Engineered In vitro Models for Pathological Calcification: Routes Toward Mechanistic Understanding
Bibliographic record
Abstract
Physiological calcification plays an essential part in the development of the skeleton and teeth; however, the occurrence of calcification in soft tissues such as the brain, heart, and kidneys associates with health impacts, creating a massive social and economic burden. The current paradigm for pathological calcification focuses on the biological factors responsible for bone‐like mineralization, including osteoblast‐like cells and proteins inducing nucleation and crystal growth. However, the exact mechanism responsible for calcification remains unknown. Toward this goal, this review dissects the current understanding of structure–function relationships and physico‐chemical properties of pathologic calcification from a materials science point of view. We will discuss a range of potential mechanisms of pathological calcification, with the purpose of identifying universal mechanistic pathways that occur across multiple organs/tissues at multiple length scales. The possible effect of extracellular components in signaling and templating mineralization, as well as the role of intrinsically disordered proteins in calcification, is reviewed. The state‐of‐the‐art in vitro models and strategies that can recreate the highly dynamic environment of calcification are identified.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".