Effects of low-intensity and high-intensity cycling with diesel exhaust exposure on soluble P-selectin, E-selectin, I-CAM-1, VCAM-1 and complete blood count
Bibliographic record
Abstract
Background Exposure to particulate matter 2.5 μm or less (PM2.5) that contains transition metals may play a role in systemic oxidative stress and inflammation. Exposure to diesel exhaust (DE) can increase adhesion molecules, which are important in the inflammatory response; however, it is unclear how exercising in DE affects adhesion molecules and how exercise intensity modulates this response. Aim To determine how DE exposure during exercise of varying intensities affects adhesion molecules and markers of systemic inflammation. Methods Eighteen males performed 30 min cycling bouts at low intensity and high intensity (30% and 60% of power at VO2peak (peak oxygen consumption) and a control condition (rest)). Each trial was performed once breathing filtered air (FA) and once breathing DE (300 μg/m3 of PM2.5, six trials in total). Prior to, immediately post, 1 and 2 hours post exposure, blood was drawn to measure parameters of a complete blood count and soluble (s) platelet-Selectin, endothelin-Selectin, intracellular cell adhesion molecule (sICAM)-1 and vascular cell adhesion molecule (sVCAM)-1. Data were analysed using repeated-measures analysis of variance. Results Two hours following high-intensity exercise, sICAM-1 was significantly less in DE compared with FA (p=0.008). Immediately following rest (p=0.013) and high-intensity exercise (p=0.042) in DE, sICAM-1 was significantly greater than immediately following low-intensity exercise in DE. There were no significant differences in other markers between DE and FA. Conclusions Based on this study, healthy individuals may not experience an acute increase in adhesion molecules and systemic inflammatory markers from exercising in DE compared with FA, and higher exercise intensities do not appear to increase the likelihood that DE will affect adhesion molecules and systemic inflammatory markers.
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".