Targeted Delivery of Neural Stem Cells to the Brain Using MRI-Guided Focused Ultrasound to Disrupt the Blood-Brain Barrier
Bibliographic record
Abstract
Stem cell therapy is a promising strategy to treat neurodegenerative diseases, traumatic brain injury, and stroke. For stem cells to progress towards clinical use, the risks associated with invasive intracranial surgery used to deliver the cells to the brain, needs to be reduced. Here, we show that MRI-guided focused ultrasound (MRIgFUS) is a novel method for non-invasive delivery of stem cells from the blood to the brain by opening the blood brain barrier (BBB) in specific brain regions. We used MRI guidance to target the ultrasound beam thereby delivering the iron-labeled, green fluorescent protein (GFP)-expressing neural stem cells specifically to the striatum and the hippocampus of the rat brain. Detection of cellular iron using MRI established that the cells crossed the BBB to enter the brain. After sacrifice, 24 hours later, immunohistochemical analysis confirmed the presence of GFP-positive cells in the targeted brain regions. We determined that the neural stem cells expressed common stem cell markers (nestin and polysialic acid) suggesting they survived after transplantation with MRIgFUS. Furthermore, delivered stem cells expressed doublecortin in vivo indicating the stem cells were capable of differentiating into neurons. Together, we demonstrate that transient opening of the BBB with MRIgFUS is sufficient for transplantation of stem cells from the blood to targeted brain structures. These results suggest that MRIgFUS may be an effective alternative to invasive intracranial surgery for stem cell transplantation.
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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) | 0.001 | 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".