Using combination of albumin to fibrinogen ratio and prognostic nutritional index model for predicting disease activity in patients with systemic lupus erythematosus.

Background: Systemic lupus erythematosus (SLE) is chronic autoimmune disease with multiple organ damage and is associated with poor prognosis and high mortality. Identification of universal biomarkers to predict SLE activity is challenging due to the heterogeneity of the disease. This study aimed to identify the indicators that are sensitive and specific to predict activity of SLE.Methods: We retrospectively analyzed 108 patients with SLE. Patients were categorized into SLE with activity and without activity groups on the basis of SLE disease activity index. We analyzed the potential of routine and novel indicators in predicting the SLE activity using receiver operating characteristic curves and multivariate logistic regression. The Spearman method was used to understand the correlation between albumin to fibrinogen ratio (AFR), prognostic nutritional index (PNI), AFR-PNI model and disease activity.Results: SLE with activity group had higher ESR, CRP, D-dimer, fibrinogen, CRP to albumin ratio, positive rate of anti-dsDNA and ANUA, and lower C3, total bilirubin, total protein, albumin, albumin/globulin, creatinine, high density liptein cholesterol, hemoglobin, hematocrit, lymphocyte count, positive rate of anti-SSA, AFR, PNI than SLE without activity. A further established model based on combination of AFR and PNI (AFR-PNI model) showed prominent value in distinguishing SLE with activity patients from SLE without activity patients. In addition, the sensitivity and specificity of AFR-PNI model + anti-dsDNA combination model were superior to AFR-PNI model. AFR and PNI were risk factors for SLE activity. Moreover, AFR+PNI model correlated with disease activity and AFR-PNI model was associated with fever, pleurisy, pericarditis, renal involvement.Conclusion: These findings suggest that predictive model based on combination of AFR and PNI may be useful markers to identify active SLE in clinical practice.

Impact of Minerals and Sealing Systems on the Pore Characteristics of the Qiongzhusi Formation Shale in the Southern Sichuan Basin.

The characteristics, distribution, and preservation of pores are vital in controlling the storage and distribution of shale gas. The Qiongzhusi Formation shales taken from different members with similar tectonic and thermal evolutions were used to evaluate the response of pore characteristics to minerals and sealing systems using field-emission scanning electron microscopy and gas adsorption. Because of differences in mineral structure and arrangement, feldspar, organic matter (OM)-clay, OM-rutile, and OM-apatite aggregates facilitate multiple types of pores in the shale and influence the relative proportions of surface porosity for different types of pores owing to differences in mineral structure and arrangement. Rigid frameworks and pressure shadows formed by rigid minerals and OM-mineral aggregates preserved OM and pores to some extent. The sealing capacity of the floor controls the sealing system and hydrocarbon expulsion efficiency of the Qiongzhusi Formation in different members. During thermal evolution, the amount of hydrocarbons generated and expelled affected the stress equilibrium state between the pore pressure and external stress, influencing the compaction intensity of shales. The OM pore development characteristics were evolved with variation in the stress equilibrium state in different sealing systems. Once the stress equilibrium state was disrupted, the OM pores deformed, narrowed, or even closed under the influence of compaction owing to the loss of overpressure support. The pore characteristics of the Qiongzhusi Formation shales responded significantly to different sealing systems. A few OM pores are flat and slitlike in the open system, whereas numerous OM pores are round and elliptical in the semiopen system. Meanwhile, the average diameter of the OM pores in the open system was reduced by approximately 40.2% compared with that of the semiopen system. Furthermore, the pore volume and specific surface area of the mesopores for open system shales were reduced by 38.4% and 37.7%, respectively, compared to the semiopen system. These findings will improve the understanding of the distribution and preservation of pore in shale and help assess the sweet-spot members for the Qiongzhusi Formation shale gas.

[Chemical Species of PM2.5 in the Urban Area of Beijing].

From August 2012 to July 2013, 220 groups of PM2.5 samples were continuously collected at four locations in the urban area of Beijing (Shijingshan, Chegongzhuang, Dongsi, and Tongzhou), and the primary chemical species of PM2.5 were analysed by the chemical mass balance method. It was found that the mass of PM2.5 obtained from chemical mass balance method agreed well with the value from gravimetric measurement, with a good correlation of 0. 95 in spring, autumn, and winter. However, the correlation seasonally changed in summer, with a relatively lower correlation coefficient of 0. 77. The concentrations of OM, EC, SO(4)2-, NO3-, NH4+, Cl-, crustal matter, and trace species were 31. 4, 3. 8, 19. 9, 21. 6, 14. 4, 4. 0, 15. 4, and 2. 9 µg.m-3, which accounted for 25. 1%, 3. 0% , 15. 9%, 17. 2%, 11. 5%, 3. 2%, 12. 3%, and 2. 3% of PM2, , respectively. Besides crustal matter, concentrations of the primary chemical species in PM2.5 from the west to the east gradually increased. The most serious PM pollution occurred between 11 and 14 February 2013, during which concentrations of OM, SO2-, NO3-, NH4+ were 1. 9, 5. 0, 3.2 and 4. 2 times as high as the annual average. SO(4)2- was recognized as the most important species for the pollution in the process. OM was the largest component of urban PM2.5 during both heating and non-heating periods. Comparing to non-heating period, the concentrations of OM, NH4+, NO3-, and SO(4)2- all increased during the heating period, except for the component of crust and EC. The biggest difference between the two periods was the component of Cl- (4. 4 fold), which can be attributed to the burning of coal. For unknown components, the main component was moisture, which accounted for about 6.0% in urban PM2.5. The highest moisture appeared in summer (6. 5%), followed by spring and winter, and the least appeared in fall (3. 7% ).