Comprehensive Unbiased Analysis of Vascular Tissue Changes in Accelerated Atherosclerosis Using High-Resolution Ultrasound combined with Photoacoustic Imaging
Bibliographic record
Abstract
Abstract Venous bypass grafts are commonly used to circumvent complex coronary or peripheral artery occlusions. The patency rates, however, are hampered due to accelerated buildup of atherosclerotic lesions in the vein graft wall. Identification of unstable plaques is crucial to guide clinical decision making. In this study, we employ advanced high-resolution ultrasound (US) coupled with spectral photoacoustic imaging (sPAI) to enhance the accurate visualization and analysis of tissue composition in vivo . By applying unbiased spectral analysis, we investigate the composition and plaque instability in a murine vein graft model. Method Male hypercholesterolemic ApoE3*Leiden mice and normocholesterolemic C57BL/6 mice underwent vein graft surgery in which a caval vein from a donor mouse was interpositioned into the arterial circulation of a recipient at the sight of the right common carotid artery. US imaging with sPAI was conducted on 7, 14, 21, and 28 days after surgery. Spectral curves from the near-infrared (NIR) I region, spanning 680 to 970nm, were extracted using a data-driven approach. Component discovery and cross-correlation analysis were performed with Matlab, and ImageJ reconstructed the components within 3D images. At the endpoint histological analysis of the vein grafts was performed. Results Analysis of the NIRI region revealed distinct components, with 7 and 10 components tested in the cross-correlation map. Relative abundance values identified melanin, oxidized hemoglobin, deoxygenized hemoglobin, lipids, and collagen. Lipids and collagen spectra accurately identified lipid and collagen-rich tissues in vivo . The sPAI analysis of of the vein graft wall in vivo resulted in a 8.7% lipids in the vein graft wall compared to 1.8% lipids in the histological analysis at t=28d. For vein grafts from ApoE*3-Leiden mice no differences in the lipid positive area was observed between the sPAI analysis or histological quantification. The percentages collagen present in the vein graft walls from both strains analyzed via sPAI and histological showed comparable results at t=28d. Conclusion Our study demonstrates that sPAI can be utilized for compositional analysis of murine tissue in an unbiased manner. This methodology can be used to enhance our understanding of vein graft dynamics and holds promise to advance non-invasive characterization of vascular diseases to ultimately guide clinical decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".