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Background: Patients with hemorrhagic shock may develop emerging enterogenic sepsis due to damage to the intestinal mucosal barrier and translocation of intestinal bacteria and endotoxins caused by ischemic injury. Because of the dual effects of anesthesia state and hemorrhagic shock, perioperative emerging enterogenic sepsis is even more rare and insidious. Case presentation: We reported a case of 56-year-old man who underwent right hepatectomy for intrahepatic bile duct stones. Severe hemorrhage occurred during the procedure and the hemodynamics neither improved nor worsened after rehydration therapy and vasoactive drug administration. Based on the patient's history and clinical presentation, a possible enterogenic sepsis was considered. After anti-infective treatment and hormone supplementation, the patient's circulation improved significantly and he had an uneventful recovery. Conclusion: The possibility of emerging enterogenic sepsis in hemorrhagic shock must always be taken into consideration. Familiarity with the risk factors and pathophysiological alterations of enterogenic sepsis is a prerequisite for early recognition and sound clinical decision making. [ABSTRACT FROM AUTHOR]

Background: Airborne fine particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) pollution is associated with the prevalence of respiratory diseases, including asthma, bronchitis and chronic obstructive pulmonary disease. In patients with those diseases, circulating asymmetric dimethylarginine (ADMA) levels are increased, which contributes to airway nitric oxide deficiency, oxidative stress and inflammation. Overexpression of dimethylarginine dimethylaminohydrolase 1 (DDAH1), an enzyme degrading ADMA, exerts protective effects in animal models. However, the impact of DDAH1/ADMA on PM2.5-induced lung injury has not been investigated. Methods: Ddah1−/− and DDAH1-transgenic mice, as well as their respective wild-type (WT) littermates, were exposed to either filtered air or airborne PM2.5 (mean daily concentration ~ 50 µg/m3) for 6 months through a whole-body exposure system. Mice were also acutely exposed to 10 mg/kg PM2.5 and/or exogenous ADMA (2 mg/kg) via intratracheal instillation every other day for 2 weeks. Inflammatory response, oxidative stress and related gene expressions in the lungs were examined. In addition, RAW264.7 cells were exposed to PM2.5 and/or ADMA and the changes in intracellular oxidative stress and inflammatory response were determined. Results: Ddah1−/− mice developed more severe lung injury than WT mice after long-term PM2.5 exposure, which was associated with greater induction of pulmonary oxidative stress and inflammation. In the lungs of PM2.5-exposed mice, Ddah1 deficiency increased protein expression of p-p65, iNOS and Bax, and decreased protein expression of Bcl-2, SOD1 and peroxiredoxin 4. Conversely, DDAH1 overexpression significantly alleviated lung injury, attenuated pulmonary oxidative stress and inflammation, and exerted opposite effects on those proteins in PM2.5-exposed mice. In addition, exogenous ADMA administration could mimic the effect of Ddah1 deficiency on PM2.5-induced lung injury, oxidative stress and inflammation. In PM2.5-exposed macrophages, ADMA aggravated the inflammatory response and oxidative stress in an iNOS-dependent manner. Conclusion: Our data revealed that DDAH1 has a marked protective effect on long-term PM2.5 exposure-induced lung injury. [ABSTRACT FROM AUTHOR]

Background: Human heart failure is a complex disease that manifests from multiple genetic and environmental factors. Although ischemic and non-ischemic heart disease present clinically with many similar decreases in ventricular function, emerging work suggests that they are distinct diseases with different responses to therapy. The ability to distinguish between ischemic and non-ischemic heart failure may be essential to guide appropriate therapy and determine prognosis for successful treatment. In this paper we consider discriminating the etiologies of heart failure using gene expression libraries from two separate institutions., Results: We apply five new statistical methods, including partial least squares, penalized partial least squares, LASSO, nearest shrunken centroids and random forest, to two real datasets and compare their performance for multiclass classification. It is found that the five statistical methods perform similarly on each of the two datasets: it is difficult to correctly distinguish the etiologies of heart failure in one dataset whereas it is easy for the other one. In a simulation study, it is confirmed that the five methods tend to have close performance, though the random forest seems to have a slight edge., Conclusions: For some gene expression data, several recently developed discriminant methods may perform similarly. More importantly, one must remain cautious when assessing the discriminating performance using gene expression profiles based on a small dataset; our analysis suggests the importance of utilizing multiple or larger datasets.