PubMed HealthSearch

PubMed · 389945

Nonfermentative bacilli: evaluation of three systems for identification.

Abstract

Three systems for the identification of nonfermentative bacilli were evaluated for their rapidity and accuracy of identification of 217 strains. Two of the systems, API 20E (API) and Oxi/Ferm tube (OxiF), are available as kits; the oxidative attack (OA) system is not commerically available. The overall accuracies of the OA, API, and OxiF systems were 91, 69, and 50%, respectively. Identification within 48 h was achieved for 98% of the strains by OA, for 50% by API, and for 18% by OxiF. Most of the organisms that were either misidentified or not identified by API and OxiF were those nonfermentative bacilli which are relatively more fastidious or rarely encountered or both. All three systems accurately identified nonfermentative bacilli commonly isolated at Olive View Medical Center, namely, Pseudomonas aeruginosa, Acinetobacter anitratus, Pseudomonas maltophilia, Acinetobacter lwoffi, saccharolytic flavobacteria (CDC IIb), moraxellae, Pseudomonas fluorescens, and Pseudomonas putida. The OA system identified 100% of the above organisms correctly, API identified 99.4%, and OxiF identified 99.3%. Since these organisms comprise 92% of the total number of nonfermentative bacilli isolated at Olive View Medical Center, we conclude that both API and OxiF may be useful alternatives to conventional methods, based on accuracy of identification alone. These two systems were considered substantially inferior to the OA system when both accuracy and rapidity of identification were taken into account.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

L A Otto, U Blachman. 1979. Nonfermentative bacilli: evaluation of three systems for identification.. https://doi.org/10.1128/jcm.10.2.147-154.1979

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Collateral sensitivity-harnessing microbial vulnerabilities as a solution to antimicrobial resistance.

Bacteria exhibit an evolutionary trade-off through their development of collateral sensitivity (CS) which allows them to resist one antibiotic while becoming more vulnerable to another. This vulnerability offers a compelling therapeutic opportunity by selecting against resistant isolates. Laboratory evolution studies, genome sequencing, deep mutagenesis and use of artificial intelligence and machine learning can design the bespoke strategy against multi-drug-resistant bacteria. This review discusses about recent studies that are rationally designed to harness this evolutionary trade-off for the development of alternative antimicrobial strategies. The translational barriers to the clinical implementation of CS are addressed and evidence-based design principles for optimization of CS-guided therapy are discussed.

Bacteria

How the social lives of bacteria affect their pangenome.

Although the study of microbes started with type strains and reference genomes, advances in sequencing technology and new interest in mixed microbial communities have made us aware that a single genome cannot and does not reflect the diversity of a given bacterial species. Bacteria rarely occupy an environmental or host niche alone and quickly diversify into strains upon colonization of a new niche. The genetic diversity present within a phylogenetically related set of bacterial strains (the 'pangenome') is influenced by the niche that they occupy and how they interact with the other microorganisms that they share that niche with. In this review, I examine how the social lives of bacteria can affect their genetic diversity and the bioinformatic techniques that we use to detect that diversity.

Bacteria

DURABLE: A Workflow for Determining Corrosion-Driving and Protective Microbial Mechanisms.

Microbiologically influenced corrosion (MIC) threatens global infrastructure, causing billions of dollars in annual losses. Its persistence stems from unresolved mechanisms─particularly the metabolites produced by microorganisms that drive or inhibit corrosion─and the microbial community structures. Progress has been hindered by the absence of systematic workflows to rapidly and accurately identify MIC-relevant microorganisms and their functions. Here, we present DURABLE (Detection of Unique Corrosion Resistant or Accelerating Biologics in a Laboratory Environment), a pipeline that couples high-throughput microbial screening with genomic and metabolic workflows. We applied the DURABLE workflow to six diesel tank samples and revealed fuel-dependent microbial community structures, which showed greater diversity and evenness in bacterial communities than their fungal counterparts. The workflow used carbon steel beads to rapidly screen over 80 bacterial isolates for corrosive activity, reducing assay time to approximately 2 days compared with the conventional 30-day metal coupon test. More than 40 isolates were identified as corrosive. Further testing using mass spectrometry analysis revealed corrosion-associated metabolites, which were further validated using electrochemical assays. Thus, DURABLE achieved a ∼15-fold increase in screening speed and provided a scalable and mechanistic framework for dissecting MIC dynamics. We expect this advance will enable the development of precision mitigation strategies in hydrocarbon fuel infrastructure.

Bacteria