Evaluation of eXIA 160XL cardiac‐related enhancement in C57BL/6 and BALB/c mice using micro‐CT
Bibliographic record
Abstract
Evaluation of cardiovascular function in mice using micro-CT requires that a contrast agent be administered to differentiate the blood from the myocardium. eXIA 160, an aqueous colloidal poly-disperse contrast agent with a high iodine concentration (160 mg I ml(-1)), creates strong contrast between blood and tissue with a low injection volume. In this study, the blood-pool enhancement time-course of eXIA 160 is monitored over a 48 h period to determine its optimal use during cardiac function studies in C57BL/6 and BALB/c mice. Eight-second scans were performed (80 kV(p), 110 mA) using the GE Locus Ultra micro-CT scanner. Six C57BL/6 and six BALB/c male mice (22-24 g) were injected via tail vein with 5 µl g(-1) body weight eXIA 160. A precontrast scan was performed; following injection, mice were scanned at 5, 15, 30, 45 and 60 min, and 2, 4, 8, 12, 24 and 48 h. Images were reconstructed, and enhancement-time curves were generated for each of the following tissues: left ventricle (LV), myocardium, liver, spleen, renal cortex, bladder and brown adipose tissue. The highest contrast in the LV occurred at 5 min in both strains (~670 HU above precontrast value). Uptake of the contrast agent by the myocardium was also observed: myocardial tissue showed increasing enhancement over a 4 h period in both strains, remaining even once the contrast was eliminated from the vasculature. In both C57BL/6 and BALB/c strains, eXIA 160 provided high contrast between blood and myocardial tissue for a period of 30 min following injection. Notably, this contrast agent was also taken up by the myocardium and provided continued enhancement when it was eliminated from the blood, making LV wall motion studies possible. In conclusion, eXIA 160, with its high iodine concentration and targeted tissue uptake characteristics, is an ideal agent to use when evaluating cardiovascular function in mice.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".