Focused Magnetic Stem Cell Targeting to the Retina Using Superparamagnetic Iron Oxide Nanoparticles
Bibliographic record
Abstract
Developing new ways of delivering cells to diseased tissue will be a key factor in translating cell therapeutics research into clinical use. Magnetically targeting cells enables delivery of significant numbers of cells to key areas of specific organs. To demonstrate feasibility in neurological tissue, we targeted cells magnetically to the upper hemisphere of the rodent retina. Rat mesenchymal stem cells (MSCs) were magnetized using superparamagnetic iron oxide nanoparticles (SPIONs). In vitro studies suggested that magnetization with fluidMAG-D was well tolerated, that cells remained viable, and they retained their differentiation capabilities. FluidMAG-D-labeled MSCs were injected intravitreally or via the tail vein of the S334ter-4 transgenic rat model of retinal degeneration with or without placing a gold-plated neodymium disc magnet within the orbit, but outside the eye. Retinal flatmount and cryosection imaging demonstrated that after intravitreal injection cells localized to the inner retina in a tightly confined area corresponding to the position of the orbital magnet. After intravenous injection, similar retinal localization was achieved and remarkably was associated with a tenfold increase in magnetic MSC delivery to the retina. Cryosections demonstrated that cells had migrated into both the inner and outer retina. Magnetic MSC treatment with orbital magnet also resulted in significantly higher retinal concentrations of anti-inflammatory molecules interleukin-10 and hepatocyte growth factor. This suggested that intravenous MSC therapy also resulted in significant therapeutic benefit in the dystrophic retina. With minimal risk of collateral damage, these results suggest that magnetic cell delivery is the best approach for controlled delivery of cells to the outer retina-the focus for disease in age-related macular degeneration and retinitis pigmentosa.
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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".