Investigating Wildfire Smoke Effects on Patrolling Alveolar Macrophages
Bibliographic record
Abstract
Under homeostatic conditions, patrolling alveolar macrophages migrate between alveoli to effectively clear inhaled noxious substances throughout the lung. However, certain pathogens and toxins can impair this migratory function, resulting in deleterious neutrophil recruitment and tissue injury. During wildfire events, animals and humans breathe in smoke containing large amounts of particulate matter and aerosolized microbes. Using intravital imaging, we aimed to characterize the impact of wildfire smoke particulate matter on alveolar macrophage migration to elucidate the mechanisms underlying wildfire smoke’s adverse respiratory effects. To this end, we administered sterilized biomass smoke particles or saline to 6-8 week old C57BL/6 mice (i.n.) for 3 days. On day 4, in vivo macrophage and neutrophil behaviour were assessed via spinning disk confocal microscopy. To further characterize the acute effects of sterile smoke particle inhalation, immunophenotyping of bronchoalveolar lavage fluid and whole lung homogenates was conducted using flow cytometry. Preliminary data indicate that exposure to sterile biomass smoke particulates does not significantly decrease alveolar macrophage displacement in the lung, suggesting that migratory capacity remained unchanged. Using unsterilized biomass smoke particulates, we will next determine whether alveolar macrophage migration is impaired following simultaneous particulate and microbial exposure, resulting in downstream neutrophilic inflammation. Given the increasing frequency of wildfires, investigating associated health impacts is increasingly warranted. Determining the impact of wildfire smoke on patrolling alveolar macrophages may ultimately uncover key mechanisms and therapies to alleviate adverse respiratory effects.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".