In Vitro Antimicrobial Effect of Cetylpyridinium Chloride on Complex Multispecies Subgingival Biofilm
Bibliographic record
Abstract
Abstract Periodontopathogenic subgingival biofilm is the main etiological agent of periodontitis. Thus, a search for antimicrobials as adjuvant for periodontal treatment in the literature is intense. Cetylpyridinium chloride (CPC) is a well-known antimicrobial agent commonly used in mouthrinses. However, CPC effects on a complex biofilm model were not found over the literature. Therefore, the aim of this manuscript is to evaluate 0.075% CPC antimicrobial properties in a multispecies subgingival biofilm model in vitro. The subgingival biofilm composed by 31 species related to periodontitis was formed for 7 days, using the calgary device. The treatments with CPC and chlorhexidine (CHX) 0.12% (as positive control) were performed 2x/day, for 1 min, from day 3 until the end of experimental period, totaling 8 treatments. After 7 days of biofilm formation, biofilm metabolic activity was evaluated by a colorimetric reaction and biofilms microbial composition by DNA-DNA hybridization. Statistical analysis was performed using ANOVA with data transformed via BOX-COX followed by Dunnett post-hoc. Both CPC and CHX reduced biofilm metabolic activity in 60% and presented antimicrobial activity against 13 different species. Specifically, only CHX reduced levels of F.n. vicentii and P. gingivalis while only CPC reduced A. odontolyticus and A. israelli. CPC was as effective as CHX as antimicrobial through in vitro complex multispecies subgingival biofilm. However, future studies using in vivo models of experimental periodontal disease should be performed to prove such effect.
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".