Cytotoxic and proinflammatory effects of ambient and source-related particulate matter (PM) in relation to the production of reactive oxygen species (ROS) and cytokine adsorption by particles
Bibliographic record
Abstract
The composition of airborne particulate matter (PM) varies widely depending on its source, and recent studies have suggested that particle-associated adverse health effects are related to particle composition. The objective of this study was to compare the biological/toxicological effects of different source-related PM. Specifically, we investigated the biological/toxicological effects of standard reference materials (SRMs): non-ferrous dust (PD-1, industrial), urban PM (UPM, SRM1648a), and diesel PM (DPM, SRM2975), and ambient PM(2.5) (PM with an aerodynamic diameter <2.5 µm) collected at an urban site (Toronto, Canada). The dithiothreitol assay was used to measure the redox activity of the particles. Human alveolar epithelial cells (A549) were exposed to a range of concentrations (10-1000 µg/ml) of total PM, and the respective water-soluble and insoluble fractions, for 24 h. Biological responses were then evaluated in terms of cytotoxicity and interleukin (IL)-8 release, and compared with the PM composition and redox activity. We demonstrated that transition metal-enriched PD-1 exhibited the greatest cytotoxic effect (LD(50) values of 100-400 µg/ml vs. >1000 µg/ml for the SRM1648a, SRM2975, and ambient PM(2.5)). Similarly, the PM-induced release of IL-8 was greatest for PD-1 (~6-9 ng/ml vs. ~1.5-3 ng/ml for others). These endpoints were more responsive to metals as compared with compared with secondary inorganic ions and organic compounds. Interestingly, we demonstrated a high degree of adsorption of IL-8 to the various SRMs and ambient PM(2.5), and subsequently derived a new correction method to aid in interpretation of these data. These characteristics likely impart differential effects toward the toxic and immune effects of PM.
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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) | 0.000 | 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) | 0.000 | 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 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".