestive systems by activating ACE2 and suppressing cytokine storm.

Background: Yeasts, mostly Candida, are important causes of bloodstream infections (BSI), responsible for significant mortality and morbidity among hospitalized patients. The epidemiology and species distribution vary from different regions. The goals of this study were to report the current epidemiology of Candida BSI in a Shanghai Teaching Hospital and estimate the impact of appropriate antifungal therapy on the outcome. Methods: From January 2008 to December 2012, all consecutive patients who developed Candida BSI at Ruijin University Hospital were enrolled. Underlying diseases, clinical severity, species distribution, antifungal therapy and its impact on the outcome were analyzed. Results: A total of 121 episodes of Candida BSI were identified, with an incidence of 0.32 episodes/1,000 admissions (0.21 in 2008 and 0.42 in 2012) The proportion of candidemia caused by non-albicans species (62.8%), including C. parapsilosis (19.8%), C. tropicalis (14.9%), C. glabrata (7.4%), C. guilliermondii (5.8%), C. sake (5.0%) was higher than that of candidemia caused by C. albicans (37.2%). The overall crude 28-day mortality was 28.1% and significantly reduced with appropriate empiric antifungal therapy administered within 5 days (P = 0.006). Advanced age (OR 1.04; P = 0.014), neutropenia < 500/mm³ (OR 17.44; P < 0.001) were independent risk factors for 28-day mortality, while appropriate empiric antifungal therapy (OR 0.369; P = 0.035) was protective against 28-day mortality. Conclusion: The epidemiology of candidemia in Shanghai differed from that observed in Western countries. Appropriate empiric antifungal therapy influenced the short-term survival.

Background: Acute respiratory distress syndrome (ARDS) is one of the leading causes of death in patients with sepsis. As such, early and accurate identification of sepsis-related ARDS is critical. Methods: Bioinformatic analysis was used to explore the GEO datasets. ELISA method was used to detect the plasma or cellular supernatant of relevant proteins. Quantitative real-time PCR was used for mRNA measurements and Western blot was applied for protein measurements. Immunohistochemistry staining and Immunofluorescence staining were used to identify the localization of OLFM4. Cecal ligation and puncture (CLP) model was used to establish sepsis model. Results: The bioinformatic analysis results identified ten genes (CAMP, LTF, RETN, LCN2, ELANE, PGLYRP1, BPI, DEFA4, MPO, and OLFM4) as critical in sepsis and sepsis-related ARDS. OLFM4, LCN2, and BPI were further demonstrated to have diagnostic values in sepsis-related ARDS. Plasma expression of OLFM4 and LCN2 was also upregulated in sepsis-related ARDS patients compared to septic patients alone. OLFM4 expression was significantly increased in the lung tissues of septic mice and was co-localized with Ly6G+ neutrophils, F4/80+ macrophages and pro-surfactant C+ lung epithelial cells. In vitro data showed that OLFM4 expression in lung epithelial cells was downregulated upon LPS stimulation, whereas neutrophil media induced OLFM4 expression in lung epithelial cells. Overexpression of OLFM4 and treatment with recombinant OLFM4 effectively suppressed LPS-induced pro-inflammatory responses in lung epithelial cells. Furthermore, the increased levels of LDHA phosphorylation and the downstream NF-κB activation induced by LPS in epithelial cells were effectively diminished by OLFM4 overexpression and recombinant OLFM4 treatment via a reduction in ROS production and HIF1α expression. Conclusion: OLFM4 may regulate the pro-inflammatory response of lung epithelial cells in sepsis-related ARDS by modulating metabolic disorders; this result could provide new insights into the treatment of sepsis-induced ARDS.

Sepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes involved in the molecular mechanisms of sepsis and search for potential new biomarkers and treatment targets for sepsis using bioinformatics analyses. Three datasets (GSE95233, GSE57065, and GSE28750) associated with sepsis were downloaded from the public functional genomics data repository Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using R packages (Affy and limma). Functional enrichment of the DEGs was analyzed with the DAVID database. Protein-protein interaction networks were derived using the STRING database and visualized using Cytoscape software. Potential biomarker genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). The three datasets included 156 whole blood RNA samples from 89 sepsis patients and 67 healthy controls. Between the two groups, 568 DEGs were identified, among which 315 were upregulated and 253 were downregulated in the septic group. These genes were enriched for pathways mainly involved in the innate immune response, T-cell biology, antigen presentation, and natural killer cell function. ROC analyses identified nine genes—LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN—as potential new biomarkers for sepsis. Real-time PCR confirmed that the expression of seven of these genes was in accordance with the microarray results. This study revealed imbalanced immune responses at the transcriptomic level during early sepsis and identified nine genes as potential biomarkers for sepsis.