Effects of air pollution exposure on inflammatory and endurance performance in recreationally trained cyclists adapted to traffic-related air pollution
Bibliographic record
Abstract
The aim of the present study was to analyze the effects of traffic-related air pollution (TRAP) on markers of inflammatory, neuroplasticity, and endurance performance-related parameters in recreationally trained cyclists who were adapted to TRAP during a 50-km cycling time trial (50-km cycling TT). Ten male cyclists performed a 50-km cycling TT inside an environmental chamber located in downtown Sao Paulo (Brazil), under TRAP or filtered air conditions. Blood samples were obtained before and after the 50-km cycling TT to measure markers of inflammatory [interleukin-6 (IL-6), C-reactive protein (CRP), interleukin-10 (IL-10), intercellular adhesion molecule-1 (ICAM-1)] and neuroplasticity [brain-derived neurotrophic factor (BDNF)]. Rating of perceived exertion (RPE), heart rate (HR), and power output (PO) were measured throughout the 50-km cycling TT. There were no significant differences between experimental conditions for responses of IL-6, CRP, and IL-10 ( P > 0.05). When compared with exercise-induced changes in filtered air condition, TRAP provoked greater exercise-induced increase in BDNF levels (TRAP = 3.3 ± 2.4-fold change; Filtered = 1.3 ± 0.5-fold change; P = 0.04) and lower exercise-induced increase in ICAM-1 (Filtered = 1.1 ± 0.1-fold change; TRAP = 1.0 ± 0.1-fold change; P = 0.01). The endurance performance-related parameters (RPE, HR, PO, and time to complete the 50-km cycling TT) were not different between TRAP and filtered air conditions ( P > 0.05). These findings suggest that the potential negative impacts of exposure to pollution on inflammatory, neuroplasticity, and performance-related parameters do not occur in recreationally trained cyclists who are adapted to TRAP.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".