Erbium-Based Perfusion Contrast Agent for Small-Animal Microvessel Imaging
Bibliographic record
Abstract
Micro-computed tomography (micro-CT) facilitates the visualization and quantification of contrast-enhanced microvessels within intact tissue specimens, but conventional preclinical vascular contrast agents may be inadequate near dense tissue (such as bone). Typical lead-based contrast agents do not exhibit optimal X-ray absorption properties when used with X-ray tube potentials below 90 kilo-electron volts (keV). We have developed a high-atomic number lanthanide (erbium) contrast agent, with a K-edge at 57.5 keV. This approach optimizes X-ray absorption in the output spectral band of conventional microfocal spot X-ray tubes. Erbium oxide nanoparticles (nominal diameter < 50 nm) suspended in a two-part silicone elastomer produce a perfusable fluid with viscosity of 19.2 mPa-s. Ultrasonic cavitation was used to reduce aggregate sizes to <70 nm. Postmortem intact mice were perfused to investigate the efficacy of contrast agent. The observed vessel contrast was >4000 Hounsfield units, and perfusion of vessels < 10 μ m in diameter was demonstrated in kidney glomeruli. The described new contrast agent facilitated the visualization and quantification of vessel density and microarchitecture, even adjacent to dense bone. Erbium’s K-edge makes this contrast agent ideally suited for both single- and dual-energy micro-CT, expanding potential preclinical research applications in models of musculoskeletal, oncological, cardiovascular, and neurovascular diseases.
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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) | 0.000 | 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 | 0.000 |
| 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".