17β-Estradiol induces odontoblastic differentiation via activation of the c-Src/MAPK pathway in human dental pulp cells
Bibliographic record
Abstract
The present study is aimed at investigating the effects of the exogenous estrogen 17β-estradiol (E2) on odontoblastic differentiation in human dental pulp cells (HDPCs) immotalized with hTERT gene and their molecular mechanism. Proliferation was detected by BrdU assay, and odontoblast differentiation induction was evaluated by the expression of dentin sialophosphoprotein (DSPP), dentin sialoprotein (DSP) and dentin matrix protein1 (DMP1), and alkaline phosphatase (ALP) activity and mineralization. Estrogen receptor-α (ER-α), c-Src, and mitogen-activated protein kinases (MAPKs) were examined and their inhibitors were used to determine the roles on odontogenic induction. E2 significantly promoted the HDPC proliferation, which was mediated by extracellular signal-related kinase 1/2. E2 upregulated DSPP, DSP, and DMP1 as the odontogenic differentiation markers and enhanced ALP activity and mineralization. E2 increased phosphorylation of ER-α and fulvestrant, an ER downregulator, significantly downregulated DSPP, DMP1, and DSP induced by E2. Moreover, E2 treatment activated c-Src and MAPKs upon odontogenic induction, whereas chemical inhibition of c-Src and MAPKs decreased expression of DSPP, DMP1, and DSP and mineralization augmented by E2. Moreover, fulvestrant reduced E2-induced phosphorylation of c-Src and MAPK and inhibition of c-Src by PP2 attenuated activation of MAPKs during E2-induced odontoblastic differentiation. Taken together, these results indicated that E2 stimulates odontoblastic differentiation of HDPCs via coordinated regulation of ER-α, c-Src, and MAPK signaling pathways, which may play a key role in the regeneration of dentin.
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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".