Brazilian Red Propolis Is as Effective as Amoxicillin in Controlling Red-Complex of Multispecies Subgingival Mature Biofilm In Vitro
Bibliographic record
Abstract
This study investigated the effects of Brazilian Red Propolis (BRP) extract on seven-day-old multispecies subgingival biofilms. Mixed biofilm cultures containing 31 species associated with periodontal health or disease were grown for six days on a Calgary device. Then, mature biofilms were treated for 24 h with BRP extract at different concentrations (200–1600 µg/mL), amoxicillin (AMOXI) at 54 µg/mL (positive control) or vehicle (negative control). Biofilm metabolic activity was determined by colorimetry, and bacterial counts/proportions were determined by DNA–DNA hybridization. Data were analyzed by Kruskal–Wallis and Dunn’s tests. Treatment with BRP at 1600, 800 and 400 μg/mL reduced biofilm metabolic activity by 56%, 56% and 57%, respectively, as compared to 65% reduction obtained with AMOXI. Mean total cell counts were significantly reduced in all test groups (~50–55%). Lower proportions of red, green and yellow complex species were observed upon treatment with BRP (400 µg/mL) and AMOXI, but only AMOXI reduced the proportions of Actinomyces species. In conclusion, BRP extract was as effective as AMOXI in killing seven-day-old multispecies biofilm pathogens and did not affect the levels of the host-compatible Actinomyces species. These data suggest that BRP may be an alternative to AMOXI as an adjunct in periodontal therapy. In vivo studies are needed to validate these results.
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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) | 0.000 | 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".