Toward a better understanding of enteric gliogenesis
Bibliographic record
Abstract
Most of gastrointestinal functions are controlled by the enteric nervous system (ENS), which contains a vast diversity of neurons and glial cells. In accordance with its key role, defective ENS formation is the cause of several diseases that affect quality of life and can even be life-threatening. Treatment of these diseases would greatly benefit from a better understanding of the molecular mechanisms underlying ENS formation. In this regard, although several important discoveries have been made over the years, how the full spectrum of enteric neuronal and glial cell subtypes is generated from neural crest cells during development still remains enigmatic. Because they also have stem cell properties, such knowledge would be especially important for the enteric glial cell lineage. In a recent study, we identified the NR2F1 transcription factor as a new key regulator of enteric gliogenesis. Here we discuss our recent findings and briefly review what is already known about the mechanisms and signaling pathways involved in enteric gliogenesis, with an emphasis on Hedgehog and Notch signaling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".