Upper Airway Muscle Inflammation and Denervation Changes in Obstructive Sleep Apnea
Bibliographic record
Abstract
Inflammatory cell infiltration and afferent neuropathy have been shown in the upper airway (UA) mucosa of subjects with obstructive sleep apnea (OSA). We hypothesized that inflammatory and denervation changes also involve the muscular layer of the pharynx in OSA. Morphometric analysis was performed on UA tissue from nonsnoring control subjects (n = 7) and patients with OSA (n = 11) following palatal surgery. As compared with control subjects, inflammatory cells were increased in the muscular layer of patients with OSA, with CD4+ and activated CD25+ T cells (both increased approximately threefold) predominating. Inflammation was also present in UA mucosa, but with a different pattern consisting of CD8+ (2.8-fold increase) and activated CD25+ (3.2-fold increase) T cell predominance. As ascertained by immunoreactivity for the panneuronal marker PGP9.5, there was a dramatic (5.7-fold) increase in intramuscular nerve fibers in OSA patients compared with control subjects, as well as direct evidence of denervation based on positive immunostaining of the muscle fiber sarcolemmal membrane for the neural cell adhesion molecule in patients with OSA. These data suggest that inflammatory cell infiltration and denervation changes affect not only the mucosa, but also the UA muscle of patients with OSA. This may have important implications for the ability to generate adequate muscular dilating forces during sleep.
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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".