Chronic exposure to E-cigarette aerosols potentiates atherosclerosis in a sex-dependent manner
Bibliographic record
Abstract
Despite being designed for smoking cessation, e-cigarettes and their variety of flavors have become increasingly attractive to teens and young adults. This trend has fueled concerns regarding the potential role of e-cigarettes in advancing chronic diseases, notably those affecting the cardiovascular system. E-cigarettes contain a mixture of metals and chemical compounds, some of which have been implicated in cardiovascular diseases like atherosclerosis. Our laboratory has optimized in vivo exposure regimens to mimic human vaping patterns. Using these established protocols in an inducible (AAV-PCSK9) hyperlipidemic mouse model, this study tests the hypothesis that a chronic exposure to e-cigarette aerosols will increase atherosclerotic plaques. The exposures were conducted using the SCIREQ InExpose™ nose-only inhalation system and STLTH or Vuse products for 16 weeks. We observed that only male mice exposed to STLTH or Vuse aerosols had significantly increased plasma total cholesterol, triglycerides, and LDL cholesterol levels compared to mice exposed to system air. Moreover, these male mice also had a significant increase in aortic and sinus plaque area. Male mice exposed to e-cigarette aerosol had a significant reduction in weight gain over the exposure period. Our data indicate that e-cigarette use in young hyperlipidemic male mice increases atherosclerosis in the absence of significant pulmonary and systemic inflammation. These results underscore the need for extensive research to unravel the long-term health effects of e-cigarettes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".