Emdogain<sup>®</sup> regulation of cellular differentiation in wounded rat periodontium
Bibliographic record
Abstract
Emdogain is an enamel matrix derivative that may promote periodontal regeneration by recapitulating critical events in tooth morphogenesis. We hypothesized that Emdogain enhances periodontal regeneration by promoting the differentiation of cells required for the synthesis of periodontal ligament, bone and cementum. Cell differentiation was examined in rat periodontal window wounds in which there is no microbial biofilm or epithelial downgrowth, thereby simplifying the model system. Defects were filled with vehicle control or Emdogain (3 mg/ml or 30 mg/ml). Rats were sacrificed at 7, 14 and 21 d after wounding. Specimens of periodontium were immunostained for osteopontin, bone sialoprotein, osteocalcin as markers of osteogenic differentiation and for alpha-smooth muscle actin, a myofibroblastic marker. Morphometry and 3H-proline radioautography were used for assessment of tissue homeostasis and matrix production. Rats treated with Emdogain (only at 30 mg/ml) showed widening of the periodontal ligament at 7 d; by 14 and 21 d, periodontal ligament width was restored to normal values for all groups. Emdogain exerted no effect on cementum thickness, bone volume, osteoid deposition rates, or extracellular staining for osteopontin, bone sialoprotein or osteocalcin. Further, the percentage of cells with intracellular staining for osteopontin, osteocalcin or bone sialoprotein was unaffected by Emdogain. Staining for alpha-smooth muscle actin was abundant in the repopulating wound but was also unaffected by Emdogain. In conclusion, Emdogain does not apparently affect the expression of differentiation markers or bone matrix protein synthesis in the repopulation response of wounded rat molar periodontium. Therefore the effect of Emdogain on wound healing in the periodontium may be independent of differentiation in the cell populations examined in this model.
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".