Properties of a novel magnetized alginate for magnetic resonance imaging
Bibliographic record
Abstract
Implanting recombinant cells encapsulated in alginate microcapsules to secrete therapeutic proteins has been proven clinically effective in treating several murine models of human diseases. However, once implanted, these microcapsules cannot be assessed without invasive surgery. We now report the preparation and characterization of a novel ferrofluid to render these microcapsules visible with magnetic resonance imaging (MRI). The ferrofluid was prepared as a colloidal iron oxide stabilized in water by alginate. The presence of iron particles in the ferrofluid was verified with chemical titration, dynamic light scattering, and magnetization measurement. The microcapsules fabricated with various concentrations of the ferrofluid in the core, or on the surface of alginate microcapsules, or both, all produced microcapsules with smooth surfaces as shown with light and scanning electron microscopy. However, at the nanoscale level, as revealed with atomic force microscopy, the ferrofluid-fabricated microcapsules demonstrated increased granularity, particularly when the ferrofluid was used to laminate the surface. From the force spectroscopy measurements, these modified microcapsules showed increasing surface rigidity in the following order: traditional alginate < ferrofluid in the core < ferrofluid on the surface. Although the mechanical stability of low-concentration ferrofluid (0.1%) microcapsules was reduced, increasing concentrations, up to 20%, were able to improve stability. When these ferrofluid microcapsules were examined with MRI, their T(2) relaxation time was reduced, thereby producing increased contrast readily detectable with MRI, whereas the traditional alginate microcapsules showed no difference when compared with water. In conclusion, such ferrofluid-enhanced alginate is suitable for fabricating microcapsules that offer the potential for in vivo tracking of implanted microcapsules without invasive surgery.
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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 | 0.001 |
| 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) | 0.001 | 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".