Effects of Azithromycin, Metronidazole, Amoxicillin, and Metronidazole plus Amoxicillin on an<i>In Vitro</i>Polymicrobial Subgingival Biofilm Model
Bibliographic record
Abstract
Chronic periodontitis is one of the most prevalent human diseases and is caused by dysbiosis of the subgingival microbiota. Treatment involves primarily mechanical disruption of subgingival biofilms and, in certain cases, adjunctive use of systemic antibiotic therapy. In vitro biofilm models have been developed to study antimicrobial agents targeting subgingival species. However, these models accommodate a limited number of taxa, lack reproducibility, and have low throughput. We aimed to develop an in vitro multispecies biofilm model that mimics subgingival plaque, to test antimicrobial agents. Biofilms were cultivated using the Calgary Biofilm Device and were exposed to amoxicillin (AMX), metronidazole (MTZ), azithromycin (AZM), and AMX-MTZ at four different concentrations for 12, 24, or 36 h. Chlorhexidine (CHX) (0.12%) was used as the positive control. The compositions of the biofilms were analyzed by checkerboard DNA-DNA hybridization, and the percent reduction in biofilm metabolic activity was determined using 2,3,5-triphenyltetrazolium chloride and spectrophotometry. Thirty-five of the 40 species used in the inoculum were consistently recovered from the resulting in vitro biofilms. After 36 h of exposure at the 1:27 dilution, AMX-MTZ reduced metabolic activity 11% less than CHX (q = 0.0207) but 54% more than AMX (q = 0.0031), 72% more than MTZ (q = 0.0031), and 67% more than AZM (q = 0.0008). Preliminary evidence of a synergistic interaction between AMX and MTZ was also observed. In summary, we developed reproducible biofilms with 35 subgingival bacterial species, and our results suggested that the combination of AMX and MTZ had greater antimicrobial effects on these in vitro multispecies biofilms than expected on the basis of the independent effects of the drugs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".