Then, the first modified QuEChERS method was established according to the original QuEChERS cleanup procedure. For the second modified QuEChERS method, the extract was evaporated to dryness and reconstituted in acetonitrile. Subsequently, the analytes in the reconstituted solution were retained by primary secondary amine (PSA) and released from PSA with 1.0% formic acid in methanol. Finally, the eluate was evaporated and dissolved in 0.1% formic acid solution/methanol (v/v, 80:20). All of the samples were analyzed by LC-MS/MS on a Waters Acquity BEH C-18 column with 0.1% formic acid in water/methanol as the mobile phase with gradient elution. The matrix effect, recovery, and repeatability,
selleck within laboratory reproducibility, and the LODs and LOQs of the two modified QuEChERS sample preparation methods were investigated and compared. Comparative results showed that the second method was obviously superior to the first method. (C) 2013 Elsevier B.V. All rights reserved.”
“Background: Discerning the genetic contributions to complex human diseases is a challenging mandate that demands new types of data and calls for new avenues for advancing the state-of-the-art in computational approaches to uncovering disease etiology. Systems approaches to studying observable phenotypic relationships among
diseases are emerging as an active area of research for both novel disease gene discovery and drug repositioning. PFTα molecular weight Currently, systematic study of disease relationships on a phenome-wide scale is limited due to the lack of large-scale
machine understandable disease phenotype relationship knowledge bases. Our study innovates a semi-supervised iterative pattern learning approach that is used to build an precise, large-scale disease-disease risk relationship (D1 – bigger than D2) knowledge base (dRiskKB) from a vast corpus of free-text published biomedical literature. Results: 21,354,075 MEDLINE records comprised the text corpus under study. DMH1 molecular weight First, we used one typical disease risk-specific syntactic pattern (i.e. “D1 due to D2″) as a seed to automatically discover other patterns specifying similar semantic relationships among diseases. We then extracted D1 – bigger than D2 risk pairs from MEDLINE using the learned patterns. We manually evaluated the precisions of the learned patterns and extracted pairs. Finally, we analyzed the correlations between disease-disease risk pairs and their associated genes and drugs. The newly created dRiskKB consists of a total of 34,448 unique D1 – bigger than D2 pairs, representing the risk-specific semantic relationships among 12,981 diseases with each disease linked to its associated genes and drugs. The identified patterns are highly precise (average precision of 0.99) in specifying the risk-specific relationships among diseases. The precisions of extracted pairs are 0.919 for those that are exactly matched and 0.988 for those that are partiallymatched.