This Article Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Reprints and Permissions Copyright Information Books from ASM Press MicrobeWorld Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Janda, W M Articles by Bohnhoff, M Search for Related Content PubMed PubMed Citation Articles by Janda, W M Articles by Bohnhoff, M

 Previous Article  |  Next Article 

J Clin Microbiol. 1984 March; 19(3): 338-341

Use of the API NeIdent system for identification of pathogenic Neisseria spp. and Branhamella catarrhalis.

ABSTRACT

The API NeIdent system (Analytab Products, Plainview, N.Y.) was evaluated for identifying Neisseria spp. and Branhamella catarrhalis commonly isolated from clinical specimens. The system identified 90% of 303 Neisseria gonorrhoeae isolates, 71% of 113 Neisseria meningitidis isolates, and 63% of 16 Neisseria lactamica isolates but failed to identify any of 22 B. catarrhalis isolates. Testing of gonococcal strains of various auxotypes revealed no relationship between nutritional requirements and NeIdent profile numbers. With the Neisseria species, interpretation of the cinnamaldehyde-coupled beta-naphthylamine reactions was difficult and resulted in profile numbers not listed in the Profile Register. Positive resazurin-glucose reactions resulted in unlisted numbers for all B. catarrhalis strains. Inconsistent results were also obtained when 62 N. gonorrhoeae isolates were tested more than once on the strip. In all cases, profile variability and failure to identify these organisms were related to the beta-naphthylamide substrate tests. Expansion of the data base and modification of the substrate formulations or their interpretive criteria may increase the reliability of the NeIdent system for identifying Neisseria spp. and B. catarrhalis.

This article has been cited by other articles:

• Lancashire, L., Schmid, O., Shah, H., Ball, G. (2005). Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis. Bioinformatics 21: 2191-2199 [Abstract] [Full Text]