Early animal model evaluation of an implantable contrast agent to enhance magnetic resonance imaging of arterial bypass vein grafts
Bibliographic record
Abstract
Background Non-invasive monitoring of autologous vein graft (VG) bypass grafts is largely limited to detecting late luminal narrowing. Although magnetic resonance imaging (MRI) delineates vein graft intima, media, and adventitia, which may detect early failure, the scan time required to achieve sufficient resolution is at present impractical. Purpose To study VG visualization enhancement in vivo and delineate whether a covalently attached MRI contrast agent would enable quicker longitudinal imaging of the VG wall. Material and Methods Sixteen 12-week-old male C57BL/6J mice underwent carotid interposition vein grafting. The inferior vena cava of nine donor mice was treated with a gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA)-based contrast agent, with control VGs labeled with a vehicle. T1-weighted (T1W) MRI was performed serially at postoperative weeks 1, 4, 12, and 20. A portion of animals was sacrificed for histopathology following each imaging time point. Results MRI signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were significantly higher for treated VGs in the first three time points (1.73 × higher SNR, P = 0.0006, and 5.83 × higher CNR at the first time point, P = 0.0006). However, MRI signal enhancement decreased consistently in the study period, to 1.29 × higher SNR and 2.64 × higher CNR, by the final time point. There were no apparent differences in graft morphometric analyses in Masson's trichrome-stained sections. Conclusion A MRI contrast agent that binds covalently to the VG wall provides significant increase in T1W MRI signal with no observed adverse effects in a mouse model. Further optimization of the contrast agent to enhance its durability is required.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".