MRI tracking of human Wharton’s jelly stem cells seeded onto acellular dermal matrix labeled with superparamagnetic iron oxide nanoparticles in burn wounds
Bibliographic record
Abstract
Abstract Background In vivo cell tracking after transplantation in regenerative medicine remains an unmet challenge and limits current understanding of the wound healing mechanism through cell-based therapies. This study investigated tracking of human Wharton’s jelly stem cells (hWJSCs) seeded onto an acellular dermal matrix (ADM) and labeled with superparamagnetic iron oxide nanoparticles (SPIONs) by magnetic resonance imaging (MRI) in burn injury. Method The hWJSCs were characterized and assessed for growth kinetics. A total of 30 rats were enrolled in three equal groups. Group 1 underwent scald burn injury left without treatment, the group 2 was treated by an ADM that was prepared from cosmetic surgery skin samples and the group 3 received hWJSCs labeled with SPIONs seeded onto an ADM. Tensile strength was evaluated before and after interventions, real time PCR assessed apoptosis, and Prussian blue staining, scanning electron microscopy (SEM) and MRI were used for the tracking of labeled cells. Results The hWJSCs exhibited mesenchymal stem cell properties. Population doubling time was 40.1 hours. SPIONs did not show any toxic effect. The hWJSCs seeded onto an ADM decreased Bax and increased Bcl-2 gene expression. Internalization of SPIONs within hWJSCs was confirmed by Prussian blue staining, SEM and MRI until day 21. There was a significant difference between the Young’s moduli of normal skin and the group receiving hWJSCs seeded onto an ADM. Histological observations and SEM imaging confirmed that MRI is an accurate method to track SPION-labeled hWJSCs in vivo. Conclusions This study showed that SPION labeling coupled with MRI can be used to further understand the fate of stem cells after transplantation in a burn model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".