Signaling by SHH rescues facial defects following blockade in the brain
Bibliographic record
Abstract
BACKGROUND: The Frontonasal Ectodermal Zone (FEZ) is a signaling center in the face that expresses Sonic hedgehog (Shh) and regulates patterned growth of the upper jaw. Blocking SHH in the forebrain blocks Shh expression in the FEZ and creates malformations resembling holoprosencephaly (HPE), while inhibition of BMP signaling in the mesenchyme blocks FEZ formation and causes similar dysmorphology. Thus, the brain could regulate FEZ formation by SHH or BMP signaling, and if so, activating one of these pathways in the face might alleviate the effects of repression of SHH in the brain. RESULTS: We blocked SHH signaling in the brain while adding SHH or BMP between the neural and facial ectoderm of the frontonasal process. When applied early, SHH restored Shh expression in the FEZ and significantly improved shape outcomes, which contrasts with our previous experiments that showed later SHH treatments have no effect. BMP-soaked beads introduced early and late caused apoptosis that exacerbated malformations. Finally, removal of Smoothened from neural crest cells did not inhibit Shh expression in the FEZ. CONCLUSIONS: Collectively, this work suggests that a direct, time-sensitive SHH signal from the brain is required for the later induction of Shh in the FEZ. We propose a testable model of FEZ activation and discuss signaling mediators that may regulate these interactions.
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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 | 0.000 |
| 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 | 0.001 |
| 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".