Magnetic Resonance Imaging of Alginate Beads Containing Pancreatic Beta Cells and Paramagnetic Nanoparticles
Bibliographic record
Abstract
Microencapsulation is being investigated as a means to avoid rejection of transplanted pancreatic islets. Monitoring bead distribution and stability in vivo is an important step toward improving microencapsulated islet transplantation strategies. Islet co-encapsulation with gadolinium-labeled mesoporous silica nanoparticles (Gd-MSNs) could allow bead visualization while immobilizing and limiting the potential internalization of the contrast agent. The porous nature of the MSNs could also be used to locally release anti-inflammatory, angiogenic, or anti-apoptotic factors. Mouse insulinoma 6 (MIN6) beta cells were co-encapsulated with Gd-MSNs in alginate beads produced by emulsification and internal gelation. Gd-MSN alginate beads appeared brighter in T 1 -weighted imaging sequences (detection threshold of 0.016 mM Gd; relaxometric ratio r 2 / r 1 = 1.45) than beads without Gd-MSNs. No leaching of Gd 3+ from the hydrogels was detected over the course of 3 months. MIN6 cells co-encapsulated with Gd-MSNs were viable without significant differences in cell growth rate compared to encapsulated controls without Gd-MSNs. This study paves the way for microencapsulated islet tracking via MRI using co-encapsulated paramagnetic nanomaterials.
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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".