Specific flavor chemicals used in vaping products modulate maturation of dendritic cells in vitro and in mice exposed to flavored vaping aerosols in vivo 2958
Bibliographic record
Abstract
Abstract Description Rationale Vaping products contain numerous flavor compounds that are known immunological sensitizers. However, respiratory tract sensitization and pathological consequences from exposure to flavored vaping aerosols need to be thoroughly investigated. Methods Female BALB/c mice were exposed 2h/day for 4 days to flavored or unflavored vaping aerosols or room air. Flavored vaping liquids included a mixture of 1% citral (lemon), 1% cinnamaldehyde (cinnamon), 1% dihydrocoumarin (coconut) and 1% vanillin (vanilla). Bronchoalveolar lavage and flow cytometry were performed on pulmonary tissue to assess immune cell populations with emphasis on dendritic cells. In vitro model of dendritic cells was also used to assess dendritic cells maturation upon vaping liquid and aerosol condensate treatments. Results After 4 days of daily exposure, we observed a significant increase of maturation markers CD86 and MHC-II at the surface of conventional dendritic cells (cDCs) the lung tissue. This activation was mainly observed on type 2 cDCs. No changes were observed for macrophages, neutrophils, B and T lymphocyte of the lung tissue. Furthermore, in vitro experiments indicate that cinnamaldehyde and citral are capable to increase maturation markers on dendritic cells. Conclusions Specific flavored vaping aerosols or liquids can lead to dendritic cells maturation in the lung of mice or in vitro which is an early cellular event related to respiratory sensitization and pulmonary disease. Funding Sources Funding Sources: Fonds de recherche du Québec – Santé, Ministère de la Santé et des Services sociaux, Fondation de l’Institut universitaire de cardiologie et de pneumologie de Québec, Réseau Air, intersectorialité, recherche respiratoire et sonore du Québec. Topic Categories Antigen and Dendritic Cell Processing, Presentation, and Biology (AGDC)
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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".