Effect of pharmacological lowering of plasma urate on exercise-induced oxidative stress
Bibliographic record
Abstract
Urate is a metabolic end product of purine metabolism that contributes about 66% of the antioxidant capacity of plasma. The objective of this study was to evaluate the importance of plasma urate as an antioxidant using pharmacological lowering and examining the impact on plasma antioxidant capacity and oxidative stress after intense exercise. Fifteen subjects ran for 45 min at ~80% VO2 max under the influence of probenecid (1 g/d) (PRO) or placebo (PLA) in a double-blind, crossover design. Blood samples obtained at baseline, pre-exercise, and immediately post-exercise were analyzed for F2-isoprostanes, lipid hydroperoxides (LHs), ferric-reducing ability of plasma (FRAP), urate, ascorbate (AA), and nitrite. A 2 (group) × 2 (time) repeated-measures analysis of variance (ANOVA), one-way ANOVA, Tukey–Kramer multiple comparison tests, and Student’s t tests were used for statistical analysis. PRO exhibited lowered urate and FRAP compared with baseline (p ≤ 0.05), and group effects existed for the exercise trials (p = 0.023 and p ≤ 0.001, respectively) versus PLA. F2-isoprostanes, nitrite, and AA were increased after exercise (p = 0.004, p = 0.001, and p = 0.003, respectively), but the pattern of change was not different between treatments. This study indicates that plasma markers of exercise-induced oxidative stress were not affected by below-normal physiological concentrations of urate and a diminished antioxidant capacity within the plasma compartment.
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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) | 0.001 | 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 | 0.001 |
| 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".