A real-world approach to in-vitro lung epithelial cell toxicology of atmospheric air pollutants : from lab to field
Bibliographic record
Abstract
Exposure to atmospheric air pollution is a major global health risk, recognized through a combination of epidemiological and toxicological studies. This thesis includes a literature review of various models used to study air pollution toxicology, including in-vivo and in-vitro models, and discusses the findings from a range of controlled laboratory experiments. To date and to the best of our knowledge, our understanding of the toxicological effects of air pollution is often limited to controlled laboratory experiments that do not truly represent the ambient air we breathe where complex reactions occur and where mixtures exist. This thesis reviews the directions for ambient study, summarizing studies using archived ambient pollutant samples and the limited number of direct ambient exposures. With varying exposure characteristics and timelines, this thesis aimed to create a generalizable framework to deploy a cell air-liquid-interface (ALI) exposure instrument, namely the Cultex®-RFS, in a mobile laboratory (the Portable Laboratory for Understanding human-Made Emissions, PLUME Van) using human lung epithelial cells, specifically A549 cells, to investigate the direct effects of the exposure to ambient air pollution, focussing on the Greater Vancouver area in British Columbia, Canada. The cell exposure instrument mentioned is coupled with real-time analytical instruments; including standard gas analyzers (CO, NOx, O3; Teledyne T300U and 2B Technologies Models 714 and 205), and particle sizers and counters covering the inhalable particulate matter size range (fast mobility particle sizer (FMPS, TSI 3091), water condensation particle counter (WCPC, TSI 3789)); all within a mobile setting. With these instruments within the PLUME van, there are enhanced possibilities for the exploration of the health effects of real-world pollutants. Here, we discuss results of a case-study looking at a several ambient air conditions both with and without the influence of transported-wildfire pollutants in Vancouver, BC along with the comparison to laboratory-generated woodsmoke exposures. The work of this thesis sets the precedent for future assessments to understanding air pollution toxicology under atmospheric conditions, allowing us to better inform policies and regulations for mitigating health effects and improving environmental health.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".