Determining the fate of seeded cells in venous tissue‐engineered vascular grafts using serial MRI
Bibliographic record
Abstract
ABSTRACT A major limitation of tissue engineering research is the lack of noninvasive monitoring techniques for observations of dynamic changes in single tissue‐engineered constructs. We use cellular magnetic resonance imaging (MRI) to track the fate of cells seeded onto functional tissue‐engineered vascular grafts (TEVGs) through serial imaging. After in vitro optimization, murine macrophages were labeled with ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles and seeded onto scaffolds that were surgically implanted as inferior vena cava interposition grafts in SCID/bg mice. Serial MRI showed the transverse relaxation times ( T 2 ) were significantly lower immediately following implantation of USPIO‐labeled scaffolds ( T 2 =44±6.8 vs . 71±10.2 ms) but increased rapidly at 2 h to values identical to control implants seeded with unlabeled macrophages ( T 2 =63±12 vs . 63±14 ms). This strongly indicates the rapid loss of seeded cells from the scaffolds, a finding verified using Prussian blue staining for iron containing macrophages on explanted TEVGs. Our results support a novel paradigm where seeded cells are rapidly lost from implanted scaffolds instead of developing into cells of the neovessel, as traditionally thought. Our findings confirm and validate this paradigm shift while demonstrating the first successful application of noninvasive MRI for serial study of cellular‐level processes in tissue engineering.—Harrington, J. K., Chahboune, H., Criscione, J. M., Li, A. Y., Hibino, N., Yi, T., Villalona, G. A., Kobsa, S., Meijas, D., Duncan, D. R., Devine, L., Papademetri, X., Shin'oka, T., Fahmy, T. M., Breuer, C. K. Determining the fate of seeded cells in venous tissue engineered vascular grafts using serial MRI. FASEB J. 25, 4150–4161 (2011). www.fasebj.org
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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 | 0.000 | 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) | 0.000 | 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 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".