Perflubron enhances mucin plug clearance in vitro in the presence of natural surfactant
Bibliographic record
Abstract
Introduction : Severe acute asthma is characterized by bronchoconstriction in combination with mucous plugs. These mucous plugs prevent effective delivery of aerosolized bronchodilators with potentially fatal consequences. Perflubron is a chemically stable and inert compound that has been used in many clinical applications, including bronchial lavage, liquid ventilation, with no significant toxicity. It is an excellent carrier of respiratory gases and may have mucolytic properties. We developed an in vitro model of airway mucous plug obstruction. We looked at the ability of perflubron to enhance clearance of a mucin plug with and without the addition of endogenous surfactant. Methods : Mucin (M3895) was reconstituted with PBS to a concentration of 150 mg/mL. The mucin was placed between two 2.5% agarose gel plugs and inserted into a siliconized 100 µl capillary tube. The tube was exposed to a respiratory ventilator with a stroke volume of 0.5 mL of air at a rate of 20 strokes/min. The apparatus was connected to 10 cm water column to maintain constant peak pressure. Perflubron and bovine lipid extract surfactant (BLES) were added to the capillary tubes separately and together and their effect on mucin movement rates (mm/sec) analyzed Results : ![Figure][1] Conclusion : This study demonstrates that mucin clearance was significantly (P<0.001) increased when treated with PFOB in the presence of surfactant. [1]: pending:yes
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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".