Developmental impairments of craniofacial bone and cartilage in transgenic mice expressing FGF10
Bibliographic record
Abstract
Mutations in a common extracellular domain of fibroblast growth factor receptor (FGFR)-2 isoforms (type IIIb and IIIc) cause craniosynostosis syndrome and chondrodysplasia syndrome. FGF10, a major ligand for FGFR2-IIIb and FGFR1-IIIb, is a key participant in the epithelial-mesenchymal interactions required for morphogenetic events. FGF10 also regulates preadipocyte differentiation and early chondrogenesis in vitro, suggesting that FGF10-FGFR signaling may be involved in craniofacial skeletogenesis in vivo. To test this hypothesis, we used a tet-on doxycycline-inducible transgenic mouse model (FGF10 Tg) to overexpress Fgf10 from embryonic day 12.5. Fgf10 expression was 73.3-fold higher in FGF10 Tg than in wild-type mice. FGF10 Tg mice exhibited craniofacial anomalies, such as a short rostrum and mandible, an underdeveloped (cleft) palate, and no tympanic ring. Opposite effects on chondrogenesis in different anatomical regions were seen, e.g., hyperplasia in the nasal septum and hypoplasia in the mandibular condyle. We found an alternative splicing variant of Fgfr2-IIIb with a predicted translation product lacking the transmembrane domain, and suggesting a soluble form of FGFR2-IIIb (sFGFR2-IIIb), differentially expressed in some of the craniofacial bones and cartilages. Thus, excessive FGF10 may perturb signal transduction of the FGF-FGFR, leading to craniofacial skeletal abnormalities in FGF10 Tg mice.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".