E-cigarette exposure causes early pro-atherogenic changes in an inducible murine model of atherosclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| 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 it