Cerebrovascular Regulation During Transient Hypotension and Hypertension in Humans
Bibliographic record
Abstract
The cerebrovasculature dilates or constricts in response to acute blood pressure changes to stabilize cerebral blood flow across a range of blood pressures. It is unclear, however, whether such dynamic cerebral autoregulation (dCA) is equally effective in responding to falling versus rising blood pressure. In this study we applied a pharmacological approach to evaluate dCA gain to transient hypotension and hypertension and compared this method with 2 established indices of dCA that do not explicitly differentiate between dCA efficacy and falling versus rising blood pressure. Middle cerebral arterial velocity and blood pressure recordings were made in 26 healthy volunteers randomized to 2 protocols. In 10 subjects, dCA gain to transient hypotension induced with intravenous nitroprusside was compared with dCA gain to transient hypertension induced with intravenous phenylephrine. In 16 subjects, dCA gain to transient hypotension induced with intravenous nitroprusside was compared with the rate of regulation and autoregulatory index derived from transient hypotension induced with the thigh cuff deflation technique. dCA gain to transient hypotension induced with intravenous nitroprusside was unrelated to dCA gain to transient hypertension induced with intravenous phenylephrine (r=0.06; P=0.87) and was consistently greater than dCA gain to transient hypertension induced with intravenous phenylephrine (0.57+/-0.16 versus 0.31+/-0.20 cm/s per millimeter of mercury; P<0.01). However, dCA gain to transient hypotension induced with intravenous nitroprusside was inversely related to the rate of regulation (r=-0.52; P=0.037) and autoregulatory index (r=-0.66; P=0.005). These data indicate that, under our laboratory conditions, dCA appears to be inherently nonlinear with disparate efficacy against rising and falling blood pressure, and dCA gain derived from pharmacologically induced transient hypotension correlates with established nonpharmacological indices of dCA.
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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) | 0.000 | 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".