Assessing the fate and contribution of Foxd1-expressing embryonic precursors and their progeny in palatal development, homeostasis and excisional repair
Bibliographic record
Abstract
Oral mucosal tissues heal rapidly with minimal scarring, although palatal mucosa can be associated with excessive fibrosis in response to injury. Investigations on the balance between neovascularization and tissue repair suggests regulation of angiogenesis is an important determinant of repair versus scarring. Associated with pericyte mediated fibrosis in kidney injury, FoxD1 is implicated in growth centres during cranio-facial development, although which cell lineages are derived from these embryonic populations in development and in adult animals is unknown. Using a lineage tracing approach, we assessed the fate of embryonic Foxd1-expressing progenitor cells and their progeny in palatal development and during wound healing in adult mice. During palatal development as well as in post-natal tissues, Foxd1-lineage progeny were associated with the vasculature and the epineurium. Post-injury, de novo expression of FoxD1 was not detectable, although Foxd1-lineage progeny expanded while exhibiting low association with the fibroblast/myofibroblast markers PDGFα, PDGFβ, vimentin, α-smooth muscle actin, as well as the neuronal associated markers S100β and p75NTR. Foxd1-lineage progeny were primarily associated with CD146, CD31, and to a lesser extent CD105, remaining in close proximity to developing neovascular structures. Our findings demonstrate that FoxD1 derived cells are predominantly associated with the palatal vasculature and provide strong evidence that FoxD1 derived cells do not give rise to populations involved directly in the scarring of the palate.
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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) | 0.001 | 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".