Proteolytic enzymes in skeletal development: histochemical methods adapted to the study of matrix lysis during the transformation of a "cartilage model" into bone
Bibliographic record
Abstract
The replacement of a "cartilage model" by definitive bone is characterized by a series of localized excavations of the cartilage which are eventually followed by bone deposition. Each excavation requires lysis of cartilage components (defined here as the breakdown of a peptide bond) and their eventual resorption (defined here as microscopical visible cartilage loss). More precisely we have proposed that the lysis is affected by proteases capable of breaking down the main proteoglycan "aggrecan" and the main fibril element, "type II collagen". Four approaches combining biochemical, immunologic and microscopic techniques have been adapted to test this hypothesis. Each is applied to the rat tibial head's "cartilage model" where proteases have been shown to be major contributors to secondary ossification center formation. The approaches have been found both effective and distinct as cartilage resorbing enzymes have not only been identified but also detected in situ before and after activation. Achieved overall is an understanding of when, where and how specified proteases contribute to tissue component lyses. While the focus resides on the in situ proteolysis of cartilage, three of the approaches could be translated without change to other tissues, whereas one may require tissue specific adjustments before use.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".