Extracellular matrix mineralization in periodontal tissues: Noncollagenous matrix proteins, enzymes, and relationship to hypophosphatasia and X‐linked hypophosphatemia
Bibliographic record
Abstract
As broadly demonstrated for the formation of a functional skeleton, proper mineralization of periodontal alveolar bone and teeth - where calcium phosphate crystals are deposited and grow within an extracellular matrix - is essential for dental function. Mineralization defects in tooth dentin and cementum of the periodontium invariably lead to a weak (soft or brittle) dentition in which teeth become loose and prone to infection and are lost prematurely. Mineralization of the extremities of periodontal ligament fibers (Sharpey's fibers) where they insert into tooth cementum and alveolar bone is also essential for the function of the tooth-suspensory apparatus in occlusion and mastication. Molecular determinants of mineralization in these tissues include mineral ion concentrations (phosphate and calcium), pyrophosphate, small integrin-binding ligand N-linked glycoproteins and matrix vesicles. Amongst the enzymes important in regulating these mineralization determinants, two are discussed at length here, with clinical examples given, namely tissue-nonspecific alkaline phosphatase and phosphate-regulating gene with homologies to endopeptidases on the X chromosome. Inactivating mutations in these enzymes in humans and in mouse models lead to the soft bones and teeth characteristic of hypophosphatasia and X-linked hypophosphatemia, respectively, where the levels of local and systemic circulating mineralization determinants are perturbed. In X-linked hypophosphatemia, in addition to renal phosphate wasting causing low circulating phosphate levels, phosphorylated mineralization-regulating small integrin-binding ligand N-linked glycoproteins, such as matrix extracellular phosphoglycoprotein and osteopontin, and the phosphorylated peptides proteolytically released from them, such as the acidic serine- and aspartate-rich-motif peptide, may accumulate locally to impair mineralization in this disease.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".