Exchange of unsaturated fatty acids between adipose tissue and atherosclerotic plaque studied with artificial neural networks

Prostaglandins Leukotrienes and Essential Fatty Acids

Volumn 70, Issue 1, 2004, Pages 59-66

  Stachowska, E ^a   Gutowska, I ^a   Dolegowska B ^a   Chlubek, D ^a   Bober, J ^a   Rac M ^a   Gutowski, P ^a   Szumilowicz H ^a   Turowski, R ^a  

^a POMERANIAN MEDICAL UNIVERSITY   (Poland)

Author keywords

Adipose tissue; Arachidonic acid; Atherosclerotic plaque; Essential fatty acids; Neural networks

Indexed keywords

ARACHIDONIC ACID; LINOLEIC ACID; LINOLENIC ACID; UNSATURATED FATTY ACID;

ADIPOSE TISSUE; ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; ATHEROGENESIS; ATHEROSCLEROTIC PLAQUE; CLINICAL ARTICLE; FATTY ACID METABOLISM; FATTY ACID TRANSPORT; HUMAN; MALE; PRIORITY JOURNAL; SIGNAL TRANSDUCTION;

EID: 0348140621     PISSN: 09523278     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.plefa.2003.08.021     Document Type: Article

References (46)

1. H.W. Cook, E.A. Emken, Geometric and positional fatty acids isomers interact differently with desaturation and elongation of linoleic and linolenic acid in cultured glioma cells, Biochem. Cell. Biol. 68 (1990) 653-660.
2. J.L. Cracowski, P. Devillier, T. Durand, F. Stanke-Labesque, G. Bessard, Vascular biology of the isoprostanes, J. Vasc. Res. 38 (2001) 93-103.
3. S. Parthasarathy, J.C. Khoo, E. Miller, J. Barnett, J.L. Witztum, D. Steinberg, Low density lipoprotein rich in oleic acid is protected against oxidative modification: implication for dietary prevention of atherosclerosis, Proc. Natl. Acad. Sci. 87 (1990) 3894-3898.
4. J.K. Chan, B.E. McDonald, J.M. Gerrard, V.M. Bruce, B.J. Weaver, B.J. Holub, Effect of dietary α linoleic acid and its ratio to linoleic acid on platelet and plasma fatty acids and thrombogenesis, Lipids 28 (1993) 811-817.
5. C.V. Felton, D. Crook, M.J. Davies, M.F. Oliver, Dietary polyunsaturated fatty acids and composition of human aortic plaques, Lancet 344 (1994) 1195-1196.
6. W.S. Mc Culloch, W.H. Pitts, A logical calculus of ideas immanent in nervous activity, Bull. Math. Biophys. 5 (1943) 115-119.
7. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl Acad. Sci. 79 (1982) 2554-2558.
8. Z. Machoy, J. Straszko, V. Dziedziejko, I. Gutowska, Estimation of influence of industrial emissions on fluoride accumulation in deers jaws by neural networks, Environ. Sci. 9 (2002) 289-300.
9. A. Bucinski, T. Bàczek, Optimization of HPLC separations of flavonoids with the use of artificial neural networks, Pol. J. Food Nutr. Sci. 11 (2002) 47-51.
10. V.E. Vapnik, A. Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities, Theory Probab. Appl. 16 (1971) 264-280.
11. S.I. Amaris, Mathematical theory of neural learning, New Generation Comput. 8 (1991) 281-284.
12. E. Tafeit, G. Reibnegger, Artificial neural networks in laboratory medicine and medical outcome prediction, Clin Chem. Lab. Med. 37 (1999) 845-853.
13. C.C. Allain, L.S. Poon, G.S.C. Chang, W. Richmond, P.C. Fu, Enzymatic determination of total serum cholesterol, Clin. Chem. 20 (1974) 470-475.
14. G. Bucolo, H. David, Quantitative determination of serum triglycerides by use of enzymes, Clin. Chem. 19 (1973) 476-482.
15. T.H. Grove, Effect of reagent pH on determination of high-density lipoprotein cholesterol by precipitation with sodium phosphotungstate-magnesium, Clin. Chem. 25 (1979) 560-564.
16. W.T. Friedewald, R.I. Levy, D.S. Fredrickson, Estimation of the concentration of low-density lipoprotein cholesterol in plasma without use of preparative ultracentrifuge, Clin. Chem. 18 (1972) 499-502.
17. J. Folch, M. Lees, V. Stanley Sloane, A simple method for the isolation and purification of total lipids from animal tissues, J. Biol. Chem. 226 (1957) 497-509.
18. L.C. Hudgins, J. Hirsch, E.A. Emken, Correlation of isomeric fatty acids in human adipose tissue with clinical risk factors for cardiovascular disease, Am. J. Clin. Nutr. 53 (1991) 474-482.
19. T.L.V. Ulbricht, D.A.T. Southagate, Coronary heart disease: seven dietary factors, Lancet 338 (1991) 985-992.
20. J.A. Leonard, M.A. Kramer, Radial basis function networks for classifying process faults, IEEE Control Syst. Mag. 4 (1991) 31-38.
21. L. Tarassenko, S. Roberts, Supervised and unsupervised learning in radial basis classifiers, IEE Proc. Vis. Image Signal Process. 141 (1994) 210-216.
22. K.L.H. Carpenter, S.E. Taylor, J.A. Ballantine, B. Fussel, B. Halliwell, M.J. Mitchinson, Lipids and oxidized lipids in human atheroma and normal aorta, Biochim. Biophys. Acta 1167 (1993) 121-130.
23. G. Spiteller, Are changes of the cell membrane structure casually involved in the aging process, Ann. N. Y. Acad. Sci. 959 (2002) 30-44.
24. B. Henning, P. Meerarani, P. Ramadass, B.A. Watkins, M. Toborek, Fatty acid-mediated activation of vascular endothelial cells, Metabolism 49 (2000) 1006-1013.
25. 2, J. Biochem. 131 (2002) 285-292.
26. E.I. Waddington, I.B. Puddey, T.A. Mori, K.D. Croft, Similarity in the distribution of F2-isoprostanes in the lipid subfractions of atherosclerotic plaque and in vitro oxidized low density lipoprotein, Redox. Rep. 7 (2002) 179-184.
27. H. Kuhn, J. Belkner, S. Zaiss, T. Fahrenklemper, S. Wohlfeil, Involvement of 15-lipoxygenase in early stages of atherogenesis, J. Exp. Med. 179 (1994) 1903-1911.
28. Y. Li, P. Jama, I. Seljeflot, H. Meyer, E. Hjerkinn, H. Arnesen, J. Pedersen, Serum non-esterified very long-chain PUFA are associated with markers of endothelial dysfunction, Atherosclerosis 164 (2002) 275-281.
29. 2 may be casually related to NO-induced apoptosis in vascular smooth muscle cells, J. Cell Physiol. 191 (2002) 191-197.
30. 2 is linked to macrophage foam cell formation, J. Biol. Chem. 277 (2002) 2916-2924.
31. Z.H. Khan, S.K. Mohapatra, P.K. Khodiar, S.N. Ragu Kumar, Artificial neural network and medicine, Indian J. Physiol. Pharmacol. 42 (1998) 321-342.
32. C. Hand, Epicenter location by analysis of interictal spikes: a case study for the use of artificial neural networks in biomedical engineering, Ann. N. Y. Acad. Sci. 980 (2002) 306-314.
33. D. Sheppard, D. McPhee, C. Darke, B. Shrethra, R. Moore, A. Jurewitz, A. Gray, Predicting cytomegalovirus disease after renal transplantation: an artificial neural network approach, Int. J. Med. Inf. 54 (1999) 55-76.
34. R.L. Lammers, D.L. Hudson, M.E. Seaman, Prediction of traumatic wound infection with a neural network-derived decision model, Am J. Emerg. Med. 21 (2003) 1-7.
35. T.H. Douglas, J.W. Moul, Applications of neural networks in urologic oncology, Sem. Urol. Oncol. 16 (1998) 35-39.
36. T. Loch, I. Leuschner, C. Genberg, K. Weichert-Jacobsen, F. Kuppers, M. Retz, J. Lehmann, E. Yfantis, M. Evans, V. Tsarev, M. Stockle, Improvement of transrectal ultrasound. Artificial neural network analysis (ANNA) in detection and staging of prostatic carcinoma, Urologe 39 (2000) 341-347.
37. C. Niederberger, Computational tools for the modern andrologist, J. Androl. 17 (1996) 462-466.
38. R.L. Kennedy, R.F. Harrison, A.M. Burton, H.S. Fraser, W.G. Hamer, D. MacArthur, R. McAllum, D.J. Steedman, An artificial neural network system for diagnosis of acute myocardial infraction (AMI) in the accident and emergency department: evaluation and comparison with serum myoglobin measurements, Comput. Methods Programs Biomed. 52 (1997) 93-103.
39. D. Itchhaporia, P.B. Snow, R.J. Almassy, W.J. Oetgen, Artificial neural networks: current status in cardiovascular medicine, J. Am. Coll. Cardiol. 28 (1996) 515-523.
40. W.G. Baxt, J. Skora, Prospective validation of artificial neural network trained to identify acute myocardial infraction, Lancet 347 (1996) 12-15.
41. D.L. Silver, G.A. Hurwitz, The predictive and explanatory power of inductive decision trees: a comparison with artificial neural network learning as applied to the noninvasive diagnosis of coronary artery disease, J. Investig. Med. 45 (1997) 99-108.
42. L. Edenbrandt, R. Rittner, Recognition of lead reversals in pediatric electrocardiograms, Am. J. Cardiol. 82 (1998) 1290-1292 A10.
43. N.I. Birndorf, J.O. Pentecost, J.R. Coakley, K.A. Spackman, An expert system to diagnose anemia and report results directly on hematology forms, Comput. Biomed. Res. 29 (1996) 16-26.
44. M.S. Beksac, B. Durak, O. Ozkan, A.N. Cakar, S. Balci, U. Karakas, Y. Laleli, An artificial intelligent diagnostic system with neural networks to determine genetical disorders and fetal health by using maternal serum markers, Eur. J. Obstet. Gynecol. Reprod. Biol. 59 (1995) 131-136.
45. P.M. Spreight, A.E. Elliott, J.A. Jullien, M.C. Downer, J.M. Zakzrewska, The use of artificial intelligence to identify people at risk of oral cancer and precancer, Br. Dent. J. 197 (1995) 382-387.
46. A.A. El-Solh, S.K. Saltzman, F.H. Ramadan, B.J. Naughton, Validity of an artificial neural network in predicting discharge destination from a postacute geriatric rehabilitation unit, Arch. Phys. Med. Rehabil. 81 (2000) 1388-1393.