P.001 Application of low-intensity transcranial focused unltrasound to the hippocampus in Alzheimer’s Disease
Bibliographic record
Abstract
Background: The purpose of this study was to evaluate the safety and efficacy of low-intensity tFUS under the threshold for BBB disruption in patients with AD. In addition, we assessed changes in the regional cerebral metabolic rate of glucose (rCMRglu) using F-18 fluoro-2-deoxyglucose positron emission tomography (FDG-PET) and cognitive function after tFUS. Methods: Eight AD patients were recruited. We applied low-intensity tFUS to the right hippocampus for 3 minutes using an image-guided tFUS system. For multi-modal neuroimaging guidance, MRI and CT data were spatially co-registered using the maximization of normalized mutual information. The subjectspecific coordinates of the hippocampus in the right hemisphere were identified as the tFUS target location. Results: Radiological evidence of contrast enhancement associated with BBB opening was not found in neither the visual inspection nor the ICA of the DCE-MRI data. No adverse events were observed during the hospitalization and follow-up outpatient visits for 5 to 24 months. The immediate recall and recognition memory on the SVLT were significantly improved after the sonication. The PET analysis showed the increased level of rCMRglu in the right hippocampus. Conclusions: Application of low-intensity tFUS to the hippocampus with MB did not open blood brain barrier but increased hippocampal glucose metabolism and memory function.
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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 | 0.001 |
| 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) | 0.003 | 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".