Understanding the Retention of Vaping Additives in the Lungs: Model Lung Surfactant Membrane Perturbation by Vitamin E and Vitamin E Acetate
Bibliographic record
Abstract
Deviations from the normal physicochemical and functional properties of pulmonary surfactants are associated with the incidence of lung injury and other respiratory disorders. This study aims to evaluate the alteration of the 2D molecular organization and morphology of pulmonary surfactant model membranes by the electronic cigarette additives α-tocopherol (vitamin E) and α-tocopherol acetate (vitamin E acetate), which have been associated with lung injury, termed e-cigarette or vaping-use-associated lung injury (EVALI). The model membranes used contained a 7:3 molar ratio of DPPC (1,2-dipalmitoyl- sn -glycero-3-phosphocholine) and POPG (1-palmitoyl-2-oleoyl- sn -glycero-3-phosphoglycerol) to which α-tocopherol and α-tocopherol acetate were added to form mixtures of up to 20 mol % additive. The properties of the neat tocopherol additives and DPPC/POPG (7:3) mixtures with increasing molar proportions of additive were evaluated by surface pressure–area isotherms, excess area calculations, Brewster angle microscopy, grazing incidence X-ray diffraction, X-ray reflectivity, and atomic force microscopy. The addition of either additive alters the essential phase balance of the model pulmonary surfactant membrane by generating a greater proportion of the fluid phase. Despite this net fluidization, both tocopherol additives have space-filling effects on the liquid-expanded and condensed phases, yielding negative excess areas in the liquid-expanded phase and reduced tilt angles in the condensed phase. Both tocopherol additives alter the stability of the fluid phase, pushing the eventual collapse of this phase to higher surface pressures than the model membrane in the absence of an additive.
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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) | 0.000 | 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 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".