Pregled bibliografske jedinice broj: 20972
Identification of enterococcal strains using an inductive learning algorithm
Identification of enterococcal strains using an inductive learning algorithm // XIIIth Lancefield International Symposium on Streptococci and Streptococcal Diseases : Streptococci and the Host : Proceedings / Horaud, T. ; Bouvet, A. ; Leclercq, R. ; de Montclos, H. ; Sicard, M. (ur.).
Pariz, Francuska: Plenum Publishing Corporation, 1997. str. 417-418 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 20972 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Identification of enterococcal strains using an inductive learning algorithm
Autori
Bejuk, Danijela ; Begovac, Josip ; Gamberger, Dragan ; Kuzmanović, Nataša
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
XIIIth Lancefield International Symposium on Streptococci and Streptococcal Diseases : Streptococci and the Host : Proceedings
/ Horaud, T. ; Bouvet, A. ; Leclercq, R. ; de Montclos, H. ; Sicard, M. - : Plenum Publishing Corporation, 1997, 417-418
ISBN
0-306-45603-6
Skup
Lancefield International Symposium on Streptococci and Streptococcal Diseases (18 ; 1996)
Mjesto i datum
Pariz, Francuska, 09.1996
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
enterococcus; algorithms; bacterial typing
Sažetak
A total of 153 enterococcal isolates from blood and urine were collected at the University Hospital for Infectious Diseases, Zagreb, Croatia in the period from March 1992 to September 1992. One hundred and thirty-eight of the isolates were identified as E.faecalis (90.1%), 13 (8.4%) as E. faecium and the remaining 2 (1.5%) as E. avium by the conventional test scheme (Facklam, Collins). The aim of this study was to evaluate a simple scheme for species identification of most frequently isolated enterococcal strains in our institution. The necessary and sufficient conditions to detect E. faecalis, E. faecium and E. avium are computed from the selected set of 13 tests by the inductive learning algorithm ILLM (Inductive Learning by Logic Minimization). The tests were: 1) growth in 6.5% NaCl, 2) tolerance to 0.4% tellurite, 3) reduction of 0.01% tetrazolium, 4) detection of yellow pigment, 5) arginine hydrolysis, 6) hydrolysis of sodium hippurate, 7) fermentation of sucrose, 8) lactose, 9) raffinose, 10) glycerol, 11) sorbitol, 12) mannitol and 13) determination of Lancefield's D antigen. The ILLM algorythm searches over all possible combinations of the tests from starting set and selects the minimal condition with the property that is true for and only for the target species. We analysed 16 enterococcal species and found out 18 tests that correctly identified E. faecalis, E.faecium and E. avium. The results were: E. faecalis( if (50=neg, 9=neg, 10=pos) ; E.faecium if (2=pos, 5=pos, 8=pos, 9=neg) ; E. avium if (3=neg, 6=pos, 9=neg, 10=pos, 11=neg). The ILLM algorythm could be a simple way to determine a minimal set of tests for identification of enterococcal strains.
Izvorni jezik
Engleski
Znanstvena područja
Javno zdravstvo i zdravstvena zaštita
Napomena
Advances in Experimental Medicine and Biology ; Vol. 418
POVEZANOST RADA
Projekti:
108021
Ustanove:
Medicinski fakultet, Zagreb
Profili:
Danijela Bejuk
(autor)
Josip Begovac
(autor)
Nataša Šterk-Kuzmanović
(autor)
Dragan Gamberger
(autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- Scopus
- MEDLINE