Bibliographic record
Abstract
Airway surface liquid (ASL), the thin film of fluid covering the luminal aspect of airway epithelial cells, plays a central but mysterious role in cystic fibrosis (CF).CF is characterized by abnormal transepithelial salt transport, viscous airway mucus, chronic bacterial infections, and inflammation, but precisely how mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene lead to these symptoms is the subject of a vigorous debate.As discussed one year ago in the Perspective by J.J. Wine (1), there are two very different hypotheses to explain CF pathogenesis in the airways.According to the "volume" model, lack of CFTR in the apical membrane leads to increased salt and fluid absorption by airway epithelial cells, which reduces ASL volume and leads to ineffective mucociliary clearance of bacteria.According to this hypothesis, sodium transport is increased in CF through upregulation of sodium channels that would normally be inhibited by CFTR (2).The other hypothesis, which we shall refer to here as the "salt" model, proposes that a lack of functional CFTR chloride channels leads to decreased salt absorption, which elevates salt concentration in the ASL, thereby inhibiting activity of antibacterial substances (3).Both hypotheses are strongly supported by in vitro experiments, despite making different predictions concerning the volume and composition of ASL.Fluid collected from the surface of CF airway cell cultures by one group had elevated salt and diminished ability to kill bacteria (3), whereas liquid collected from cultures by the other group was isotonic (2).Different methodologies were used for sampling and analysis, but the contrasting results may simply reflect phenotypic differences between cell cultures prepared in different laboratories.One would like to use the composition of ASL in vivo as the gold standard, but problems associated with sampling a compartment that is only tens of microns thick are even worse in vivo, and those data are also contradictory.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".