Multidisciplinary Approach to Understand Medial Arterial Calcification
Bibliographic record
Abstract
OBJECTIVE: Vascular calcification significantly increases morbidity in life-threatening diseases, and no treatments are available because of lack of understanding of the underlying molecular mechanism. Here, we study the physicochemical details of mineral nucleation and growth in an animal model that faithfully recapitulates medial arterial calcification in humans, to understand how pathological calcification is initiated on the vascular extracellular matrix. APPROACH AND RESULTS: MGP (matrix Gla protein) is a potent mineralization inhibitor. We study the evolution of medial calcification in MGP-deficient mice over the course of 5 weeks using a combination of material science techniques and find that mineral composition and crystallinity evolve over time and space. We show that calcium is adsorbed first and then amorphous calcium phosphate and octacalcium phosphate forms, which then transform into hydroxyapatite and carbonated apatite. These events are repeated after each nucleation event, providing a snapshot of the overall mineral evolution at each time point analyzed. CONCLUSIONS: Our results show that an interdisciplinary approach combining animal models and materials science can provide insights into the mechanism of vascular calcification and suggest the importance of analyzing mineral phases, rather than just overall mineralization extent, to diagnose and possibly prevent disease development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".