Wildfire and wood smoke effects on human airway epithelial cells: A scoping review
Bibliographic record
Abstract
BACKGROUND: Wildfires, which occur naturally but are increasingly intensified by climate change, release a complex mixture of organic and inorganic pollutants. These emissions have significant public health implications, contributing to increased morbidity and mortality. Epidemiological and clinical studies have consistently shown that exposure to wildfire smoke exacerbates respiratory conditions such as chronic obstructive pulmonary disease (COPD) and asthma. There are many epidemiological studies of the potential impact of smoke on human health; however, there are remarkably few in vitro studies, and an investigation of the underlying mechanisms of wildfire and wood smoke exposure on airway epithelial cells is required to better understand their toxicity and significance. OBJECTIVES: This scoping review aimed to critically examine studies on the association between wildfire and wood smoke exposure and airway epithelial cell responses. METHODS: We conducted a systematic search of relevant studies that used a combination of keywords related to wood smoke, wildfire, and epithelial cells and were published up to May 2024. Studies were retrieved from MEDLINE, PubMed, Google Scholar, and Web of Science. RESULTS: Twenty-three studies fulfilled our inclusion criteria and were included. This review highlights inflammation, oxidative stress, and cytotoxicity as key impacts of wildfire and wood smoke on airway epithelial cells, causing lung damage. More studies are needed to understand these effects and guide prevention strategies. DISCUSSION: This scoping review underscores the need for further research to better understand the complex biological endpoints associated with exposure to wildfire/wood smoke, informing strategies to mitigate health effects, ultimately improving health and well-being of population exposed to wildfire/wood smoke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".