Double whole‐mount staining for bone deposition and resorption
Bibliographic record
Abstract
Methods to detect osteoblasts, bone depositing cells, and osteoclasts, bone resorpting cells are known. These methods involve enzymatic procedures (alkaline phosphatase and tartrate‐resistant acid phosphatase) and are usually conducted on sectioned material; with the procedure for each type of staining conducted on adjacent serial sections. We have optimised a procedure to whole‐mount stain the same zebrafish for both bone deposition and resorption. The alkaline phosphatase procedure stains osteoblasts a purple colour and the tartrate‐resistant acid phosphatase gives a red colour; making the two cell types easily distinguishable. The stained specimen can be decalcified if necessary and sectioned, with preservation of the cell staining. We have applied this methodology to whole fish at various stages of development and have focused our investigations on the jaw, fins and vertebral column regions. Bone deposition was clearly observed in the mandible and maxilla; the developing fins, the ends of fin ray segments and the edges of the developing calvariae. On similarly aged fish, bone resorption was detected in the neural spines of the vertebrae; mandible and maxilla; with very limited amounts within the fins. We believe this methodology would be useful to the field of skeletal biology as it enables double staining of the same specimen and avoids lengthy resin embedding procedures. Funding NSERC, Canada Grant Funding Source NSERC, Canada
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".