Molecular determinants of extracellular matrix mineralization in bone and blood vessels
Bibliographic record
Abstract
PURPOSE OF REVIEW: Mineralization imparts important biomechanical and other functional properties to bones and teeth. Ectopic pathologic mineralization, however, occurring in soft tissues such as blood vessels, kidneys, articular cartilage and also in body fluids, including urine and synovial fluid, is generally debilitating, often painful and typically is destructive of compromised tissue. Here we review new findings on direct molecular determinants of mineralization operating locally at the level of the extracellular matrix, with a focus on bone and blood vessels. RECENT FINDINGS: Accumulating evidence indicates important key roles for secreted noncollagenous proteins in regulating mineralization, wherein they also contribute structurally to the scaffolding properties of the extracellular matrix. Mineral-binding proteins contain conserved acidic peptide domains (often highly phosphorylated), which bind strongly to calcium within the apatitic mineral phase of bone and calcified blood vessels to regulate crystal growth. Other recent work has underscored the importance of the small-molecule mineralization inhibitor pyrophosphate in inhibiting tissue mineralization - an inhibition released through its enzymatic cleavage by tissue-nonspecific alkaline phosphatase. Recent findings on mechanisms involved in matrix vesicle-mediated mineralization are also discussed. SUMMARY: Mechanistic details are emerging that describe a scenario wherein the combined actions of mineral-binding noncollagenous matrix peptides/proteins within a scaffolding of collagen (and also elastin in blood vessels), phosphatases and matrix vesicles all contribute importantly to promoting or limiting 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".