Chronic low‐level <scp>JUUL</scp> aerosol exposure causes pulmonary immunologic, transcriptomic, and proteomic changes
Bibliographic record
Abstract
E-cigarettes currently divide public opinion, with some considering them a useful tool for smoking cessation and while others are concerned with potentially adverse health consequences. However, it may take decades to fully understand the effects of e-cigarette use in humans given their relative newness on the market. This highlights the need for comprehensive preclinical studies investigating the effects of e-cigarette exposure on health outcomes. Here, we investigated the impact of chronic, low-level JUUL aerosol exposure on multiple lung outcomes. JUUL is a brand of e-cigarettes popular with youth and young adults. To replicate human exposures, 8- to 12-week-old male and female C57BL/6J mice were exposed to commercially available JUUL products (containing 59 mg/ml nicotine). Mice were exposed to room air, PG/VG, or JUUL daily for 4 weeks. After the exposure period, inflammatory markers were assessed via qRT-PCR, multiplex cytokine assays, and differential cell count. Proteomic and transcriptomic analyses were also performed on samples isolated from the lavage of the lungs; this included unbiased analysis of proteins contained within extracellular vesicles (EVs). Mice exposed to JUUL aerosols for 4 weeks had significantly increased neutrophil and lymphocyte populations in the BAL and some changes in cytokine mRNA expression. However, BAL cytokines did not change. Proteomic and transcriptomic analysis revealed significant changes in numerous biological pathways including neutrophil degranulation, PPAR signaling, and xenobiotic metabolism. Thus, e-cigarettes are not inert and can cause significant cellular and molecular changes in the lungs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".