Quantitative Magnetic Resonance Imaging Assessment of Matrix Development in Cell-Seeded Natural Urinary Bladder Smooth Muscle Tissue-Engineered Constructs
Bibliographic record
Abstract
The approach of cell-seeded natural scaffolds holds great promise for tissue engineering complicated soft-tissue organs such as the urinary bladder and heart. However, relatively little is known about cell-natural scaffold interactions or their influence on magnetic resonance imaging (MRI) characterization, which is valuable for noninvasive monitoring. Ideally, MRI should provide information on tissue biochemistry in addition to structure and function. In this study, quantitative MRI was performed on control and smooth muscle cell-seeded natural bladder matrices at different time points up to 7 days postseeding. Measurements of MR relaxation times (T1 and T2) and diffusion coefficient (D) showed an overall change that was incompatible with cell presence. Multicomponent T2 provided greater specificity, revealing time-course changes in the short T2 fraction that were consistent with biochemically determined matrix degradation from collagenase released from seeded cells. These matrix alterations are noted for the first time, and their relatively early occurrence may be unique to soft-tissue matrices compared with synthetic materials. More importantly, they are not evident on histology but are revealed on quantitative MRI. We conclude that quantitative MRI may provide specific information on cell-matrix interaction and is a promising noninvasive approach to understand and monitor cell-seeded natural scaffold-based regeneration.
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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 | 0.001 |
| 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".