Intercellular Signaling Pathways Active During and After Growth and Differentiation of the Lumbar Vertebral Growth Plate
Bibliographic record
Abstract
STUDY DESIGN: Vertebral growth plates at different postnatal ages were assessed for active intercellular signaling pathways. OBJECTIVE: To generate a spatial and temporal map of the major signaling pathways active in the postnatal mouse lumbar vertebral growth plate. SUMMARY OF BACKGROUND DATA: The growth of all long bones is known to occur by cartilaginous growth plates. The growth plate is composed of layers of chondrocyets that actively proliferate, differentiate, die and, are replaced by bone. The role of major cell signaling pathways has been suggested for regulation of the fetal long bones. But not much is known about the molecular or cellular signals that control the postnatal vertebral growth plate and hence postnatal vertebral bone growth. Understanding such molecular mechanisms will help design therapeutic treatments for vertebral growth disorders such as scoliosis. METHODS: Antibodies against activated downstream intermediates were used to identify cells in the growth plate responding to BMP, TGFβ, and FGF in cryosections of lumbar vertebrae from different postnatal age mice to identify the zones that were responding to these signals. Reporter mice were used to identify the chondrocytes responding to hedgehog (Ihh), and Wnt signaling. RESULTS: We present a spatial/temporal map of these signaling pathways during growth, and differentiation of the mouse lumbar vertebral growth plate. CONCLUSION: During growth and differentiation of the vertebral growth plate, its different components respond at different times to different intercellular signaling ligands. Response to most of these signals is dramatically downregulated at the end of vertebral growth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".