Hypercapnia induces dilation of large cerebral arteries and is mediated via a non‐selective cyclooxygenase pathway (LB704)
Bibliographic record
Abstract
Dogma in human physiology posits that hypercapnia induces arteriolar vasodilation, with little to no vasomotion in large cerebral arteries. Assuming that the middle cerebral artery (MCA) diameter remains unchanged during hypercapnia has validated use of transcranial Doppler ultrasound (TCD) as a surrogate measure of cerebral blood flow. We hypothesized that dilation of the internal carotid artery (ICA) would occur with hypercapnia and be mediated, in part, via a cyclooxygenase pathway. Moreover, we reasoned that MCA velocity (MCAv) reactivity would underestimate ICA flow reactivity. Before and 90 min following oral indomethacin (INDO; 1.45±0.2 mg/kg; mean ± SD), concurrent vascular ultrasound measures of beat‐to‐beat flow, diameter, and velocity through the ICA, and blood velocity in the downstream MCA (n=7) were made at rest and during incremental steady‐state hyperoxic (300mmHg PETO2) hypercapnia (+3, +6, and +9 mmHg PETCO2). The reactivity of ICA flow to hypercapnia (8.2±1.8 %/mmHg) was greater (P<0.01) than the velocity reactivity in the ICA and MCA (5.5±2.5 vs. 4.4±1.3%/mmHg, respectively; P=0.13). This discrepancy between flow and velocity reactivity was explained by a progressive dilation in ICA diameter with hypercapnia (1.0±0.5 %/mmHg). Following INDO, the slope of ICA dilation was reduced by 41.50% (P<0.05). The significance of these findings are: 1) the underestimation of MCAv reactivity compared to ICA flow reactivity suggests that the MCA dilates in response to hypercapnia: and 2) ~40% of hypercapnic ICA dilation is mediated by a non‐selective cyclooxygenase pathway. Grant Funding Source : Supported by the Natural Sciences and Engineering Research Council of Canada
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".