Laryngeal mucosa: Its susceptibility to damage by acid and pepsin
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Exposure of pig laryngeal mucosa to pepsin and acid will have a differential damaging effect depending on the anatomical site, mirroring the effects seen in the human larynx in laryngopharyngeal reflux (LPR). This study aims to quantitate damage caused to laryngeal tissue by acid alone, and acid and pepsin, and also to determine if the extent of this damage depends on the tissue site. STUDY DESIGN: Prospective translational research study. METHODS: An excised porcine laryngeal damage model in a small Ussing chamber was used to measure the effect of pepsin and acid on five sites (ventricles, vocal folds, posterior commissure, supraglottic, and subglottic mucosa). The tissue samples were incubated on the lumenal side for 1 hour with pH 2 and 4 HCl, pH 2 plus 1 mg/mL pepsin, and pH 4 plus 1 mg/mL pepsin. Damage was assessed by changes in absorbance of the bathing solution at optical density (OD) 260 nm and OD 280 nm and by measurement of released DNA compared to tissues bathed in pH 7.4 buffer. Damage was also assessed histologically. RESULTS: Based on histology, all the tissues were resistant to pH 4.0 except the subglottic mucosa. Only the posterior commissure was not damaged by pH 2.0 plus pepsin. Similar patterns were observed with absorbance changes and DNA release. CONCLUSIONS: The subglottic mucosa was the most susceptible to damage and the posterior commissure the least. Laryngeal tissues are essentially resistant to damage at pH 4.0, but are damaged when pepsin is present. This suggests that in LPR, pH 4.0 or above refluxate would only be damaging if it contains pepsin.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".