Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0024141 (systemic lupus erythematosus)
 44,322 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The mechanisms that cause the appearance of autoantibodies are not understood. Compared to normal antibody production, factors responsible for autoantibody synthesis are more complex; they are thought to disrupt the normal mechanisms proposed to eliminate or down-regulate self-antibodies or to interfere with anti-self-receptor editing. Data presented show that autoantibodies exist in the blood of all normal individuals. The autoantibodies appear after simple oxidation-reduction (redox) reactions and react by ELISA, immunofluorescence, flow cytometry, Western blots, and in lupus anticoagulant (LA) assays. Antiphospholipid antibody (aPL) specificities detected after redox are cardiolipin (aCL), antiphosphatidylserine (aPS), antiphosphatidylethanolamine (aPE), antiphosphatidylcholine (aPC), and LA. These antibody activities were confirmed in several outside laboratories. The aPL isotypes detected in ELISA are plasma protein-dependent and include IgG, IgA, and IgM. Oxidizing agents tested to date include hemin, KMnO4, and NaIO4. Furthermore, aPL appear after exposure to direct current (DC)-mediated electromotive force. Alternating current (AC) is ineffective. Commercial IvIg preparations, also a source of IgG autoantibodies, provide a less complex milieu than plasma or serum for studying the biology of aPL redox-mediated mechanisms. Inhibition of hemin-mediated IvIg aPL conversion can be achieved by the addition of antioxidants, e.g., ascorbic acid, hemopexin, apotransferrin, and by addition of normal plasma or serum. Remarkably, the aPL specificities in the blood of autoimmunity patients disappear subsequent to application of redox reactions. These data document the hitherto unknown existence of redox-reactive autoantibodies in all normal individuals. The evolutionary persistence of these redox-sensitive antibodies raises interesting possibilities about their potentially beneficial role in immunological homeostasis.
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PMID:The appearance and disappearance of antiphospholipid autoantibodies subsequent to oxidation--reduction reactions. 1550 94

Complex genetic disorders are a result of a combination of genetic and nongenetic factors, all potentially interacting. Machine learning methods hold the potential to identify multilocus and environmental associations thought to drive complex genetic traits. Decision trees, a popular machine learning technique, offer a computationally low complexity algorithm capable of detecting associated sets of single nucleotide polymorphisms (SNPs) of arbitrary size, including modern genome-wide SNP scans. However, interpretation of the importance of an individual SNP within these trees can present challenges. We present a new decision tree algorithm denoted as Bagged Alternating Decision Trees (BADTrees) that is based on identifying common structural elements in a bootstrapped set of Alternating Decision Trees (ADTrees). The algorithm is order nk(2), where n is the number of SNPs considered and k is the number of SNPs in the tree constructed. Our simulation study suggests that BADTrees have higher power and lower type I error rates than ADTrees alone and comparable power with lower type I error rates compared to logistic regression. We illustrate the application of these data using simulated data as well as from the Lupus Large Association Study 1 (7,822 SNPs in 3,548 individuals). Our results suggest that BADTrees hold promise as a low computational order algorithm for detecting complex combinations of SNP and environmental factors associated with disease.
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PMID:Bootstrap aggregating of alternating decision trees to detect sets of SNPs that associate with disease. 2285 73