Pulse pressure oscillations regulate cerebrovascular reactivity to flow (1070.5)
Bibliographic record
Abstract
In vivo, arterial blood pressure is oscillatory and pulsated. This pulse pressure (PP) corresponds to the difference between the systolic and diastolic pressure, and its frequency is dictated by heart rate (bpm). Our aim was to determine, in vitro, the unknown impact of PP (30 mm Hg, 550 bpm) on endothelial shear stress sensitivity (flow‐mediated dilatation) of mouse cerebral arteries. Methods/results: We developed an original system that generates a pulse‐wave adjustable in amplitude and frequency, and can be connected to a pressure arteriograph. Middle cerebral arteries (≍160 µm diameter, n=10 per group) were isolated from 3‐month old C57Bl6 mice and pressurized at 60 or 100 mm Hg either in static pressure (SP) or PP conditions. At 60 mm Hg, pre‐constricted arteries similarly dilated to an increase in flow up to a shear stress of 15 dyn/cm2, to 59±6% of the maximal diameter in PP, and 52±10% in SP conditions. In response to a higher shear stress of 20 dyn/cm2, however, arteries further dilated in PP (73±7%; p<0.05) unlike in SP (43±9%) conditions. At 100 mm Hg, arteries dilated in response to low shear stress (8 dyn/cm2) but less at high shear stresses (20 dyn/cm2): dilation was reduced by 50% in SP (from 44±11% to 23±11%; p<0.05) and 75% in PP (from 34±3% to 8±4%; p<0.05) conditions. Conclusion: PP increases the sensitivity of the endothelium to shear stress at physiological pressure (60 mm Hg) while it limits its sensitivity to high shear stress at high pressure (100 mm Hg), suggesting that PP regulates the endothelial function to critically adjust vessel diameter and flow to neuronal metabolic demand. Grant Funding Source : CIHR
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".