Odontogenic differentiation of dental pulp stem cells by glycogen synthase kinase-3β inhibitory peptides
Bibliographic record
Abstract
BACKGROUND: To investigate the effects of peptide-based substrate competitive inhibitors of GSK-3β (GSK-3βi) on promoting odontogenic differentiation of human dental pulp stem cells (hDPSCs). METHODS: The biocompatibility and proliferation of hDPSCs treated with GSK-3βi peptides (pS9, LRP 6a, L803, and L803-mts) were evaluated using the tetrazolium reduction assay and cell counting kit-8 assay, respectively. The differentiation of hDPSCs following peptide treatment was determined using the alkaline phosphatase assay (ALP), calcium mineralization (alizarin red staining), and quantification of mRNA expression of differentiation markers via quantitative real-time polymerase chain reaction. The accumulation of β-catenin in the nucleus of GSK3-βi-treated hDPSCs was determined using immunofluorescence staining. The effect of peptide treatment on hDPSC migration was characterized using the transwell assay. RESULTS: All tested concentrations of the peptides were found to be biocompatible with the hDPSCs, with no significant difference compared to the control (p > 0.05). The peptides had no effect on the proliferation of hDPSCs compared to the control (p > 0.05). However, all the tested peptides significantly increased ALP activity and calcium deposition in a dose-dependent manner (p < 0.05). Specifically, L803-mts showed significantly greater ALP activity and mineralization compared to the other peptides and the controls (p < 0.05). Additionally, L803-mts showed a significant increase (p < 0.05) in the expression of DSPP, DMP-1, Runx-2, along with increased protein expression of DSPP and DMP-1 compared to the control. Furthermore, it enhanced the nuclear translocation of β-catenin and increased the chemotactic migratory potential of hDPSCs. CONCLUSIONS: L803-mts, a peptide-based substrate competitive inhibitor of GSK-3β, enhanced the odontogenic differentiation of hDPSCs by activating the Wnt signaling pathway.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".