The plant coumarins auraptene and lacinartin as potential multifunctional therapeutic agents for treating periodontal disease
Bibliographic record
Abstract
BACKGROUND: Periodontal diseases are bacterial infections leading to chronic inflammation disorders that are frequently observed in adults. In the present study, we evaluated the effect of auraptene and lacinartin, two natural oxyprenylated coumarins, on the growth, adherence properties, and collagenase activity of Porphyromonas gingivalis. We also investigated the capacity of these compounds to reduce cytokine and matrix metalloproteinase (MMP) secretion by lipopolysaccharide (LPS)-stimulated macrophages and to inhibit MMP-9 activity. METHODS: Microplate dilution assays were performed to determine the effect of auraptene and lacinartin on P. gingivalis growth as well as biofilm formation stained with crystal violet. Adhesion of FITC-labeled P. gingivalis to oral epithelial cells was monitored by fluorometry. The effects of auraptene and lacinartin on LPS-induced cytokine and MMP secretion by macrophages were determined by immunological assays. Fluorogenic assays were used to evaluate the capacity of the two coumarins to inhibit the activity of P. gingivalis collagenase and MMP-9. RESULTS: Only lacinartin completely inhibited P. gingivalis growth in a complex culture medium. However, under iron-limiting conditions, auraptene and lacinartin both inhibited the growth of P. gingivalis. Lacinartin also inhibited biofilm formation by P. gingivalis and promoted biofilm desorption. Both compounds prevented the adherence of P. gingivalis to oral epithelial cells, dose-dependently reduced the secretion of cytokines (IL-8 and TNF-α) and MMP-8 and MMP-9 by LPS-stimulated macrophages, and inhibited MMP-9 activity. Lacinartin also inhibited P. gingivalis collagenase activity. CONCLUSIONS: By acting on multiple targets, including pathogenic bacteria, tissue-destructive enzymes, and the host inflammatory response, auraptene and lacinartin may be promising natural compounds for preventing and treating periodontal diseases.
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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) | 0.000 | 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) | 0.002 | 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".