Effect of blueberry ingestion on natural killer cell counts, oxidative stress, and inflammation prior to and after 2.5 h of running
Bibliographic record
Abstract
Blueberries are rich in antioxidants known as anthocyanins, which may exhibit significant health benefits. Strenous exercise is known to acutely generate oxidative stress and an inflammatory state, and serves as an on-demand model to test antioxidant and anti-inflammatory compounds. The purpose of this study was to examine whether 250 g of blueberries per day for 6 weeks and 375 g given 1 h prior to 2.5 h of running at ∼72% maximal oxygen consumption counters oxidative stress, inflammation, and immune changes. Twenty-five well-trained subjects were recruited and randomized into blueberry (BB) (N = 13) or control (CON) (N = 12) groups. Blood, muscle, and urine samples were obtained pre-exercise and immediately postexercise, and blood and urine 1 h postexercise. Blood was examined for F₂-isoprostanes for oxidative stress, cortisol, cytokines, homocysteine, leukocytes, T-cell function, natural killer (NK), and lymphocyte cell counts for inflammation and immune system activation, and ferric reducing ability of plasma for antioxidant capacity. Muscle biopsies were examined for glycogen and NFkB expression to evaluate stress and inflammation. Urine was tested for modification of DNA (8-OHDG) and RNA (5-OHMU) as markers of nucleic acid oxidation. A 2 (treatment) × 3 (time) repeated measures ANOVA was used for statistical analysis. Increases in F₂-isoprostanes and 5-OHMU were significantly less in BB and plasma IL-10 and NK cell counts were significantly greater in BB vs. CON. Changes in all other markers did not differ. This study indicates that daily blueberry consumption for 6 weeks increases NK cell counts, and acute ingestion reduces oxidative stress and increases anti-inflammatory cytokines.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".