Delivery of stem cells to the brain using MRIgFUS
Bibliographic record
Abstract
Stem cell therapy is promising to treat neurodegenerative diseases, traumatic brain injury, and stroke. For stem cells to progress towards clinical use, the associated risks of current methods for delivery need to be reduced. Here, we introduce focused ultrasound (FUS) as a novel method for non-invasive delivery of neural stem cells to the brain. Magnetic resonance imaging (MRI) was used to target the striatum and the hippocampus on the left side for disruption of the blood-brain barrier (BBB). Definity microbubble contrast agent was injected intravenously at the onset of sonication. Following BBB disruption, green fluorescent protein (GFP)-expressing neural stem cells were injected into the carotid artery. MRI and standard post-mortem immunohistochemistry were used to detect the cells in vivo. Contrast-enhanced T1w images confirmed FUS increased BBB permeability in the targeted regions. Immunohistochemistry revealed that the cells crossed the BBB and that they were localized to the left striatum and left hippocampus. Twenty four hours after sonication, GFP-positive cells exhibited a neuronal phenotype and expressed nestin, polysialic acid and doublecortin. Together these results demonstrate that MRI-guided FUS (MRIgFUS) is an effective tool for delivery of cells to the brain. This technique may be the key to reducing the risks of cell transplantation and leading to improvements in stem cell therapy in the clinical setting.
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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".