Differential Changes with T Cell Subsets And Cytokine Production In Response To Endurance Exercise In Trained And Untrained Women
Bibliographic record
Abstract
PURPOSE: To investigate T cell immunity following 60 minutes of endurance exercise in trained and untrained women. METHODS: Compared 3 exercise conditions: 1) untrained females (UT) cycled at an intensity = 20 w below their ventilatory threshold (VT; ∼90 w); 2) trained females (TE1) cycled at the same PO as the UT (∼90 w); and 3) the same trained females cycled at 20 w below their own VT (TE2; ∼155 w). Blood samples were taken at rest, immediately after exercise, and following 2 hours of recovery. RESULTS: Proportion of CD3+CD4+ (Th) cells numbers were higher in the TE2 group compared to the UT group and had increased from T60 to T1 80 in the TE1 and TE2 exercise conditions (p < 0.05). The proportion CRTH2+ (Th2) cells decreased at T60 from T0 in the TE1 condition, but not in the UT condition. IL-12 and IFN-γ concentrations increased following TE1 and TE2 conditions (p < 0.05). IL-2 increased in the TE2 and decreased in the UT condition (p < 0.05). IL-10 concentrations were lower in the TE1 compared to TE2 (p < 0.05). CONCLUSIONS: The findings indicate that T cell immunity, in particular Th1 immunity, was more pronounced in the trained women. Th2 immunity was favoured in the untrained women following endurance exercise. Therefore, T cell immunity is altered in females following acute endurance exercise and is influenced by fitness level.
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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) | 0.002 | 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".