Hot water immersion increases internal carotid artery shear rate but does not alter intracranial vascular reactivity to carbon dioxide
Bibliographic record
Abstract
Acute hot water immersion (HWI) increases cerebral blood flow, which may increase arterial shear rate and benefit cerebrovascular function. However, it is unclear whether HWI alters cerebral artery shear rate and cerebrovascular reactivity to carbon dioxide (CVRCO2). We tested the hypotheses that HWI (39 °C) increases extracranial artery shear rate and intracranial artery hypercapnic CVRCO2, but reduces hypocapnic CVRCO2 compared to temperate water immersion (TWI; 35 °C). Eighteen healthy adults completed two experimental visits. Middle and posterior cerebral artery blood velocities (transcranial Doppler; MCAv and PCAv) were continuously recorded. Right internal carotid artery (ICA) and vertebral artery (VA) shear rate were obtained via Doppler ultrasound. Hypocapnic and hypercapnic CVRCO2 were assessed in the middle cerebral artery (MCA) and posterior cerebral artery (PCA) during self-paced hyperventilation and during 30 s of 7% CO2 inhalation. Measures were completed pre-immersion (PRE) and at 1.0 °C increase in core temperature during HWI and time-matched during TWI. Data are reported as mean ± SD. There were no differences between conditions at PRE. MCAv (64 ± 12 vs. 55 ± 9 cm/s; P = 0.01) and PCAv (39 ± 7 vs. 29 ± 5 cm/s; P < 0.01) were greater in TWI versus HWI at the 1.0 °C time point. ICA shear rate was greater in HWI versus TWI at 1.0 °C (247 ± 51 vs. 180 ± 43 s−1; P < 0.01) but VA shear did not differ. Hypocapnic and hypercapnic CVRCO2 in the MCA and PCA did not differ between conditions. Compared to TWI, MCAv and PCAv are lower in HWI, but HWI augments shear rate in the ICA, which may be beneficial for cerebrovascular health if done recurrently.
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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".