p38 MAP Kinase-Mediated Odontogenic Differentiation of Dental Pulp Stem Cells
Bibliographic record
Abstract
The multipotent nature of dental pulp stem cells (DPSCs) promises regenerative endodontic potentials. Alterations in microenvironment have been shown to control the differentiation phenotypes of DPSCs. Understanding the biological mechanisms and finding the optimal DPSC differentiation protocols are crucial for successful DPSC engineering strategies in pulp and dentin healing. The aim of this study is to identify the role of p38 mitogen-activated protein kinase (p38) under normal and oxygen-deprived conditions (2%) to reveal its effect on odontogenic DPSC differentiation. Human DPSCs were isolated from healthy molars and underwent odontogenic differentiation in regular and osteogenic media treated with SB203580, a p38 inhibitor, for 72 hours, and then swapped with osteogenic media for 21 days under hypoxic condition. Immunochemistry and PCR analysis for the various odontogenic differentiation genes and proteins were performed. Our PCR data demonstrate that p38 inhibition resulted in a significant upregulation in odontogenic gene expressions such as DMP-1, DSPP, RUNX, and OSX in normal conditions. Under hypoxia, this effect was reversed. These results were further supported by DSPP immunohistochemistry. The DSPP expression under hypoxia was significantly weaker compared to the control. Our results indicate that p38 represents a negative regulator of the odontogenic DPSC differentiation in normoxia. Under hypoxia, p38 exerts a positive function of DPSC differentiation. Taken together, we identified the p38 and oxygen level as crucial factors to control odontogenic DPSC differentiation providing their essential roles in designing for successful pulp-dentin complex engineering strategies.
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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 | 0.000 | 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".