Effect of budesonide and salbutamol on surfactant properties
Bibliographic record
Abstract
The objective of this study was to evaluate the in vitro effect of budesonide and salbutamol on the surfactant biophysical properties. The surface-tension properties of two bovine lipid extracts [bovine lipid extract surfactant (BLES) and Survanta] and a rat lung lavage natural surfactant were evaluated in vitro by the captive bubble surfactometer. Measurements were obtained before and after the addition of a low and high concentration of budesonide and salbutamol. Whereas salbutamol had no significant effect, budesonide markedly reduced the surface-tension-lowering properties of all surfactant preparations. Surfactant adsorption (decrease in surface tension vs. time) was significantly reduced (P < 0.01) at a high budesonide concentration with BLES, both concentrations with Survanta, and a low concentration with natural surfactant. At both concentrations, budesonide reduced (P < 0.01) Survanta film stability (minimal surface vs. time at minimum bubble volume), whereas no changes were seen with BLES. The minimal surface tension obtained for all surfactant preparations was significantly higher (P < 0.01), and the percentage of film area compression required to reach minimum surface tension was significantly lower after the addition of budesonide. In conclusion, budesonide, at concentrations used therapeutically, adversely affects the surface-tension-lowering properties of surfactant. We speculate that it may have the same adverse effect on the human surfactant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".