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ABSTRACT: Background/Aims: Endoplasmic reticulum (ER) stress and hypertriglyceridemia (HTG) have been implicated in acute pancreatitis (AP). Methodology: For cellular model, rat exocrine acinar cells were preincubated with palmitic acid (0.05 or 0.1mmol/L, 3h) and stimulated with a cholecystokinin analog, CCK-8 (100pmol/L, 30min). For animal model, rats fed a high-fat diet to cause HTG and AP was induced by injection of caerulein (20µg/kg). Injury to pancreatic cells was estimated by measuring amylase secretion, intracellular calcium concentration, apoptosis and histological changes. Expression of genes involved in ER stress-induced unfolded protein response (UPR) was monitored by RT-PCR and immunohistology. Results: In CCK-8 stimulated rat acinar cells, preincubation with PA caused an increased secretion of amylase, a higher and prolonged accumulation of intracellular calcium and increased apoptosis. Rats on high-fat diet had significantly elevated serum triglyceride levels. Induction of AP led to increased apoptosis in pancreatic tissue on high-fat diet than controls. For favoring HTG, expression of UPR components, GRP78/Bip, XBP-1, GADD153/CHOP and caspase-12 was upregulated. Conclusions: Levels of markers of AP pathogenesis and components of UPR were elevated in the presence of excess fatty acids in pancreatic acinar cells. HTG appears to aggravate ER-stress and pathogenesis of AP.
Hepato-gastroenterology 03/2012; 59(119). · 0.66 Impact Factor
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ABSTRACT: Purpose – The purpose of this paper is to present an evolutionary clustering algorithm based on mixed measure for complex distributed data. Design/methodology/approach – In this method, the data are first partitioned into some spherical distributed sub-clusters by using the Euclidean distance as the similarity measurement, and each clustering center represents all the members of corresponding cluster. Then, the clustering centers obtained in the first phase are clustered by using a novel manifold distance as the similarity measurement. The two clustering processes in this method are both based on evolutionary algorithm. Findings – Theoretical analysis and experimental results on seven artificial data sets and seven UCI data sets with different structures show that the novel algorithm has the ability to identify clusters efficiently with no matter simple or complex, convex or non-convex distribution. When compared with the genetic algorithm-based clustering and the K-means algorithm, the proposed algorithm outperformed the compared algorithms on most of the test data sets. Originality/value – The method presented in this paper represents a new approach to solving clustering problems of complex distributed data. The novel method applies the idea “coarse clustering, fine clustering”, which executes coarse clustering by Euclidean distance and fine clustering by manifold distance as similarity measurements, respectively. The proposed clustering algorithm is shown to be effective in solving data clustering problems with different distribution.
International Journal of Intelligent Computing and Cybernetics 11/2011; 4(4):511-526.
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13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Companion Material Proceedings, Dublin, Ireland, July 12-16, 2011; 01/2011
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11/2010; , ISBN: 978-953-307-426-9
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Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18-23 July 2010; 01/2010
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Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18-23 July 2010; 01/2010
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Genetic and Evolutionary Computation Conference, GECCO 2010, Proceedings, Portland, Oregon, USA, July 7-11, 2010; 01/2010
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Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18-23 July 2010; 01/2010
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Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18-23 July 2010; 01/2010
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Science in China Series F: Information Sciences. 01/2009; 52:2342-2353.
