Enamel matrix derivative, inflammation and soft tissue wound healing
Bibliographic record
Abstract
Over 15 years have now passed since enamel matrix derivative (EMD) emerged as an agent capable of periodontal regeneration. Following thorough investigation, evidenced-based clinical application is now established for a multitude of clinical settings to promote regeneration of periodontal hard tissues. Despite the large number of studies and review articles written on this topic, no single review has compiled the influence of EMD on tissue inflammation, an area of research that merits substantial attention in periodontology. The aim of the present review was to gather all studies that deal with the effects of EMD on tissue inflammation with particular interest in the cellular mechanisms involved in inflammation and soft tissue wound healing/resolution. The effects of EMD on monocytes, macrophages, lymphocytes, neutrophils, fibroblasts and endothelial cells were investigated for changes in cell behavior as well as release of inflammatory markers, including interleukins, prostaglandins, tumor necrosis factor-α, matrix metalloproteinases and members of the OPG-RANKL pathway. In summary, studies listed in this review have reported that EMD is able to significantly decrease interleukin-1b and RANKL expression, increase prostaglandin E2 and OPG expression, increase proliferation and migration of T lymphocytes, induce monocyte differentiation, increase bacterial and tissue debris clearance, as well as increase fibroplasias and angiogenesis by inducing endothelial cell proliferation, migration and capillary-like sprout formation. The outcomes from the present review article indicate that EMD is able to affect substantially the inflammatory and healing responses and lay the groundwork for future investigation in the field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".