One Acute Exposure to E-Cigarette Smoke Using Various Heating Elements and Power Levels Induces Pulmonary Inflammation
Bibliographic record
Abstract
Background: Electronic cigarettes (eC) may not be entirely benign. There is a lack of data on the effect of a single acute exposure of eC vapor using various heating sources and power settings upon lung injury. The purpose of this study was to determine if an acute exposure with eC vapor heated with different heating elements and power levels induced inflammatory changes in the lungs and heart. Methods: Rats were exposed to pure air or received a single, 4-h exposure to eC vapor. The devices used either a stainless steel (SS) or nichrome (NC) heating element randomized to a low or high atomization power (45 versus 70 W). Rats were euthanized within 48 h of exposure. Results: The eC groups showed accumulation of inflammatory cells in bronchial lumen, near the pleura, and within the alveolar spaces. The numbers of inflammatory cells per field in the lung parenchyma were significantly greater in the rats exposed to eC groups vs. the air group. There were significantly higher inflammatory gene expression changes in the lungs of animals assigned to 70 W power. We observed that eC vapor generated using burnt coils were toxic and could cause acute respiratory distress and myocarditis. Conclusion: In conclusion, one 4-h exposure to eC vapor, in the absence of vitamin E oil or nicotine, significantly increased lung inflammation. Effects were seen after exposures to vapor generated using SS and NC heating elements at either high or low power. Vapor from devices with burnt coils can negatively affect the heart and lung.
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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) | 0.002 | 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".