Antibodies Targeted to the Brain with Image-Guided Focused Ultrasound Reduces Amyloid-β Plaque Load in the TgCRND8 Mouse Model of Alzheimer's Disease
Bibliographic record
Abstract
Immunotherapy for Alzheimer's disease (AD) relies on antibodies directed against toxic amyloid-beta peptide (Abeta), which circulate in the bloodstream and remove Abeta from the brain. In mouse models of AD, the administration of anti-Abeta antibodies directly into the brain, in comparison to the bloodstream, was shown to be more efficient at reducing Abeta plaque pathology. Therefore, delivering anti-Abeta antibodies to the brain of AD patients may also improve treatment efficiency. Transcranial focused ultrasound (FUS) is known to transiently-enhance the permeability of the blood-brain barrier (BBB), allowing intravenously administered therapeutics to enter the brain. Our goal was to establish that anti-Abeta antibodies delivered to the brain using magnetic resonance imaging-guided FUS (MRIgFUS) can reduce plaque pathology. To test this, TgCRND8 mice received intravenous injections of MRI and FUS contrast agents, as well as anti-Abeta antibody, BAM-10. MRIgFUS was then applied transcranially. Within minutes, the MRI contrast agent entered the brain, and BAM-10 was later found bound to Abeta plaques in targeted cortical areas. Four days post-treatment, Abeta pathology was significantly reduced in TgCRND8 mice. In conclusion, this is the first report to demonstrate that MRIgFUS delivery of anti-Abeta antibodies provides the combined advantages of using a low dose of antibody and rapidly reducing plaque pathology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".