Inflammation and Oxidative Stress from E-cigarette Exposure: Implications for COPD and Asthma
Bibliographic record
Abstract
Currently, little is known about the effects of e-cigarette use on chronic respiratory diseases, due to their relative novelty. This review compiles data on the cellular effects of e-cigarette use with population data on disease incidence to determine potential risk for COPD and asthma development, two of the most prevalent respiratory diseases. We searched the Google Scholar database for studies on e-cigarette exposure and levels of inflammation and oxidative stress in human cells and e-cigarette users, as well a population studies analyzing e-cigarette use and respiratory disease incidence. All reviewed studies found significant increases in inflammatory biomarkers, as well as pro-inflammatory cytokines, demonstrating a correlation between e-cigarette use and a pro-inflammatory affect. Our findings suggest e-cigarette vapor contains reactive oxygen species, and that exposure increases cellular oxidation and lowers antioxidant power. Every population study we reviewed found significant correlations between COPD and e-cigarette use, and asthma and e-cigarette use. These population studies cannot provide causational data, though the basic cellular data provides support for causative effects. Further research should investigate the link between the cellular and population data to identify causation and understand the impact of e-cigarette use on disease rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".