Influence of Hypercapnia and Skin Temperature on Pulmonary Ventilation in Hyperthermic Humans
Bibliographic record
Abstract
Hyperthermia potentiates the influence of CO 2 on pulmonary ventilation (V E ). It remains to be resolved how mean skin (T SK ) and core temperatures contribute to elevated exercise ventilation response to CO 2 . PURPOSE To assess influences of T SK and end‐tidal CO 2 (P ET CO 2 ) on the ventilatory equivalent for oxygen (V E /VO 2 ) during exercise with a hyperthermic rectal temperature (T RE ). HYPOTHESIS During hyperthermia mean skin temperature will positively interact with P ET CO 2 in their influence on exercise ventilation. METHODS Six participants 1.78 ± 0.13 m (mean ± SD) in height, who weighed 74.7 ± 16.5 kg and were 24.3 ± 2.3 yrs of age performed three 1 h exercise trials on each of 2 days in a climatic chamber. For each exercise session conditions were maintained at an RH of 29.5 ± 8.9 % and ambient temperatures of one of 25, 30, or 35°C. This gave 3 significantly different T SK levels (F=15.8, p=0.001) between ~33 and ~36°C. In each trial the volunteer breathed eucapnic air for 5 min during rest before cycling on an ergometer at either ~50 W (normothermic T RE ) or at ~150 W (hyperthermic T RE ). Once T RE stabilized the volunteers breathed hypercapnic air twice for ~5 min with P ET CO 2 elevated to ~+4 and ~+8 mmHg. RESULTS In the normothermic T RE condition there was a significant effect of P ET CO 2 (P<0.05) but no effect of T SK on ventilation. In the hyperthermic T RE condition for V E /VO 2 there were main effects of T SK (F=4.0, p=0.06) and P ET CO 2 (F=13.2, p<0.0001) as well as a significant positive interaction (F=3.7, p=0.01) between T SK and P ET CO 2 . CONCLUSION During exercise with a steady state hyperthermic core temperature, skin temperatures and P ET CO 2 positively interact in their influence on exercise ventilation. Supported by NSERC, Canadian Foundation for Innovation.
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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 | 0.001 |
| 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".