Enamel matrix proteins bind to wound matrix proteins and regulate their cell‐adhesive properties
Bibliographic record
Abstract
Enamel matrix proteins (EMP) induce periodontal regeneration and accelerate dermal wound healing, but the cellular mechanisms of these processes are unclear. We investigated the binding of EMP to the wound matrix proteins fibronectin, laminin-1, collagen type I, and collagen type IV and analyzed the interaction of epithelial cells and periodontal ligament fibroblasts (PDLF) with EMP and composite matrices of EMP + fibronectin or EMP + collagen. The adhesion of PDLF to EMP was concentration- and integrin-dependent and did not require de novo protein synthesis. EMP supported PDLF migration. In contrast, keratinocytes did not adhere to EMP if their protein synthesis was blocked. EMP showed concentration-dependent binding of fibronectin, peaking at 100 microg ml(-1) (before the precipitation point) of EMP. Type I collagen binding to EMP peaked at a low (1 microg ml(-1)) and narrow concentration range. Neither laminin-1 nor type IV collagen bound to EMP. Collagen and fibronectin, bound to EMP, showed significantly reduced (> 50%) binding of both epithelial cells and PDLF compared with the equivalent concentration of these proteins alone. PDLF, but not epithelial cell, adhesion was rescued by increasing the EMP concentration. These findings show that EMP binds to wound extracellular matrix proteins and regulates their adhesive properties. Such interactions may favor fibroblast adhesion over epithelial cells, potentially promoting connective tissue regeneration.
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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".