Immunocytochemistry of matrix proteins in calcified tissues: functional biochemistry on section
Bibliographic record
Abstract
The organic matrix of calcified tissues comprises collagenous and/or noncollagenous matrix proteins (NCPs). Identification and precise mapping of these matrix components is essential for determining their function, formulating coherent hypotheses on their mechanism(s) of action, and developing novel therapeutic approaches based on biologics. Fibrillar collagen can be readily identified by its conspicuous structure, however, NCPs, in general, do not individually exhibit characteristic structural features that permit to identify them and morphologically determine their localization. To address this limitation, we have used immunocytochemistry, a form of "biochemistry on section", to correlate composition with structure. For cytochemical characterizations, including immunolabeling, our laboratory has opted for colloidal gold labelings and pioneered their application to calcified tissues because they yield high spatial resolution and are quantitative. Over the years, this approach has been applied to identify and map various NCPs in bone and teeth and, in this review of our work, we will emphasize some selected studies that highlight it application to also achieve functional information.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".