MRI method for labeling and imaging decellularized extracellular matrix scaffolds for tissue engineering
Bibliographic record
Abstract
Purpose To develop a facile method for labeling and imaging decellularized extracellular matrix (dECM) scaffolds intended for regenerating 3D tissues. Methods A small molecule manganese porphyrin, MnPNH 2 , was synthesized and used to label dECM scaffolds made from porcine bladder and trachea and murine whole lungs. The labeling protocol was optimized on bladder dECM, and imaging on a 3T clinical scanner was performed to assess reductions in T 1 and T 2 relaxation times. In vivo MRI was performed on dECM injected in the rat dorsum to verify sensitivity of detection. Toxicity assays for cell viability, metabolism, and proliferation were performed on human umbilical vein endothelial cells. The incorporation of MnPNH 2 and its long‐term retention in dECM were assessed on transmission electron microscopy and ultraviolet absorbance of eluted MnPNH 2 over time. Results All tissues, including thick whole 3D organs, were uniformly labeled and demonstrated high signal‐to‐noise on MRI. A nearly 10‐fold reduction in T 1 was consistently obtained at a labeling dose of 0.4 mM, and even 0.2 mM provided sufficient contrast in vivo and ex vivo. No toxicity was observed up to 0.4 mM, the maximum tested. Binding studies suggested nonspecific association, and retention studies in the labeled whole decellularized lungs revealed less than 20% MnPNH 2 loss over 30 days, the majority occurring in the first 3 days after labeling. Conclusion The proposed labeling method is the first report for visualizing dECM on MRI and has the potential for long‐term monitoring and optimization of dECM‐based organ tissue engineering.
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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) | 0.001 | 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 | 0.001 |
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".