Natural killer cell lytic activity and CD56<sup>dim</sup>and CD56<sup>bright</sup>cell distributions during and after intensive training
Bibliographic record
Abstract
The purpose of this study was to examine the impact of intensive training for competitive sports on natural killer (NK) cell lytic activity and subset distribution. Eight female college-level volleyball players undertook 1 mo of heavy preseason training. Volleyball drills were performed 5 h/day, 6 days/wk. Morning resting blood samples were collected before training (Pre), on the 10th day of training (During), 1 day before the end of training (End), and 1 wk after intensive training had ceased (Post). CD3(-)CD16(bright)CD56(dim) (CD56(dim) NK), CD3(-)CD16(dim/-)CD56(bright) NK (CD56(bright) NK), and CD3(+)CD16(-)CD56(dim) (CD56(dim) T) cells in peripheral blood were determined by flow cytometry. The circulating count of CD56(dim) NK cells (the predominant population, with a high cytotoxicity) did not change, nor did the counts for other leukocyte subsets. However, counts for CD56(bright) NK and CD56(dim) T cells (subsets with a lower cytotoxicity) increased significantly (P < 0.01) in response to the heavy training. Overall NK cell cytotoxicity decreased from Pre to End (P = 0.002), with a return to initial values at Post. Lytic units per NK cell followed a similar pattern (P = 0.008). Circulating levels of interleukin-6, interferon-gamma, and tumor necrosis factor-alpha remained unchanged. These results suggest that heavy training can decrease total NK cell cytotoxicity as well as lytic units per NK cell. Such effects may reflect in part an increase in the proportion of circulating NK cells with a low cytotoxicity.
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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".