Time course of focused ultrasound effects on β-amyloid plaque pathology in the TgCRND8 mouse model of Alzheimer’s disease
Bibliographic record
Abstract
Previous studies have demonstrated that temporarily increasing the permeability of the blood-brain barrier using focused ultrasound can reduce β-amyloid plaque load and improve cognitive function in animal models of Alzheimer's disease. However, the underlying mechanism and duration for which the effects of one treatment persists for are unknown. Here, we used in vivo two-photon fluorescence microscopy to track changes in β-amyloid plaque sizes in the TgCRND8 mouse model of Alzheimer's disease after one focused ultrasound treatment. We found that one treatment reduced plaques to 62 ± 16% (p ≤ 0.001) of their original volume two days post-sonication; this decrease in size persisted for two weeks. We then sought to evaluate the effectiveness of biweekly focused ultrasound treatments using magnetic resonance imaging-guided focused ultrasound treatments. Three to five biweekly treatments resulted in a 27 ± 7% (p ≤ 0.01) decrease in plaque number and 40 ± 10% (p ≤ 0.01) decrease in plaque surface area compared to untreated littermates. This study demonstrates that one focused ultrasound treatment reduces the size of existing β-amyloid plaques for two weeks, and that repeated biweekly focused ultrasound treatments is an effective method of reducing β-amyloid pathology in moderate-to-late stages of Alzheimer's disease.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".