Cerebral Vascular Control and Metabolism in Heat Stress
Bibliographic record
Abstract
This review provides an in-depth update on the impact of heat stress on cerebrovascular functioning. The regulation of cerebral temperature, blood flow, and metabolism are discussed. We further provide an overview of vascular permeability, the neurocognitive changes, and the key clinical implications and pathologies known to confound cerebral functioning during hyperthermia. A reduction in cerebral blood flow (CBF), derived primarily from a respiratory-induced alkalosis, underscores the cerebrovascular changes to hyperthermia. Arterial pressures may also become compromised because of reduced peripheral resistance secondary to skin vasodilatation. Therefore, when hyperthermia is combined with conditions that increase cardiovascular strain, for example, orthostasis or dehydration, the inability to preserve cerebral perfusion pressure further reduces CBF. A reduced cerebral perfusion pressure is in turn the primary mechanism for impaired tolerance to orthostatic challenges. Any reduction in CBF attenuates the brain's convective heat loss, while the hyperthermic-induced increase in metabolic rate increases the cerebral heat gain. This paradoxical uncoupling of CBF to metabolism increases brain temperature, and potentiates a condition whereby cerebral oxygenation may be compromised. With levels of experimentally viable passive hyperthermia (up to 39.5-40.0 °C core temperature), the associated reduction in CBF (∼ 30%) and increase in cerebral metabolic demand (∼ 10%) is likely compensated by increases in cerebral oxygen extraction. However, severe increases in whole-body and brain temperature may increase blood-brain barrier permeability, potentially leading to cerebral vasogenic edema. The cerebrovascular challenges associated with hyperthermia are of paramount importance for populations with compromised thermoregulatory control--for example, spinal cord injury, elderly, and those with preexisting cardiovascular diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".