Influence of circulating cytokines on prolactin during slow vs. fast exertional heat stress followed by active or passive recovery
Bibliographic record
Abstract
Prolactin (PRL) has been suggested as an indicator of fatigue during exertional heat stress (EHS), given its strong relationship with body core temperature (T(c)); however, the strength of this relationship during different rates of T(c) increase and subsequent recovery is unknown. In addition, given the influence that systemic cytokines, such as interleukin (IL)-6 and tumor necrosis factor (TNF)-α, have on the pituitary gland, it would be of interest to determine the relationship between PRL, IL-6, and TNF-α during EHS. The purpose was to examine the PRL, IL-6, and TNF-α heat stress responses during slow and fast heating and subsequent resting or cold water immersion recovery. On 4 days, nine individuals walked at ≈ 45% (slow heating) or ran at ≈ 65% (fast heating) maximal oxygen consumption on a treadmill in the heat (40°C, 30% relative humidity) until rectal temperature (T(re)) reached 39.5°C (esophageal temperature; fast = 39.41 ± 0.04°C, slow = 39.82 ± 0.09°C). Post-EHS, subjects were either immersed in 2°C water or rested seated until T(re) returned to 38.0°C. Venous blood, analyzed for PRL, IL-6, and TNF-α, was obtained at rest, during exercise (T(re) 38.0, 39.0, 39.5°C), the start of recovery (≈ 5 min after 39.5°C), and subsequent recovery (T(re) 39.0, 38.0°C). IL-6 exhibited myokine properties, given the greater increases with slow heating and lack of increase in TNF-α. A strong temperature-dependent PRL response during slow and fast heating provides additional support for the use of PRL as a peripheral marker of impending fatigue, which is independent of IL-6 and TNF-α cytokine responses.
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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".