Effect of carbon on whole-biofilm metabolic response to high doses of streptomycin
Bibliographic record
Abstract
Biofilms typically exist as complex communities comprising multiple species with the ability to adapt to a variety of harsh conditions. In clinical settings, antibiotic treatments based on planktonic susceptibility tests are often ineffective against biofilm infections. Using a CO2 evolution measurement system we delineated the real-time metabolic response in continuous flow biofilms to streptomycin doses much greater than their planktonic susceptibilities. Stable biofilms from a multispecies culture (containing mainly Pseudomonas aeruginosa and Stenotrophomonas maltophilia), Gram-negative environmental isolates, and biofilms formed by pure culture P. aeruginosa strains PAO1 and PAO1 ΔMexXY (minimum planktonic inhibitory concentrations between 1.5 and 3.5 mg/l), were exposed in separate experiments to 4000 mg/l streptomycin for 4 h after which growth medium resumed. In complex medium, early steady state multispecies biofilms were susceptible to streptomycin exposure, inferred by a cessation of CO2 production. However, multispecies biofilms survived high dose exposures when there was extra carbon in the antibiotic medium, or when they were grown in defined citrate medium. The environmental isolates and PAO1 biofilms showed similar metabolic profiles in response to streptomycin; ceasing CO2 production after initial exposure, with CO2 levels dropping toward baseline levels prior to recovery back to steady state levels, while subsequent antibiotic exposure elicited increased CO2 output. Monitoring biofilm metabolic response in real-time allowed exploration of conditions resulting in vulnerability after antibiotic exposure compared to the resistance displayed following subsequent exposures.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".