The effect of low and high-intensity cycling in diesel exhaust on flow-mediated dilation, circulating NOx, endothelin-1 and blood pressure
Bibliographic record
Abstract
INTRODUCTION: Exposure to air pollution impairs aspects of endothelial function such as flow-mediated dilation (FMD). Outdoor exercisers are frequently exposed to air pollution, but how exercising in air pollution affects endothelial function and how these effects are modified by exercise intensity are poorly understood. OBJECTIVES: Therefore, the purpose of this study was to determine the effects of low-intensity and high-intensity cycling with diesel exhaust (DE) exposure on FMD, blood pressure, plasma nitrite and nitrate (NOx) and endothelin-1. METHODS: Eighteen males performed 30-minute trials of low or high-intensity cycling (30% and 60% of power at VO2peak) or a resting control condition. For each subject, each trial was performed once while breathing filtered air (FA) and once while breathing DE (300ug/m3 of PM2.5, six trials in total). Preceding exposure, immediately post-exposure, 1 hour and 2 hours post-exposure, FMD, blood pressure and plasma endothelin-1 and NOx concentrations were measured. Data were analyzed using repeated-measures ANOVA and linear mixed model. RESULTS: Following exercise in DE, plasma NOx significantly increased and was significantly greater than FA (p<0.05). Two hours following DE exposure, endothelin-1 was significantly less than FA (p = 0.037) but exercise intensity did not modify this response. DE exposure did not affect FMD or blood pressure. CONCLUSION: Our results suggest that exercising in DE did not adversely affect plasma NOX, endothelin-1, FMD and blood pressure. Therefore, recommendations for healthy individuals to moderate or avoid exercise during bouts of high pollution appear to have no acute protective effect.
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) | 0.002 | 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".