Neural crest-specific loss of<i>Bmp7</i>leads to midfacial hypoplasia, nasal airway obstruction and disordered breathing, modeling obstructive sleep apnea
Bibliographic record
Abstract
ABSTRACT Pediatric obstructive sleep apnea (OSA), a relatively common sleep-related breathing disorder affecting ∼1-5% of children, is often caused by anatomical obstruction and/or collapse of the nasal and/or pharyngeal airways. The resulting sleep disruption and intermittent hypoxia lead to various systemic morbidities. Predicting the development of OSA from craniofacial features alone is currently not possible, and controversy remains as to whether upper-airway obstruction facilitates reduced midfacial growth or vice versa. Currently, there is no rodent model that recapitulates both the development of craniofacial abnormalities and upper-airway obstruction to address these questions. Here, we describe that mice with a neural crest-specific deletion of Bmp7 (Bmp7ncko) present with a shorter, more acute-angled cranial base, midfacial hypoplasia, nasal septum deviation, turbinate swelling and branching defects, and nasal airway obstruction. Interestingly, several of these craniofacial features develop after birth during periods of rapid midfacial growth and precede the development of an upper-airway obstruction. We identified that, in this rodent model, no single feature appeared to predict upper-airway obstruction, but the sum of those features resulted in reduced breathing frequency, apneas and overall reduced oxygen consumption. Metabolomics analysis of serum from peripheral blood identified increased levels of hydroxyproline, a metabolite upregulated under hypoxic conditions. As this model recapitulates many features observed in OSA, it offers unique opportunities for studying how upper-airway obstruction affects breathing physiology and leads to systemic morbidities. This article has an associated First Person interview with the first author of the paper.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".