Evaluating <i>in vivo</i> tissue stiffness of capillaries using Microbubbles Under an Ultrasound Field
Bibliographic record
Abstract
Introduction: We developed a new technique to study in vivo biomechanical properties of capillaries using microbubbles (MBs), ultrasound, and high-speed microscopic imaging. We hypothesized that diabetes stiffens capillaries and that MB acoustic behaviors within capillaries will change as wall stiffness changes. Methods: Type 1 diabetes was induced in rats using streptozotocin. Cremaster muscle was externalized in anesthetized rats. Definity® was intra-arterially injected (40–60 μl boluses). Capillaries were imaged microscopically at 8–12 Mfps, during delivery of one 6–15 cycle ultrasound pulse [f = 1 MHz, PNP = 0.5–2.0 MPa]. Capillary and MB diameters were obtained from image analysis and power spectra were derived. Stress–strain curves were generated using normal stress exerted by MB (derived from the linearized Euler’s equation) and circumferential vessel Green strain. Results: MBs in diabetic capillaries (n = 3) displayed greater subharmonic/fundamental power ratio (p = 0.03) and ultraharmonic/fundamental power ratio (p = 0.01) compared with MBs in healthy capillaries (n = 28). Preliminary elastic modulus analysis (n = 9 control and n = 2 diabetic) showed estimated elastic moduli of 1.7 MPa for healthy and 2.2 MPa for diabetic capillaries. Conclusions: Our preliminary data suggest that the MB frequency spectrum under the US may differ in diabetic capillaries, which may provide a basis for a novel approach to diagnose microvascular disease.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".