ULTRASOUND MICRO-ELASTOGRAPHY: A NEW IMAGING MODALITY TO PHENOTYPE HYPERTENSION IN RAT MODELS
Bibliographic record
Abstract
New ultrasound imaging methods are proposed to non-invasively characterize the mechanical properties of superficial arteries (MicroNIVE) and kidneys (MicroNIKE) in rodents. In MicroNIVE, the vessel wall is compressed/dilated by the blood flow pulsation, whereas time-sequences of high-resolution radio-frequency (RF) ultrasound data are externally acquired. The kinematics of the vascular tissue, assessed with the Lagrangian Speckle Model Estimator (LSME), provides a strain cartography also known as elastogram. Because the LSME assumes linear elasticity conditions, strain is inversely proportional to stiffness, which is an intermediate phenotype of the hypertension (HT) trait. Results are presented for the common carotid artery of spontaneously hypertensive rats (SHR, n = 5) and control normotensive Brown Norway (BN, n = 5) rats. At 15-weeksold, the SHR rats'carotid artery (4.46 ± 1.79% of strain) was found, on average, stiffer that of the BN's, which exhibited strains of 6.76 ± 1.48% (p < .059). On the other hand, in MicroNIKE, the kidney is externally compressed with the ultrasound probe while time-sequences of high-resolution RF data are acquired. For the purpose of investigating the feasibility of MicroNIKE, a fresh excised kidney from a Recombinant Inbred (RI) rat was investigated. The elastograms, computed with the LSME, clearly exhibited the medulla with distinct mechanical properties. It is concluded that MicroNIVE and MicroNIKE are promising new imaging tools to non-invasively and longitudinally study the impact of targeted genes on vascular tissue remodeling and nephroangiosclerosis in engineered rat models of HT.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".