Lack of organ specific commitment of vagal neural crest cell derivatives as shown by back-transplantation of GFP chicken tissues
Bibliographic record
Abstract
Neural crest cells (NCC) are multipotent progenitors that migrate extensively throughout the developing embryo and generate a diverse range of cell types. Vagal NCC migrate from the hindbrain into the foregut and from there along the gastrointestinal tract to form the enteric nervous system (ENS), the intrinsic innervation of the gut, and into the developing lung buds to form the intrinsic innervation of the lungs. The aim of this study was to determine the developmental potential of vagal NCC that had already colonised the gut or the lungs. We used transgenic chicken embryos that ubiquitously express green fluorescent protein (GFP) to permanently mark and fate-map vagal NCC using intraspecies grafting. This was combined with back-transplantation of gut and lung segments, containing GFP-positive NCC, into the vagal region of a second recipient embryo to determine, using immunohistochemical staining, whether gut or lung NCC are competent of re-colonising both these organs, or whether their fate is restricted. Chick(GFP)-chick intraspecies grafting efficiently labelled NCC within the gut and lung of chick embryos. When segments of embryonic day (E)5.5 pre-umbilical midgut containing GFP-positive NCC were back-transplanted into the vagal region of E1.5 host embryos, the GFP-positive NCC remigrated to colonise both the gut and lungs and differentiated into neurons in stereotypical locations. However, GFP-positive lung NCC did not remigrate when back-transplanted. Our studies suggest that gut NCC are not restricted to colonising only this organ, since upon back-transplantation GFP-positive gut NCC colonised both the gut and the lung.
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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 | 0.001 | 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".