Alpha-1 antitrypsin deficiency: A conformational disease associated with lung and liver manifestations

Alpha-1 antitrypsin (A1AT) is a serine anti-protease produced chiefly by the liver. A1AT deficiency is a genetic disorder characterized by serum levels of less than 11 μmol/L and is associated with liver and lung manifestations. The liver disease, which occurs in up to 15% of A1AT-deficient individuals, is a result of toxic gain-of-function mutations in the A1AT gene, which cause the A1AT protein to fold aberrantly and accumulate in the endoplasmic reticulum of hepatocytes. The lung disease is associated with loss-of-function, specifically decreased anti-protease protection on the airway epithelial surface. The so-called 'Z' mutation in A1AT deficiency encodes a glutamic acid-to-lysine substitution at position 342 in A1AT and is the most common A1AT allele associated with disease. Here we review the current understanding of the molecular pathogenesis of A1AT deficiency and the best clinical management protocols. © Springer Science+Business Media B.V. 2008.

Assignment methods for incidence calculus

Incidence calculus is a mechanism for probabilistic reasoning in which sets of possible worlds, called incidences, are associated with axioms, and probabilities are then associated with these sets. Inference rules are used to deduce bounds on the incidence of formulae which are not axioms, and bounds for the probability of such a formula can then be obtained. In practice an assignment of probabilities directly to axioms may be given, and it is then necessary to find an assignment of incidence which will reproduce these probabilities. We show that this task of assigning incidences can be viewed as a tree searching problem, and two techniques for performing this research are discussed. One of these is a new proposal involving a depth first search, while the other incorporates a random element. A Prolog implementation of these methods has been developed. The two approaches are compared for efficiency and the significance of their results are discussed. Finally we discuss a new proposal for applying techniques from linear programming to incidence calculus.