P3A-6 Non-Invasive Micro-Vascular Ultrasound Elastography: Comparisons with M-Mode Strain Measurements in Rat Models of Hypertension
Bibliographic record
Abstract
Noninvasive micro-vascular ultrasound elastography (MicroNIVE) was recently proposed forinsituphenotyping in rat models of hypertension through the assessment of mechanical properties of carotid arteries. This paper reports comparisons between MicroNIVE and M-Mode strain measurements. Brown Norway male rats (n = 5) were investigated over 24 weeks. The common carotid arteries were imaged with an ultrasound biomicroscope equipped with a 40-MHz central frequency probe and an external workstation to collect radio-frequency (RF) data. Time-sequences of RF and M-mode signals were recorded over several consecutive cardiac cycles. MicroNIVE strain cartographies were computed for each pair of successive RF images with the Lagrangian Speckle Model Estimator. Diastolic strain (sD) and systolic strain (ss) parameters were estimated. M- mode strain estimations were computed as sM(t) = (w(t)-wmax)/wmax, with w(t) and wmaxbeing the wall thicknesses at time "t" and at end-diastole, respectively. MicroNIVE diastolic and systolic strains were consistent with a Pearson correlation coefficient (r) of 0.75 (p-11). M-mode and MicroNIVE strain measurements were correlated with r = 0.74 (p-5) between sMand sDand r = 0.67 (p-4) between sMand ss. Corroborated by Bland-Altman plots, M-mode and MicroNIVE were found in very good concordance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".