Recent insights into mechanisms of hypoxia‐induced vasodilatation in the human brain
Bibliographic record
Abstract
Abstract The cerebral vasculature manages oxygen delivery by adjusting arterial blood in‐flow in the face of reductions in oxygen availability. Hypoxic cerebral vasodilatation, and the associated hypoxic cerebral blood flow reactivity, involve many vascular, erythrocytic and cerebral tissue mechanisms that mediate elevations in cerebral blood flow via micro‐ and macrovascular dilatation. This contemporary review focuses on in vivo human work – with reference to seminal preclinical work where necessary – on hypoxic cerebrovascular reactivity, particularly where recent advancements have been made. We provide updates with the following information: in humans, hypoxic cerebral vasodilatation is partially mediated via a – likely non‐obligatory – combination of: (1) nitric oxide synthases, (2) deoxygenation‐coupled S‐nitrosothiols, (3) potassium channel‐related vascular smooth muscle hyperpolarization, and (4) prostaglandin mechanisms with some contribution from an interrelationship with reactive oxygen species. And finally, we discuss the fact that, due to the engagement of deoxyhaemoglobin‐related mechanisms, reductions in O2 content via haemoglobin per se seem to account for ∼50% of that seen with hypoxic cerebral vasodilatation during hypoxaemia. We further highlight the issue that methodological impediments challenge the complete elucidation of hypoxic cerebral reactivity mechanisms in vivo in healthy humans. Future research is needed to confirm recent advancements and to reconcile human and animal findings. Further investigations are also required to extend these findings to address questions of sex‐, heredity‐, age‐, and disease‐related differences. The final step is to then ultimately translate understanding of these mechanisms into actionable, targetable pathways for the prevention and treatment of cerebral vascular dysfunction and cerebral hypoxic brain injury. image
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".