Impact of Canadian Wildfire-Emitted Particulate Matter on THP-1 Lung Macrophage Health and Function
Bibliographic record
Abstract
Increasing frequency and intensity of climate-driven wildfires in recent years have resulted in increased human exposures to wildfire smoke and raised serious public health concerns. One potential risk of wildfire smoke exposure is the impairment that it may cause to lung macrophages, which serve as the first line of defense against inhaled pathogens and particles. Size-fractionated wildfire particulate matter (WFPM) collected in the New Jersey/New York metropolitan area during the June 2023 Canadian wildfire event was used to assess the effect on the health and function of THP-1 lung macrophages. Environmentally relevant in vitro WFPM doses were determined using established in vivo and in vitro dosimetry models. Exposure to WFPM 0.1–2.5 (0.1–2.5 μm) for 24 h caused a significant (∼15%) increase in reactive oxygen species, indicating oxidative stress. More importantly, exposure to either WFPM 0.1 (≤0.1 μm) or WFPM 0.1–2.5 significantly reduced THP-1 lung macrophage viability. Additionally, 24 h exposure to either of the WFPM fractions reduced phagocytosis of unopsonized 1 μm polystyrene beads by approximately 50%, which appeared to be due to a defect in binding, which could in turn be a result of scavenger receptor blockade by WFPM or diminished viability and thus ATP depletion, depriving the macrophages of energy required to perform phagocytosis. Together, these findings suggest that WFPM exposure could impair lung macrophage health and function, which could increase susceptibility to respiratory infections. Further mechanistic in vitro and in vivo studies are warranted to better understand the impacts of WFPM on lung innate immunity and the risk of pulmonary infection.
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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 | 0.001 | 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) | 0.001 | 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".