Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome.

Objective: As a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical parameters to alarm the development of ARDS in septic patients. Methods: Gene expression profile (GSE66890) was downloaded from the Gene Expression Omnibus database and clinical data were extracted. Differentially expressed genes (DEGs) from whole blood leukocytes were identified between patients with sepsis alone and septic patients who develop ARDS. ARDS prediction model was constructed using backward stepwise regression and Akaike Information Criterion (AIC). Meanwhile, a nomogram based on this model was established, with subsequent internal validation. Results: A total of 57 severe septic patients were enrolled in this study, and 28 (49.1%) developed ARDS. Based on the differential expression analysis, six DEGs (BPI, OLFM4, LCN2, CD24, MMP8 and MME) were screened. According to the outcome prediction model, six valuable risk factors (direct lung injury, shock, tumor, BPI, MME and MMP8) were incorporated into a nomogram, which was used to predict the onset of ARDS in septic patients. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. The area under the receiver operating characteristic (AUROC) for the prediction of ARDS occurrence in septic patients by the nomogram was 0.86 (95% CI = 0.767-0.952). A sensitivity analysis showed that the AUROC for the prediction of ARDS development in septic patients without direct lung injury was 0.967 (95% CI = 0.896-1.0). Conclusions: The nomogram based on transcriptional biomarkers and clinical parameters showed a good performance for the prediction of ARDS occurrence in septic patients.

[Normal alkanes characteristic parameters of Jinzhou Bay surface sediments].

The concentration, composition and characteristic parameters of 18 surface sediment samples collected from Jinzhou Bay were studied. The samples were soxhlet-extracted with a mixture of 1: 1 (V/V) dichloromethane-hexane and analyzed by GC-MS after purification and concentration. Concentrations of n-alkanes vary from 1.9 to 4.2 microg x g(-1) with an average value of 2.6 microg x g(-1) dry weight. n-Alkanes distribution patterns of all positions were characterized by double peak-cluster, which means double sources from terrestrial and marine origin. The sum of nC25 to nC31 accounts for 20%-32% of the total n-alkanes, while the average value of L/H, C31/C19, TAR ratio are 0.67, 3.06, 2.02, respectively. All of these three indices suggest that the terrestrial contributions are higher than marine sources, especially for No. 2, 3 and 7 stations, which were influenced by riverinput nearby. Carbon Preference Index (CPI) ranges from 1.19 to 2.63 with an average value of 1.73, which is close to 1; the ratio of Pristane/Phytane (Pr/Ph) and unresolved/resolved compounds (U/R) range from 0.91 to 1.28, 2.2 to 4.3, respectively. All of these three parameters indicate that No. 13 and 15 stations are influenced by petroleum pollution. Comprehensive analysis of various parameters shows that Jinzhou Bay is threatened by both terrestrial inputs and petroleum hydrocarbons contaminations, which may relate to river discharging and port shipping in Jinzhou Bay.