Effect of Two Plant Species, Flax (Linum usitatissinum L.) and Tomato (Lycopersicon esculentum Mill.), on the Diversity of Soilborne Populations of Fluorescent Pseudomonads

Suppression of soilborne disease by fluorescent pseudomonads may be inconsistent. Inefficient root colonization by the introduced bacteria is often responsible for this inconsistency. To better understand the bacterial traits involved in root colonization, the effect of two plant species, flax (Linum usitatissinum L.) and tomato (Lycopersicon esculentum Mill.), on the diversity of soilborne populations was assessed. Fluorescent pseudomonads were isolated from an uncultivated soil and from rhizosphere, rhizoplane, and root tissue of flax and tomato cultivated in the same soil. Species and biovars were identified by classical biochemical and physiological tests. The ability of bacterial isolates to assimilate 147 different organic compounds and to show three different enzyme activities was assessed to determine their intraspecific phenotypic diversity. Numerical analysis of these characteristics allowed the clustering of isolates showing a high level (87.8%) of similarity. On the whole, the populations isolated from soil were different from those isolated from plants with respect to their phenotypic characteristics. The difference in bacteria isolated from uncultivated soil and from root tissue of flax was particularly marked. The intensity of plant selection was more strongly expressed with flax than with tomato plants. The selection was, at least partly, plant specific. The use of 10 different substrates allowed us to discriminate between flax and tomato isolates. Pseudomonas fluorescens biovars II, III, and V and Pseudomonas putida biovar A and intermediate type were well distributed among the isolates from soil, rhizosphere, and rhizoplane. Most isolates from root tissue of flax and tomato belonged to P. putida bv. A and to P. fluorescens bv. II, respectively. Phenotypic characterization of bacterial isolates was well correlated with genotypic characterization based on repetitive extragenic palindromic PCR fingerprinting

Potential Impact of the VITEK 2 System and the Advanced Expert System on the Clinical Laboratory of a University-Based Hospital

A study was designed to assess the impact of the VITEK 2 automated system and the Advanced Expert System (AES) on the clinical laboratory of a typical university-based hospital. A total of 259 consecutive, nonduplicate isolates of Enterobacteriaceae members, Pseudomonas aeruginosa, and Staphylococcus aureus were collected and tested by the VITEK 2 system for identification and antimicrobial susceptibility testing, and the results were analyzed by the AES. The results were also analyzed by a human expert and compared to the AES analyses. Among the 259 isolates included in this study, 245 (94.6%) were definitively identified by VITEK 2, requiring little input from laboratory staff. For 194 (74.9%) isolates, no inconsistencies between the identification of the strain and the antimicrobial susceptibility determined by VITEK 2 were detected by the AES. Thus, no input from laboratory staff was required for these strains. The AES suggested one or more corrections to results obtained with 65 strains to remove inconsistencies. The human expert thought that most of these corrections were appropriate and that some resulted from a failure of the VITEK 2 system to detect certain forms of resistance. Antimicrobial phenotypes assigned to the strains by the AES for β-lactams, aminoglycosides, quinolones, macrolides, tetracyclines, and glycopeptides were similar to those assigned by the human expert for 95.7 to 100% of strains. These results indicate that the VITEK 2 system and AES can provide accurate information in tests for most of the clinical isolates examined and remove the need for human analysis of results for many. Certain problems were identified in the study that should be remediable with further work on the software supporting the AES

Expert Systems in Clinical Microbiology

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