Combination of Neovestitol and Vestitol Modifies the Profile of Periodontitis-Related Subgingival Multispecies Biofilm
Bibliographic record
Abstract
The aim of this study was to evaluate the effect of the combination of neovestitol–vestitol (CNV) compounds obtained from Brazilian red propolis on the microbiological profile of a mature multispecies subgingival biofilm. The biofilm with 32 bacterial species associated with periodontitis was formed for seven days using a Calgary device. Treatment with CNV (1600, 800, 400, and 200 μg/mL), amoxicillin (54 μg/mL), and vehicle control was performed for 24 h on the last day of biofilm formation. Biofilm metabolic activity and DNA–DNA hybridization (checkerboard) assays were performed. The groups treated with CNV 1600 and amoxicillin reduced 25 and 13 species, respectively, compared to the control vehicle treatment (p ≤ 0.05); both reduced P. gingivalis, while only CNV reduced T. forsythia. When the data from the two treatments (CNV and AMOXI) were compared, a statistically significant difference was observed in 13 species, particularly members of Socransky’s orange complex. Our results showed that CNV at 1600 μg/mL showed the best results regarding the metabolic activity of mature biofilms and obtained a reduction in species associated with the disease, such as T. forsythia, showing a better reduction than amoxicillin. Therefore, CNV seems to be a promising alternative to eradicate biofilms and reduce their pathogenicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".