Wood smoke exposure induces a pulmonary and systemic inflammatory response in firefighters
Bibliographic record
Abstract
Epidemiological studies report an association between exposure to biomass smoke and cardiopulmonary morbidity. The mechanisms for this association are unclear. The aim of the present study was to characterise the acute pulmonary and systemic inflammatory effects of exposure to forest fire smoke. Seasonal forest firefighters (n = 52) were recruited before and/or after a day of fire-fighting. Exposure was assessed by questionnaires and measurement of carbon monoxide levels (used to estimate respirable particulate matter exposure). The pulmonary response was assessed by questionnaires, spirometry and sputum induction. Peripheral blood cell counts and inflammatory cytokines were measured to define the systemic response. Estimated respirable particulate matter exposure was high (peak levels >2 mg x m(-3)) during fire-fighting activities. Respiratory symptoms were reported by 65% of the firefighters. The percentage sputum granulocytes increased significantly from 6.5 to 10.9% following fire-fighting shifts, with concurrent increases in circulating white blood cells (5.55x10(9) to 7.06x10(9) cells x L(-1)) and band cells (0.11x10(9) to 0.16x10(9) cells x L(-1)). Serum interleukin (IL)-6, IL-8 and monocyte chemotactic protein-1 levels significantly increased following fire-fighting. There were no changes in band cells, IL-6, and IL-8 following strenuous physical exertion without fire-fighting. There was a significant association between changes in sputum macrophages containing phagocytosed particles and circulating band cells. In conclusion, acute exposure to air pollution from forest fire smoke elicits inflammation within the lungs, as well as a systemic inflammatory response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".