On-demand chlorine dioxide solution enhances odontoblast differentiation through desulfation of cell surface heparan sulfate proteoglycan and subsequent activation of canonical Wnt signaling
Bibliographic record
Abstract
Heparan sulfate proteoglycans (HSPGs) surround the surface of odontoblasts, and their modification affects their affinity for Wnt ligands. This study proposes applying Matching Transformation System® (MA-T), a novel chlorinated oxidant, to enhance dentinogenesis. MA-T treatment in odontoblasts decreased sulfation of HSPG and upregulated the expression of dentin sialophosphoprotein (Dspp) and Dentin Matrix Protein 1 (Dmp1) via activation of canonical Wnt signaling in vitro. Ex vivo application of MA-T also enhanced dentin matrix formation in developing tooth explants. Reanalysis of a public single-cell RNA-seq dataset revealed significant Wnt activity in the odontoblast population, with enrichment for Wnt10a and Wnt6. Silencing assays showed that Wnt10a and Wnt6 were redundant in inducing Dspp and Dmp1 mRNA expression. These Wnt ligands’ expression was upregulated by MA-T treatment, and TCF/LEF binding sites are present in their promoters. Furthermore, the Wnt inhibitors Notum and Dkk1 were enriched in odontoblasts, and their expression was also upregulated by MA-T treatment, together suggesting autonomous maintenance of Wnt signaling in odontoblasts. This study provides evidence that MA-T activates dentinogenesis by modifying HSPG and through subsequent activation of Wnt signaling.
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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) | 0.001 | 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".