Enamel matrix derivative, inflammation and soft tissue wound healing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it