Effects of Plants on Osteogenic Differentiation and Mineralization of Periodontal Ligament Cells: A Systematic Review
Bibliographic record
Abstract
This systematic review aimed to evaluate the effects of plants on osteogenic differentiation and mineralization of human periodontal ligament cells. The included studies were selected using five different electronic databases. The reference list of the included studies was crosschecked, and a partial gray literature search was undertaken using Google Scholar and ProQuest. The methodology of the selected studies was evaluated using GRADE. After a two-step selection process, eight studies were identified. Six different types of plants were reported in the selected studies, which were Morinda citrifolia, Aloe vera, Fructus cnidii, Zanthoxylum schinifolium, Centella asiatica, and Epimedium species. They included five types of isolated plant components: acemannan, osthole, hesperetin, asiaticoside, and icariin. In addition, some active substances of these components were identified as polysaccharides, coumarins, flavonoids, and triterpenes. The studies demonstrated the potential effects of plants on osteogenic differentiation, cell proliferation, mineral deposition, and gene and protein expression. Four studies showed that periodontal ligament cells induce mineral deposition after plant treatment. Although there are few studies on the subject, current evidence suggests that plants are potentially useful for the treatment of periodontal diseases. However, further investigations are required to confirm the promising effect of these plants in regenerative treatments.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".