Are circulating cytokine responses to exercise in the heat augmented in older men?
Bibliographic record
Abstract
Age-related chronic low-grade inflammation may render older individuals more susceptible to heat illnesses. The purpose of this study was to examine the influence of intermittent work in the heat on the circulating cytokine responses of older workers. Fourteen young (aged 25.6 ± 0.7 years) and older (aged 57.7 ± 1.5 years) males, matched for body surface area, cycled for 4 × 15 min (separated by 15-min rest) at moderate to heavy intensity (400 W heat production) in warm/dry (35 °C, 20% relative humidity (RH)) and warm/humid (35 °C, 60% RH) conditions. Rectal (Tre) and mean skin (MTsk) temperatures and heart rate were measured continuously, ratings of perceived exertion and thermal sensation recorded at the end of each exercise bout, and blood samples at baseline (PRE) and following the final 60-min recovery (POST) were analyzed for interleukin (IL)-6, tumor necrosis factor (TNF)-α, and percent changes in blood (BV) and plasma (PV) volumes. No differences were observed between the age groups for Tre, MTsk, heart rate, perceptual strain, or percentage of changes in BV, PV, or ΔTNF-α. Under both conditions, the older males had elevated IL-6 and TNF-α (PRE, POST) compared with the young males. ΔIL-6 tended to be greater in the warm/humid condition (+2.53 ± 0.49 and +1.52 ± 0.41 pg·mL(-1)) compared with the warm/dry condition (+1.02 ± 0.13 and +0.68 ± 0.18 pg·mL(-1)) for older but not young males, respectively. Young and older males experienced similar thermal, cardiovascular, and perceptual strain within the warm/dry and warm/humid conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".