Recombinant growth/differentiation factor-5 enhances cell proliferation and tendogenic differentiation of human periodontal ligament stem cells in vitro.
Bibliographic record
Abstract
e22191 Background: To investigate the function of growth/differentiation factor-5 (GDF-5) on cell proliferation and tendogenic differentiation of human periodontal ligment stem cells (PDLSCs) in vitro. Methods: PDLSCs were obtained from human healthy periodontal ligaments of wisdom teeth. Immunofluorescence cytochemistry was used to detect the characters of mesenchymal stem cells (MSCs) in PDLSCs. Recombinant mouse GDF-5 (rmGDF-5) protein was used to stimulate the PDLSCs. Tetrazolium reduction assay was carried out for cell proliferation. Alkaline phosphatase (ALP) activity was measured by ALP activity assay and ALP staining. Real-time RT-PCR was performed to detect the gene expressions of aggrecan (ACAN), collagen, type III (COL3), scleraxis (SCX), decorin (DCN), tenomodulin (TNMD) and tenascin C (TNC) in PDLSCs after stimulated by rmGDF-5 protein. Results: 5.6% STRO-1 positive cells was detected in primary PDLSCs. The cell proliferation of PDLSCs was enhanced by rmGDF-5 in a dose-dependent manner at 0, 20, 100 and 500 ng/ml of concentration. The special markers of tendogenic differention, ACAN, COL3, SCX and TNMD were up-regulated at mRNA level after rmGDF-5 treatment for 1, 2 or 3 weeks, and the 20 ng/ml was the more effective than 100 ng/ml and 500 ng/ml for rmGDF-5 in PDLSCs. The expressions of TNC and DCN were increased at 4 or 8 hours after 20, 50 and 100 ng/m rmGDF-5 treatment. The ALP activity results showed that ALP activities were decreased paralleled with increased rmGDF-5 concentration. Conclusions: The rmGDF-5 could promote the cell proliferation and tendogenic differentiation of PDLSCs in vitro.
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) | 0.002 | 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".