Evaluation of Cerebrovascular Impedance and Wave Reflection in Mouse by Ultrasound
Bibliographic record
Abstract
Genetic and surgical mouse models are commonly used to study cerebrovascular disease, but their size makes invasive hemodynamic testing technically challenging. The purpose of this study was to demonstrate a noninvasive measurement of cerebrovascular impedance and wave reflection in mice using high-frequency ultrasound in the left common carotid artery (LCCA), and to examine whether microvascular changes associated with hypercapnia could be detected with such an approach. Ten mice (C57BL/6J) were studied using a high-frequency ultrasound system (40 MHz). Lumen area and blood flow waveforms were obtained from the LCCA and used to calculate pulse-wave velocity, input impedance, and reflection amplitude and transit time under both normocapnic and hypercapnic (5% CO2) ventilation. With hypercapnia, vascular resistance was observed to decrease by 87%±12%. Although the modulus of input impedance was unchanged with hypercapnia, a phase decrease indicative of increased total arterial compliance was observed at low harmonics together with an increased reflection coefficient in both the time (0.57±0.08 versus 0.68±0.08, P=0.04) and frequency domains (0.62±0.08 versus 0.73±0.06, P=0.02). Interestingly, the majority of LCCA blood flow was found to pass into the internal carotid artery (range=76% to 90%, N=3), suggesting that hemodynamic measurements in this vessel are a good metric for intracerebral reactivity in mouse.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".