Magnetic Resonance Imaging of Mouse Islet Grafts Labeled with Novel Chitosan-Coated Superparamagnetic Iron Oxide Nanoparticles
Bibliographic record
Abstract
OBJECT: To better understand the fate of islet isografts and allografts, we utilized a magnetic resonance (MR) imaging technique to monitor mouse islets labeled with a novel MR contrast agent, chitosan-coated superparamagnetic iron oxide (CSPIO) nanoparticles. MATERIALS AND METHODS: After being incubated with and without CSPIO (10 µg/ml), C57BL/6 mouse islets were examined under transmission electron microscope (TEM) and their insulin secretion was measured. Cytotoxicity was examined in α (αTC1) and β (NIT-1 and βTC) cell lines as well as islets. C57BL/6 mice were used as donors and inbred C57BL/6 and Balb/c mice were used as recipients of islet transplantation. Three hundred islets were transplanted under the left kidney capsule of each mouse and then MR was performed in the recipients periodically. At the end of study, the islet graft was removed for histology and TEM studies. RESULTS: After incubation of mouse islets with CSPIO (10 µg/mL), TEM showed CSPIO in endocytotic vesicles of α- and β-cells at 8 h. Incubation with CSPIO did not affect insulin secretion from islets and death rates of αTC1, NIT-1 and βTC cell lines as well as islets. After syngeneic and allogeneic transplantation, grafts of CSPIO-labeled islets were visualized on MR scans as persistent hypointense areas. At 8 weeks after syngeneic transplantation and 31 days after allogeneic transplantation, histology of CSPIO-labeled islet grafts showed colocalized insulin and iron staining in the same areas but the size of allografts decreased with time. TEM with elementary iron mapping demonstrated CSPIO distributed in the cytoplasm of islet cells, which maintained intact ultrastructure. CONCLUSION: Our results indicate that after syngeneic and allogeneic transplantation, islets labeled with CSPIO nanoparticles can be effectively and safely imaged by MR.
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 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".