BDR was not related to any particular inflammatory phenotype or any clinical or anti-inflammatory response to ICS in these subjects with mild to moderate COPD.
Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.
A simple, easy to use and efficient method was described for simultaneous determination of ten cardiovascular drugs with a broad range of physicochemical properties in rat plasma via online SPE and HPLC-MS/MS.
RationaleSmoking effects on physiological and gross pathology in chronic obstructive pulmonary disease (COPD) are relatively well described. However, there is little known in COPD about the detailed interrelationships between lung function and inflammatory profiles in different airway compartments from the same individual and whether airway inflammation in these different compartments differs in ex- and current smokers with established COPD.ObjectivesWe compared sputum, bronchoalveolar (BAL), and airway wall inflammatory profiles in current versus ex-smokers and related this to smoking intensity and lung function in 17 current and 17 ex-smokers with mild to moderate COPD.ResultsCurrent smokers had more sputum mast cells (% differential and absolute numbers), whereas ex-smokers had increased sputum neutrophils. In BAL, there was a significant increase in eosinophils in current smokers, but ex-smokers had significantly increased neutrophils, lymphocytes, and epithelial cells. There were no cell profile differences observed in airway biopsies between current and ex-smokers and there were no correlations between the individual inflammatory cell populations in any of the airway compartments. In current smokers only, smoking intensity was negatively correlated with lung function, and associated with a reduction in overall cellularity of both sputum and BAL.ConclusionAirway inflammation persists in ex-smokers with COPD, but differs from COPD current smokers. The impact of smoking appears to vary in different airway compartments and any direct relationships between cellularity and lung function tended to be negative, ie, worse lung function indicated the presence of fewer cells.
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