Sex-based effects on the distribution of NK cell subsets in response to exercise and carbohydrate intake in adolescents
Bibliographic record
Abstract
Carbohydrate (CHO) supplementation and female sex independently influence the natural killer (NK) cell response to acute exercise. Consequently, this study sought to elucidate sex-based differences in the distribution of NK cell subsets (i.e., CD56dim and CD56bright) in response to exercise and CHO intake. Twenty-two healthy 14-yr-old girls (n = 11) and boys (n = 11) cycled for 60 min at 70% maximal oxygen consumption while drinking 6% CHO (CT) or flavored water (WT). Blood was collected at rest, during exercise (30 and 60 min), and during recovery (30 and 60 min) to identify CD3- CD56dim and CD3- CD56bright NK cells. The activation marker CD69 was also determined on CD3- CD56+ cells. CD56dim responses, expressed as proportions or cell counts, were greater (P < or = 0.01) in girls by 67 and 105%, respectively. CD56bright cell counts (P = 0.006), but not CD56bright proportions (P = 0.89), were greater in girls by 82%. Both CD56dim and CD56bright subset responses, expressed as proportions or cell counts, were lower (P < or = 0.01) in CT vs. WT by 33-36%. The CD56bright-to-CD56dim ratio decreased at 30 min of exercise but increased during recovery (P < 0.001), with no effect of sex or CHO. Regardless of trial, CD3- CD56+ cells expressed approximately 18% higher levels of CD69 during recovery in girls but not boys (P = 0.03), despite similar proportions and counts of CD69+ cells. These results demonstrate sex-based differences in the distribution of NK cell subsets and activation status in response to exercise, but not CHO intake, and further support the need to control for sex in exercise immunology studies.
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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".