"Inhibition of mineralization in bones and teeth following ectopic expression of matrix Gla protein"
Bibliographic record
Abstract
Matrix Gla Protein (MGP) inhibits mineralization of arteries and cartilage. To determine its mineral-inhibiting potential in hard tissues, MGP was ectopically expressed in bones and teeth of mice using an osteoblast/odontoblast-specific 2.3kb proximal promoter for type 1 collagen. Mandibles and long bones of Col1a1-Mgp mice and wild-type littermates were analyzed by Faxitron(TM) radiography, PIXImus(TM) dual-energy x-ray absorptiometry (DEXA) and micro-computed tomography (mu-CT). In addition, light microscopy (LM) and transmission electron microscopy (TEM) were performed. While bone and tooth extracellular matrices (ECMs) appeared normally established in Col1a1-Mgp mice, examination of the mineral phase by radiography, DEXA and mu-CT, together with histological mineral localization by LM after von Kossa staining of undecalcified tissue sections, and by TEM, revealed massive hypomineralization of bone and tooth ECMs. In the skeleton of Col1a1-Mgp mice, alveolar bone of the mandible was most heavily affected (compared to long bones), showing a 50% increase in the unmineralized osteoid volume compared to wild-type littermates. For teeth, mineralization was virtually absent in root dentin of both incisors and molars, and absent in molar cellular cementum, whereas crown dentin showed localized "breakthrough" areas of mineralization. Acellular cementum formation and mineralization was absent in the Col1a1-Mgp mice. Immunohistochemical staining of bone and tooth ECM proteins in Col1a1-Mgp mice showed variations in staining relative to wild-type tissues, with immunostaining generally restricted to areas of mineralization. In conclusion, these results confirm in vivo that ECM proteins can act as inhibitors of bone and tooth 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) | 0.008 | 0.002 |
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".