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J Appl Microbiol. 2003;95(1):160-6.

Development of a Gram-negative selective agar (GNSA) for the detection of Gram-negative microflora in sputa in patients with cystic fibrosis.

Author information

1
Northern Ireland Public Health Laboratory, Department of Bacteriology, Belfast City Hospital, Belfast, UK. jemoore@niphl.co.uk

Abstract

AIMS:

To develop a selective agar medium to help detect and quantify Gram-negative flora in the sputum of patients with cystic fibrosis (CF).

METHODS AND RESULTS:

A novel Gram-negative Selective Agar (GNSA) medium was developed consisting of tryptone soya broth (30 g), bacteriological agar no.1 (10 g), yeast extract (5 g), crystal violet (2 mg), nisin (48 mg), novobiocin (5 mg), cycloheximide (100 mg), amphotericin (2 mg) and double distilled water (1 l), for the selective culture of all Gram-negative flora from the sputum of patients with CF. GNSA was able to support the proliferation of all 34 Gram-negative organisms examined, including 23 species most commonly associated with CF, but was unable to support the growth of the 12 Gram-positive or seven fungal organisms examined. Sensitivity studies demonstrated that the GNSA medium was able to detect not less than 1.50 x 102 CFU ml-1 sputum Pseudomonas aeruginosa, 2.38 x 102 CFU ml-1 sputum Burkholderia cepacia genomovar IIIb and 6.70 x 103 CFU ml-1 sputum Stenotrophomonas maltophilia. A comparison of the microbial flora detected in the sputa of 12 adult CF patients by employment of routine bacteriological agar media and GNSA, demonstrated that GNSA was able to detect all Gram-negative organisms cultured by routine media, but had the advantage of detecting Alcaligenes xylosoxidans in two CF patients, whom had no previous history of Gram-negative infection.

CONCLUSIONS:

GNSA was unable to support the proliferation of any Gram-positive organism or yeast/fungi, but was successful in supporting the growth of all Gram-negative organisms challenged.

SIGNIFICANCE AND IMPACT OF THE STUDY:

Employment of this medium coupled with semi-automated technology may aid in helping to efficiently determine Gram-negative loading of respiratory secretions, particularly in response to antibiotic intervention.

PMID:
12807467
[Indexed for MEDLINE]
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