Flavourings significantly affect inhalation toxicity of aerosol generated from electronic nicotine delivery systems (ENDS)
Bibliographic record
Abstract
BACKGROUND: E-cigarettes or electronic nicotine delivery systems (ENDS) are designed to deliver nicotine-containing aerosol via inhalation. Little is known about the health effects of flavoured ENDS aerosol when inhaled. METHODS: Aerosol from ENDS was generated using a smoking machine. Various types of ENDS devices or a tank system prefilled with liquids of different flavours, nicotine carrier, variable nicotine concentrations and with modified battery output voltage were tested. A convenience sample of commercial fluids with flavour names of tobacco, piña colada, menthol, coffee and strawberry were used. Flavouring chemicals were identified using gas chromatography/mass spectrometry. H292 human bronchial epithelial cells were directly exposed to 55 puffs of freshly generated ENDS aerosol, tobacco smoke or air (controls) using an air-liquid interface system and the Health Canada intense smoking protocol. The following in vitro toxicological effects were assessed: (1) cell viability, (2) metabolic activity and (3) release of inflammatory mediators (cytokines). RESULTS: Exposure to ENDS aerosol resulted in decreased metabolic activity and cell viability and increased release of interleukin (IL)-1β, IL-6, IL-10, CXCL1, CXCL2 and CXCL10 compared to air controls. Cell viability and metabolic activity were more adversely affected by conventional cigarettes than most tested ENDS products. Product type, battery output voltage and flavours significantly affected toxicity of ENDS aerosol, with a strawberry-flavoured product being the most cytotoxic. CONCLUSIONS: Our data suggest that characteristics of ENDS products, including flavours, may induce inhalation toxicity. Therefore, ENDS users should use the products with caution until more comprehensive studies are performed.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".