Evaluation of the impact of tromethamine edetate disodium dihydrate on antimicrobial susceptibility of <i><scp>P</scp>seudomonas aeruginosa</i> in biofilm <i>in vitro</i>
Bibliographic record
Abstract
BACKGROUND: Biofilm formation by Pseudomonas aeruginosa has been documented in canine otic isolates. An increase in minimal inhibitory concentration (MIC) for specific antibiotics has been noted for biofilm-embedded bacteria. Tromethamine edetate disodium dihydrate buffered to pH 8 with tromethamine hydrochloride and deionized water (Triz-EDTA(®)) has been documented to potentiate bactericidal activity when used in combination with topical antibiotics, but the impact on biofilm-embedded bacteria is unknown. HYPOTHESIS/OBJECTIVES: The objective of this study was to evaluate the impact of Triz-EDTA(®) use on in vitro antimicrobial susceptibility of biofilm-embedded P. aeruginosa. METHODS: Biofilm formation was documented using a microtitre plate assay. Broth microdilution was used to assess the MIC of neomycin, polymyxin B, enrofloxacin and gentamicin for the biofilm-embedded bacteria. The microtitre plate assay was again used to assess the MIC of neomycin, polymyxin B, enrofloxacin and gentamicin for biofilm-embedded bacteria with added Triz-EDTA(®). RESULTS: Thirty-one isolates from dogs with otitis were tested. Addition of Triz-EDTA(®) significantly reduced MICs for neomycin (P < 0.003) and gentamicin (P < 0.02) but not for polymyxin B (P = 0.3). Enrofloxacin MICs increased in the presence of Triz-EDTA (P < 0.036). CONCLUSIONS AND CLINICAL IMPORTANCE: Triz-EDTA(®) may be a useful adjunctive treatment for chronic cases of Pseudomonas otitis where biofilms may have developed, if gentamicin or neomycin is to be used as a topical treatment. In vivo study is required to confirm this effect.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".