Effect of Serum, Cholesterol and Low Density Lipoprotein on the Functionality and Structure of Lung Surfactant Films
Bibliographic record
Abstract
Lung surfactant is a complex mixture of lipid and protein, responsible for alveolar stability, becomes dysfunctional due to alteration of its structure and function by leaked serum materials in disease. Serum proteins, cholesterol and low density lipoprotein (LDL) were studied with bovine lipid extract surfactant (BLES) using Langmuir films, and bilayer dispersions using Raman spectroscopy. While small amount of cholesterol (10 wt%) and LDL did not significantly affect the adsorption and surface tension lowering properties of BLES. However serum lipids, whole serum as well as higher amounts of cholesterol, and LDL dramatically altered the surface properties of BLES films, as well as gel-fluid structures formed in such films observed using atomic force microscopy (AFM). Raman-spectroscopic studies revealed that serum proteins, LDL and excess cholesterol had fluidizing effects on BLES bilayers dispersion, monitored from the changes in hydrocarbon vibrational modes during gel-fluid thermal phase transitions. This study clearly suggests that patho-physiological amounts of serum lipids (and not proteins) significantly alter the molecular arrangement of surfactant in films and bilayers, and can be used to model lung disease.
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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".