Synergistic effects of FGF and non‐ridge ectoderm on gene expression involved in the formation of the anteroposterior axis of the chick limb bud in cell culture
Bibliographic record
Abstract
Skeletal patterning of the vertebrate limb is controlled by the zone of polarizing activity (ZPA), apical ectodermal ridge (AER) and dorsal ectoderm. In the present study, to understand the involvement of fibroblast growth factor (FGF) and non-ridge ectoderm in anteroposterior (AP) axis formation, gene expression in chick limb bud mesenchymal cells in culture was investigated by reverse transcription-polymerase chain reaction and in situ hybridization. It was found that Shh expression was locally maintained in the mesenchymal cells underneath and near non-ridge ectoderm in coculture with the posterior mesenchymal cells and non-ridge ectoderm in the presence of FGF-4 by in situ hybridization. In Shh-expressing anterior limb bud mesenchymal cells cultured with non-ridge ectoderm, it was also discovered that Bmp-2 was activated in the presence of FGF-2, -4 and -8, while Hoxd-13 was activated in the presence of FGF-4 and that FGF-2 had a similar effect but FGF-8 did not. This result indicates that Hoxd-13 activation by SHH depends on non-ridge ectoderm and FGF-2 or FGF-4, and that there may be a difference in the effect on AP axis formation of the limb bud between FGF-2, -4 and -8. Possible roles of these genes and signal molecules in AP pattern formation are discussed.
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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 | 0.000 | 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 | 0.000 |
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".