Investigation of the mechanisms underlying respiratory abnormalities in vaping product users 9281
Bibliographic record
Abstract
Abstract Description Symptomatic and asymptomatic effects of vaping are poorly understood. To address this issue, our research group initiated a prospective research cohort (VapALERT) monitoring respiratory health of individuals who vape for up to 5 years. So far, identified anomalies are airway hyperresponsiveness (AHR), reduced diffusion capacity and increased ventilation heterogeneity. To research the underlying mechanisms of potential immunological nature, we leveraged data from VapALERT non-invasive tests, including oscillometry and induced sputum, as well as our pre-clinical model of vaping aerosol exposure. Oscillometry assessment using the TremoFlow showed that 20 individuals out of 68 had abnormally elevated resistance at 5 Hz (R5) and/or 19 Hz (R19), suggesting smaller caliber of distal and proximal bronchi potentially caused by ongoing inflammation. Also, of the individuals with elevated R5 and/or R19, 40% had a PC20 below 4 mg/ml compared to 17% in the group with normal R5 and R19. Moreover, FeNO, sputum eosinophils and neutrophils were within normal range in volunteers able to provide sputum. Using our in vivo model, we established that a short-term exposure to nicotine- and flavor-free vaping aerosols did not cause lung inflammation or AHR. Overall, further investigations are required to identify the involvement of the immune system in the lungs of individuals who vape. Chronic exposure protocol may be required to reproduce in mice what has been observed in humans. Funding Sources Funded by Health and Social Services Ministry, AIRS network and CIHR Topic Categories Immune Mechanisms of Human Disease (HUM)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".