The buffer capacity of airway epithelial secretions
Bibliographic record
Abstract
The pH of airway epithelial secretions influences bacterial killing and mucus properties and is reduced by acidic pollutants, gastric reflux, and respiratory diseases such as cystic fibrosis (CF). The effect of acute acid loads depends on buffer capacity, however the buffering of airway secretions has not been well characterized. In this work we develop a method for titrating micro-scale (30 μl) volumes and use it to study fluid secreted by the human airway epithelial cell line Calu-3, a widely used model for submucosal gland serous cells. Microtitration curves revealed that HCO(-) 3 is the major buffer. Peak buffer capacity (β) increased from 17 to 28 mM/pH during forskolin stimulation, and was reduced by >50% in fluid secreted by cystic fibrosis transmembrane conductance regulator (CFTR)-deficient Calu-3 monolayers, confirming an important role of CFTR in HCO(-) 3 secretion. Back-titration with NaOH revealed non-volatile buffer capacity due to proteins synthesized and released by the epithelial cells. Lysozyme and mucin concentrations were too low to buffer Calu-3 fluid significantly, however model titrations of porcine gastric mucins at concentrations near the sol-gel transition suggest that mucins may contribute to the buffer capacity of ASL in vivo. We conclude that CFTR-dependent HCO(-) 3 secretion and epithelially-derived proteins are the predominant buffers in Calu-3 secretions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".