E-cigarette exposure causes early pro-atherogenic changes in an inducible murine model of atherosclerosis
Bibliographic record
Abstract
Introduction: Evidence suggests that e-cigarette use (vaping) increases cardiovascular disease risk, but decades are needed before people who vape would develop pathology. Thus, murine models of atherosclerosis can be utilized as tools to understand disease susceptibility, risk and pathogenesis. Moreover, there is a poor understanding of how risk factors for atherosclerosis (i.e., hyperlipidemia, high-fat diet) intersect with vaping to promote disease risk. Herein, we evaluated whether there was early evidence of atherosclerosis in an inducible hyperlipidemic mouse exposed to aerosol from commercial pod-style devices and e-liquid. Methods: Mice were injected with adeno-associated virus containing the human protein convertase subtilisin/kexin type 9 (PCSK9) variant to promote hyperlipidemia. These mice were fed a high-fat diet and exposed to room air or aerosol derived from JUUL pods containing polyethylene glycol/vegetable glycerin (PG/VG) or 5% nicotine with mango flavoring for 4 weeks; this timepoint was utilized to assess markers of atherosclerosis that may occur prior to the development of atherosclerotic plaques. Results: These data show that various parameters including weight, circulating lipoprotein/glucose levels, and splenic immune cells were significantly affected by exposure to PG/VG and/or nicotine-containing aerosols. Discussion: Not only can this mouse model be utilized for chronic vaping studies to assess the vascular pathology but these data support that vaping is not risk-free and may increase CVD outcomes later in life.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".