Development of an expert system for bacterial identification: study of a prototype for identifying β‐galactosidase positive enterobacteria

To determine if there is any change with age in the distribution of Bi dobacterium and Enterobacteriaceae species in human intestinal micro ora, strains were isolated from a total of 54 samples of human faeces (15 children, 3-15 years old; 17 adults, 30-46 years old; 22 elderly, 69-89 years old). The bi dobacteria were identi ed at species level using a numerical phenotypic identi cation system developed on the Internet (http:::kounou.lille.inra.fr) and DNA-DNA hyridization. The enterobacteria were identi ed using the API 20E system (Biomérieux, France). Escherichia coli was present in 93% of samples. Enterobacter and Klebsiella species were more frequently found in children while Proteus and Pro×idencia species were typically found in the elderly. The species Bi dobacterium longum was the most frequently species isolated in children and adults, whereas Bi dobacterium adolescentis was the most encountered species in the elderly. The distribution of B. adolescentis was signi cantly split between the children and the elderly age groups. There is a noticeable trend whereby Bi dobacterium pseudocatenulatu m occurs more frequently in children than in the elderly. The species Bi dobacterium infantis, Bi dobacterium gallicum and Bi dobacterium angulatum have never been isolated, while Bi dobacterium bre×e has only been isolated on one occasion.

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“…The identi cation system included 30 tests among the 77 tests recorded. They were chosen as previously described (14).…”

Section: Methods Concerning Bi Dobacteria Identi Cationmentioning

confidence: 99%

Differences in the Distribution of Bifidobacterial and Enterobacterial Species in Human Faecal Microflora of Three Different (Children, Adults, Elderly) Age Groups

Gavini

Cayuela

Antoine

et al. 2001

Microbial Ecology in Health and Disease

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“…Thomson 96 and microdilution differing by Ն2 dilution steps from those obtained with microdilution were considered discrepant results. The percentages of discrepancies in the MICs of imipenem determined with U6I panels were 74% and 84% for NFGNR and Enterobacteriaceae, respectively.…”

Section: Carbapenem Resistancementioning

confidence: 99%

“…Its objectives are to highlight errors that can occur when the kit is in use. It can indicate the presence of new groups or species and give advice or suggest additional tests for the differentiation of the new species from those included in the kit (96). Expert systems have also been used for parasite identification (256).…”

Section: Introductionmentioning

confidence: 99%

Expert Systems in Clinical Microbiology

Winstanley

Courvalin

2011

Clin Microbiol Rev

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SUMMARY This review aims to discuss expert systems in general and how they may be used in medicine as a whole and clinical microbiology in particular (with the aid of interpretive reading). It considers rule-based systems, pattern-based systems, and data mining and introduces neural nets. A variety of noncommercial systems is described, and the central role played by the EUCAST is stressed. The need for expert rules in the environment of reset EUCAST breakpoints is also questioned. Commercial automated systems with on-board expert systems are considered, with emphasis being placed on the “big three”: Vitek 2, BD Phoenix, and MicroScan. By necessity and in places, the review becomes a general review of automated system performances for the detection of specific resistance mechanisms rather than focusing solely on expert systems. Published performance evaluations of each system are drawn together and commented on critically.

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The potential for computer-aided identification in biodiversity research

Edwards

Morse

1995

Trends in Ecology & Evolution

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Development of an expert system for bacterial identification: study of a prototype for identifying β‐galactosidase positive enterobacteria

Cited by 5 publications

References 8 publications

Differences in the Distribution of Bifidobacterial and Enterobacterial Species in Human Faecal Microflora of Three Different (Children, Adults, Elderly) Age Groups

Differences in the Distribution of Bifidobacterial and Enterobacterial Species in Human Faecal Microflora of Three Different (Children, Adults, Elderly) Age Groups

Expert Systems in Clinical Microbiology

The potential for computer-aided identification in biodiversity research

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