Extracellular Matrix Control of Collagen Mineralization In Vitro
Bibliographic record
Abstract
Abstract Collagen biomineralization is a complex process and the controlling factors at the molecular level are still not well understood. A particularly high level of spatial control over collagen mineralization is evident in the anchorage of teeth to the jawbone by the periodontal ligament. Here, unmineralized ligament collagen fibrils become mineralized at an extremely sharp mineralization front in the root of the tooth. A model of collagen biomineralization based on demineralized cryosections of mouse molars in the bone socket is presented. When exposed to metastable calcium and phosphate‐containing solutions, mineral re‐deposits selectively into the natively mineralized tissues with high fidelity, demonstrating that the extracellular matrix retains sufficient information to control the rate of mineralization at the tissue level. While solutions of simulated bodily fluid produce amorphous calcium phosphate within the tissue section, a more highly supersaturated solution stabilized with polyaspartic acid produces oriented, crystalline calcium phosphate with diffraction patterns consistent with hydroxyapatite. The model thus replicates both spatial control of mineral deposition, as well as the matrix‐mineral relationships of natively mineralized collagen fibrils, and can be used to elucidate roles of specific biomolecules in the highly controlled process of collagen biomineralization. This knowledge will be critical in the design of collagen‐based scaffolds for tissue engineering of hard‐soft tissue interfaces.
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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 | 0.000 | 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 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".