Nonlinear Optical Microscopy of Murine Abdominal Aortic Aneurysm
Bibliographic record
Abstract
Abdominal aortic aneurysm (AAA) is a cardiovascular disease characterized by dilation and weakening of the vessel wall. AAA rupture is responsible for approximately 14,000 deaths annually in the United States [1]. Nonlinear optical (NLO) microscopy presents new possibilities for analyzing AAA tissue samples from murine models. Common NLO techniques are two-photon excitation fluorescence (TPEF), which detects the intrinsic autofluorescent properties of elastin, and second-harmonic generation (SHG), which is specific for collagen fibrils. Elastin and collagen, two major extracellular matrix components, help the aortic wall withstand internal pressure. Murine AAAs were created through 1) subcutaneous continuous systemic infusion of angiotensin II (AngII) in hyperlipidemic apolipoprotein E-deficient mice and 2) by intraluminal infusion of elastase (low 0.5 U/ml and high 25 U/ml concentrations) into the infrarenal aorta of rats [2]. We imaged aneurysmal and control tissue using TPEF and SHG and compared the resulting images to sections stained with standard elastin and collagen markers. TPEF images revealed disorganized elastin sheets and SHG images indicated collagen turnover after aneurysm formation. We quantified the relative degree of elastin degradation and collagen content in the aortic media within a user-defined area on sections stained with Verhoeff-van Gieson (VVG) or Masson’s trichrome (MTC), as well as on TPEF and SHG images. Our analysis with VVG-stained sections shows that elastin content in AAA tissue is significantly decreased by 64% in AngII models (P=0.02), by 34% in low concentration elastase models (P=0.07), and by 99% in high concentration elastase models (P=0.03), relative to control aortic tissue.
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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 | 0.001 | 0.000 |
| 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) | 0.004 | 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".