Latest update on the use of recombinant growth factors for periodontal regeneration: existing evidence and clinical applications
Bibliographic record
Abstract
Growth factors were introduced to increase predictability in periodontal regeneration and have since been widely applied in dentistry. This narrative review article highlights histological and latest findings of recombinant human platelet-derived growth factor-BB (rhPDGF-BB) and recombinant human fibroblast growth factor-2 (rhFGF-2) for periodontal regeneration. rhPDGF-BB enhances the proliferation and chemotaxis of periodontal ligament and alveolar bone cells. The optimal dose for rhPDGF-BB, in combination with beta-tricalcium phosphate, is 0.3 mg/ml. It is approved in the United States, Canada, and Taiwan for use in periodontal regeneration and treatment of gingival recession. rhFGF-2 promotes periodontal wound healing through mitogenic and angiogenic effects on mesenchymal cells in the periodontal ligament. It is approved in Japan at an optimal dose of 0.3% for periodontal regeneration in intrabony defects. Both recombinant growth factors show histological evidence of new bone, cementum, and periodontal ligament. Clinical studies demonstrate improved clinical attachment levels and defect resolution for treating intrabony and furcation periodontal defects. Presented clinical cases and consensus reports may serve as a reference for clinicians. rhPDGF-BB and rhFGF-2 are safe and effective biologics that can be applied to improve the outcomes of periodontal 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".