Effects of vancomycin, daptomycin, and tigecycline on coagulase-negative staphylococcus biofilm and bacterial viability within biofilm: an in vitro biofilm model
Bibliographic record
Abstract
Bacteria may hide in a hydrated polysaccharide matrix known as a biofilm. The structure of the bacterial biofilm renders phagocytosis difficult and increases antibiotic resistance. We hypothesized that repeated doses of antibiotics have an effect on bacteria within the biofilm and that it could inhibit or eradicate biofilm formation. Two clinical biofilm-positive coagulase-negative staphylococcus isolates were evaluated. The effects of antibiotics on preformed and nascent biofilm and on bacterial eradication within the biofilm were determined using different doses of vancomycin, daptomycin, and tigecycline for different durations in an in vitro biofilm model. Vancomycin neither penetrated the biofilm nor had any microbicidal effect on bacteria within the biofilm. Daptomycin had a microbicidal effect on bacteria within the biofilm but had no effect on biofilm inhibition and eradication (independent from dose and treatment time). Tigecycline inhibited and eradicated biofilm formation and had a microbicidal effect on bacteria within the biofilm. In conclusion, (i) biofilm formation appeared to be a major barrier to vancomycin activity, (ii) daptomycin had an antimicrobial effect on the bacteria within the biofilm but not on the biofilm burden, and (iii) tigecycline had effects both on bacteria within the biofilm and on biofilm burden. Thus, both tigecycline and daptomycin might be promising candidates for the treatment of biofilm infections.
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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) | 0.001 | 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 | 0.001 |
| 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".