Improvement to the surface tension reducing capability of human pulmonary surfactant with elevated levels of cholesterol via addition of surfactant protein A
Bibliographic record
Abstract
Pulmonary surfactant is composed of phospholipids, neutral lipids and proteins. Surfactant's composition is important for its ability to reduce surface tensions within alveoli. Previous studies showed that serum‐protein mediated surfactant dysfunction can be overcome by the addition of surfactant protein A (SP‐A) to the material. Elevated cholesterol levels in surfactant can also cause surfactant dysfunction, but it is unknown if SP‐A can reverse this form of impairment. It was hypothesized that the addition of SP‐A to surfactant with elevated levels of cholesterol will improve the material's ability to reduce surface tension. Human surfactant was depleted of cholesterol via repeated acetone precipitation and reconstituted with 0, 5, 10 or 20% cholesterol with or without 5% SP‐A. The minimum achievable surface tension of the samples was tested on a captive bubble surfactometer. Results showed that surfactant with 10 and 20% cholesterol had significantly higher minimum surface tensions than surfactant with 0 and 5% cholesterol. Addition of SP‐A to samples with 10 and 20% cholesterol resulted in significantly lower minimum surface tension as compared to analogous samples without SP‐A. In conclusion , cholesterol mediated surfactant dysfunction can be mitigated by SP‐A. Funding was provided by Canadian Institutes for Health Research.
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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 | 0.001 |
| 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".