MRI-guided focused ultrasound for treating Parkinson’s disease with human mesenchymal stem cells
Bibliographic record
Abstract
Parkinson's disease (PD) is a progressive disorder that affects the nervous system and causes regions of the brain to deteriorate. In this study, we investigated the effects of MR-guided focused ultrasound (MRgFUS) for the delivery of human mesenchymal stem cells (MSCs) on the 6-hydroxydopamine (6-HODA)-induced PD rat model. MRgFUS-induced blood-brain barrier (BBB) permeability modulation was conducted using an acoustic controller with the targets at the striatum (ST) and SN. Human MSCs were injected immediately before sonication. Here, we show that we can deliver human MSCs into Parkinsonian rats through MRgFUS-induced BBB modulation using an acoustic controller. Stem cells were identified in the sonicated brain regions using surface markers, indicating the feasibility of MSC delivery via MRgFUS. MSCs + FUS treatment significantly improved the behavioural outcomes compared with control, FUS alone, and MSCs alone groups (p < 0.05). In the quantification analysis of the TH stain, a significant reservation of dopamine neurons was seen in the MSCs + FUS group as compared with the MSCs group (ST: p = 0.03; SN: p = 0.0005). Mesenchymal stem cell therapy may be a viable treatment option for neurodegenerative diseases such as Parkinson's. Transcranial MRgFUS serves as an efficacious and safe method for targeted and minimally invasive stem cell homing.
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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".