Mineralization of Dense Collagen Hydrogel Scaffolds by Human Pulp Cells
Bibliographic record
Abstract
While advances in biomineralization have been made in recent years, unanswered questions persist on bone- and tooth-cell differentiation, on outside-in signaling from the extracellular matrix, and on the link between protein expression and mineral deposition. In the present study, we validate the use of a bioengineered three-dimensional (3D) dense collagen hydrogel scaffold as a cell-culture model to explore these questions. Dental pulp progenitor/stem cells from human exfoliated deciduous teeth (SHEDs) were seeded into an extracellular matrix-like collagen gel whose fibrillar density was increased through plastic compression. SHED viability, morphology, and metabolic activity, as well as scaffold mineralization, were investigated over 24 days in culture. Additionally, measurements of alkaline phosphatase enzymatic activity, together with immunoblotting for mineralized tissue cell markers ALPL (tissue-non-specific alkaline phosphatase), DMP1 (dentin matrix protein 1), and OPN (osteopontin), demonstrated osteo/odontogenic cell differentiation in the dense collagen scaffolds coincident with mineralization. Analyses of the mineral phase by electron microscopy, including electron diffraction and energy-dispersive x-ray spectroscopy, combined with Fourier-transform infrared spectroscopy and biochemical analyses, were consistent with the formation of apatitic mineral that was frequently aligned along collagen fibrils. In conclusion, use of a 3D dense collagen scaffold promoted SHED osteo/odontogenic cell differentiation and mineralization.
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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".