MRI-Tracking of Dental Pulp Stem Cells In Vitro and In Vivo Using Dextran-Coated Superparamagnetic Iron Oxide Nanoparticles
Bibliographic record
Abstract
The aim of this study was to track dental pulp stem cells (DPSCs) labeled with dextran-coated superparamagnetic iron oxide nanoparticles (SPIONs) using magnetic resonance imaging (MRI). Dental pulp was isolated from male Sprague Dawley rats and cultured in Dulbecco's modified Eagle's medium F12 (DMEM-F12) and 10% fetal bovine serum. Effects of SPIONs on morphology, viability, apoptosis, stemness, and osteogenic and adipogenic differentiation of DPSCs were assessed. Prussian blue staining and MRI were conducted to determine in vitro efficiency of SPIONs uptake by the cells. Both non-labeled and labeled DPSCs were adherent to culture plates and showed spindle-shape morphologies, respectively. They were positive for osteogenic and adipogenic induction and expression of cluster of differentiation (CD) 73 and CD90 biomarkers, but negative for expression of CD34 and CD45 biomarkers. The SPIONs were non-toxic and did not induce apoptosis in doses less than 25 mg/mL. Internalization of the SPIONs within the DPSCs was confirmed by Prussian blue staining and MRI. Our findings revealed that the MRI-based method could successfully monitor DPSCs labeled with dextran-coated SPIONs without any significant effect on osteogenic and adipogenic differentiation, viability, and stemness of DPSCs. We provided the in vitro evidence supporting the feasibility of an MRI-based method to monitor DPSCs labeled with SPIONs without any significant reduction in viability, proliferation, and differentiation properties of labeled cells, showing that internalization of SPIONs within DPSCs were not toxic at doses less than 25 mg/mL. In general, the SPION labeling does not seem to impair cell survival or differentiation. SPIONs are biocompatible, easily available, and cost effective, opening a new avenue in stem cell labeling in regenerative medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| 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 | 0.001 |
| 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".