Monitoring biofilm Co2 production in response to antibiotic exposures
Bibliographic record
Abstract
Bacteria commonly exist in nature as biofilms which help protect them from harsh environmental conditions, thus increasing their ability to adapt and survive. In clinical settings antibiotic treatments based on planktonic susceptibility tests are ineffective against biofilm infections, as biofilms can withstand much higher antibiotic concentrations. To further elucidate biofilm behaviour, multispecies and pure culture biofilms were grown in a CO2 evolution measurement system (CEMS) that tracks whole-biofilm CO2 production non-destructively and in real-time. In addition, metagenomic analysis was performed on multispecies cultures to monitor community dynamics and the intrinsic potential in different physiological states. Multispecies biofilms grown in CEMS were exposed to aminoglycosides under various environmental conditions (e.g. nutrient and pH variations). Multispecies biofilms were most susceptible to high-dose streptomycin exposures when grown for less than 48 hours (young biofilm). However, carbon addition to the antibiotic medium decreased young biofilm susceptibility to streptomycin. Furthermore, young biofilms were susceptible to 4000 mg/1doses of streptomycin and gentamicin at pH6.0 and to 4000 mg/1 doses of streptomycin at pH 7.4. Biofilms were least susceptible to the aminoglycosides at neutral pH. Metagenomic analysis on the multispecies biofilms revealed the presence of aminoglycoside-modifying enzymes and that the cultures contained Pseudomonas aeruginosa and Stenotrophomonas maltophilia strains. The dominant strain in planktonic cultures was P.aeruginosa. However, in complex growth medium, S. maltophilia increased in proportion upon biofilm development and became the dominant species following a high-dose streptomycin exposure. The monitoring of biofilm metabolic responses revealed adding carbon to the antibiotic medium or exposing the biofilms to aminoglycosides at neutral pH reduces biofilm aminoglycoside susceptibility. In addition, metagenomic analysis uncovered community composition and genes that can play a role in biofilm survival to the antibiotics. However, even with the presence of antibiotic resistance genes, carbon and pH play an important role in biofilm survival to high-dose antibiotic 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".