EGCG and Taxifolin Modulate Secretory Activity and Expression of Dentinogenesis Markers in Odontoblast‐Like Cells
Bibliographic record
Abstract
Odontoblasts are cells specialized in dentin matrix deposition and the first line of defense when the dentin–pulp complex is injured by pathological processes, such as dental caries and trauma. Natural compounds, such as flavonoids, could be useful to stimulate odontoblast activity and reparative dentinogenesis in vital pulp therapies, especially in immature permanent teeth. This study evaluated the effect of flavonoids on odontoblast secretory activity and the expression of dentinogenesis markers. The effect of flavonoids was evaluated on phenotypic mineralization markers (alkaline phosphatase (ALP) activity and mineralized nodule deposition) by colorimetric assays and on the expression of Alpl , Mmp2 , Mmp9 , Dmp1 , and Dspp genes in odontoblast‐like cells by quantitative polymerase chain reaction. Most of the flavonoids did not show toxicity between 100 and 25 μM. In distinct concentrations, epigallocatechin gallate (EGCG), taxifolin, myricetin, quercetin, and kaempferol stimulated the activity of ALP and increased mineralized nodule deposition. However, the highest effect on those phenotypic markers was observed after EGCG and taxifolin treatments. Then, they were selected for evaluation of gene expression. mRNA levels of Dmp1 and Dspp highly increased with taxifolin treatment, and Alpl expression was increased for both taxifolin and EGCG groups, without difference between them. Mmp2 and Mmp9 expression was not affected by these flavonoids. In conclusion, EGCG and taxifolin positively affect phenotypic mineralization markers; in particular, taxifolin highly stimulates early‐ and late‐stage dentinogenesis genes.
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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) | 0.001 | 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".