Effect of diesel exhaust inhalation on blood markers of inflammation and neurotoxicity: a controlled, blinded crossover study
Bibliographic record
Abstract
CONTEXT: Epidemiological studies and animal research have suggested that air pollution may negatively impact the central nervous system (CNS). Controlled human exposure studies of the effect of air pollution on the brain have potential to enhance our understanding of this relationship and to inform potential biological mechanisms. OBJECTIVES: Biomarkers of systemic and CNS inflammation may address whether air pollution exposure induces inflammation, with potential for CNS negative effects. MATERIALS AND METHODS: Twenty-seven healthy adults were exposed to two conditions: filtered air (FA) and diesel exhaust (DE) (300 μg PM2.5/m(3)) for 120 min, in a double-blinded crossover study with exposures separated by four weeks. Prior to and at 0, 3, and 24 h following each exposure, serum and plasma were collected and analyzed for inflammatory cytokines interleukin 6 (IL-6) and tumour necrosis factor alpha (TNF-α), the astrocytic protein S100b, the neuronal cytoplasmic enzyme neuron-specific enolase (NSE), and serum brain-derived neurotrophic factor (BDNF). We hypothesized that IL-6, TNF-α, S100b and NSE would increase, and BDNF would decrease, following DE exposure. RESULTS: At no time-point following exposure to DE was a significant increase in concentration from baseline seen for IL-6, TNF-α, S100b, or NSE relative to FA exposure. Similarly, no significant decrease in BDNF concentration from baseline was seen following DE exposure, relative to FA. Furthermore, the repeated measures ANOVA considered for all time-points and biomarkers revealed no significant time-exposure interaction. DISCUSSION AND CONCLUSION: These results suggest that short-term exposure to DE amongst healthy adults does not acutely affect the systemic or CNS biomarkers that we measured.
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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 | 0.001 |
| 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".