Cholesterol Enhances The Negative Impact of Vaping Additives on Lung Surfactant Model Systems
Bibliographic record
Abstract
Aims: Vaping has given rise to e-cigarette or vaping product use-associated lung injury. Model lung surfactant films were used to assess the impact of vape additives (vitamin E, vitamin E acetate, tetrahydrocannabinol, cannabidiol). This work builds upon our previous findings, by incorporating cholesterol, to understand the interplay between the additives and the sterol in surfactant function. Materials & methods: Compression–expansion cycles of lipid monofilm at the air–water interface and Brewster angle microscopy allowed elucidating the effects of vape additives. Results & conclusion: Vape additives at 5 mol% inhibited proper lipid packing and reduced film stability. Cholesterol enhanced the additive effects, resulting in significantly destabilized films and altered domains. The observed impact could signify dysfunctional lung surfactant and impaired lung function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".