SAA (Serum Amyloid A): A Novel Predictor of Stroke-Associated Infections.

BACKGROUND AND PURPOSE: The aim of this study was to evaluate and independently validate SAA (serum amyloid A)-a recently discovered blood biomarker-to predict poststroke infections. METHODS: The derivation cohort (A) was composed of 283 acute ischemic stroke patients and the independent validation cohort (B), of 367 patients. The primary outcome measure was any stroke-associated infection, defined by the criteria of the US Centers for Disease Control and Prevention, occurring during hospitalization. To determine the association of SAA levels on admission with the development of infections, logistic regression models were calculated. The discriminatory ability of SAA was assessed, by calculating the area under the receiver operating characteristic curve. RESULTS: After adjusting for all predictors that were significantly associated with any infection in the univariate analysis, SAA remained an independent predictor in study A (adjusted odds ratio, 1.44 [95% CI, 1.16-1.79]; P=0.001) and in study B (adjusted odds ratio, 1.52 [1.05-2.22]; P=0.028). Adding SAA to the best regression model without the biomarker, the discriminatory accuracy improved from 0.76 (0.69-0.83) to 0.79 (0.72-0.86; P<0.001; likelihood ratio test) in study A. These results were externally validated in study B with an improvement in the area under the receiver operating characteristic curve, from 0.75 (0.70-0.81) to 0.76 (0.71-0.82; P<0.038). CONCLUSIONS: Among patients with ischemic stroke, blood SAA measured on admission is a novel independent predictor of infection after stroke. SAA improved the discrimination between patients who developed an infection compared with those who did not in both derivation and validation cohorts. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00390962.

Antiapolipoprotein A-1 Autoantibody Positivity Is Associated with Threatened Abortion.

BACKGROUND: Autoantibodies against apolipoprotein A-1 (anti-ApoA-1 IgG) were demonstrated to be associated with cardiovascular outcomes in several inflammatory diseases. As balanced inflammation is critical for uncomplicated pregnancy, we aimed to investigate the prevalence of anti-ApoA-1 IgG and anti-c-terminal ApoA-1 autoantibodies (Ac-terAA1 IgG) in a cohort of pregnant women and their potential relationship with threatened abortion (TA). METHODS: Between 2012 and 2014, 371 consecutive outpatient pregnant women were included in this study and followed until delivery. Anti-ApoA-1 and anti-Ac-terAA1 IgG were measured by ELISA technique on serum samples collected between the 24th and 26th week of pregnancy. Associations with TA were tested using linear regression analysis and C-statistics. RESULTS: Median age was 34 with a prevalence of the Caucasian ethnicity (90.5%). TA occurred in 10 women (2.7%). C-statistics indicated that anti-ApoA-1 and anti-Ac-terAA1 IgG levels upon study inclusion were predictive of TA (0.73, 95% confidence interval [CI] 0.69-0.78, p < 0.001 and 0.76, 95% CI 0.71-0.80, p < 0.001 and 0.76, 95% CI 0.71-0.80, p < 0.001 and 0.76, 95% CI 0.71-0.80, p < 0.001 and 0.76, 95% CI 0.71-0.80. CONCLUSION: Anti-ApoA-1 and anti-Ac-terAA1 IgG are independently associated with TA during pregnancy with an appealing NPV. The causal biological mechanisms underlying this association as well as the possible clinical relevance of these findings require further investigations.

EasyProt--an easy-to-use graphical platform for proteomics data analysis.

High throughput protein identification and quantification analysis based on mass spectrometry are fundamental steps in most proteomics projects. Here, we present EasyProt (available at http://easyprot.unige.ch), a new platform for mass spectrometry data processing, protein identification, quantification and unexpected post-translational modification characterization. EasyProt provides a fully integrated graphical experience to perform a large part of the proteomic data analysis workflow. Our goal was to develop a software platform that would fulfill the needs of scientists in the field, while emphasizing ease-of-use for non-bioinformatician users. Protein identification is based on OLAV scoring schemes and protein quantification is implemented for both, isobaric labeling and label-free methods. Additional features are available, such as peak list processing, isotopic correction, spectra filtering, charge-state deconvolution and spectra merging. To illustrate the EasyProt platform, we present two identification and quantification workflows based on isobaric tagging and label-free methods.

Proteomic profiling in an animal model of acute pancreatitis.

Acute pancreatitis (AP) is an inflammatory disease of the pancreas, which evolves in approximately 20% of the patients to a severe illness associated with a high mortality rate. In this study, we performed a comparative proteomic analysis of pancreatic tissue extracts from rats with AP and healthy rodent controls in order to identify changes in protein expression related to the pathobiological processes of this disease. Pancreatic extracts from diseased and controls rats were analyzed by 2-DE and MS/MS. A total of 125 proteins were identified from both samples. Comparative analysis allowed the detection of 42 proteins or protein fragments differentially expressed between diseased and control pancreas, some of them being newly described in AP. Interestingly, these changes were representative of the main pathobiological pathways involved in this disease. We observed activation of digestive proteases and increased expression of various inflammatory markers, including several members of the alpha-macroglobulin family. We also detected changes related to oxidative and cell stress responses. Finally, we highlighted modifications of 14-3-3 proteins that could be related to apoptosis regulation. These results showed the interest of proteomic analysis to identify changes characterizing pancreatic tissue damage and, therefore, to highlight new potential biomarkers of AP.