Differentiation of airway cholinergic neurons from human pluripotent stem cells for airway neurobiology studies
Bibliographic record
Abstract
Abstract Airway cholinergic nerves play a key role in airway physiology and disease. In asthma and other diseases of the respiratory tract, airway cholinergic neurons undergo plasticity and contribute to airway hyperresponsiveness and mucus secretion. We currently lack mechanistic understanding of airway cholinergic neuroplasticity due to the absence of human in vitro models. Here, we developed the first human in vitro model for airway cholinergic neurons using human pluripotent stem cell (hPSC) technology. hPSCs were differentiated towards mature and functional airway cholinergic neurons via a vagal precursor. Airway cholinergic neurons were characterized by ChAT and VAChT expression, and responded to chemical stimulation with changes in Ca 2+ mobilization. Co-culture of hPSC-derived airway cholinergic neurons with airway smooth muscle cells enhanced phenotypic and functional characteristics of these neurons. The differentiation protocol we developed for human airway cholinergic neurons from hPSCs allows for studies into airway neurobiology and airway neuroplasticity in disease.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".