Anesthetic‐loaded nanodroplets with focused ultrasound reduces agitation in Alzheimer's mice
Bibliographic record
Abstract
OBJECTIVE: Alzheimer's disease (AD) is often associated with neuropsychiatric symptoms, including agitation and aggressive behavior. These symptoms increase with disease severity, ranging from 10% in mild cognitive impairment to 50% in patients with moderate-to-severe AD, pose a great risk for self-injury and injury to caregivers, result in high rates of institutionalization and great suffering for patients and families. Current pharmacological therapies have limited efficacy and a high potential for severe side effects. Thus, there is a growing need to develop novel therapeutics tailored to safely and effectively reduce agitation and aggressive behavior in AD. Here, we investigate for the first time the use of focused ultrasound combined with anesthetic-loaded nanodroplets (nanoFUS) targeting the amygdala (key structure in the neurocircuitry of agitation) as a novel minimally invasive tool to modulate local neural activity and reduce agitation and aggressive behavior in the TgCRND8 AD transgenic mice. METHODS: Male and female animals were tested in the resident-intruder (i.e., aggressive behavior) and open-field tests (i.e., motor agitation) for baseline measures, followed by treatment with active- or sham-nanoFUS. Behavioral testing was then repeated after treatment. RESULTS: Active-nanoFUS neuromodulation reduced aggressive behavior and agitation in male mice, as compared to sham-treated controls. Treatment with active-nanoFUS increased the time male mice spent in social-non-aggressive behaviors. INTERPRETATION: Our results show that neuromodulation with active-nanoFUS may be a potential therapeutic tool for the treatment of neuropsychiatric symptoms, with special focus on agitation and aggressive behaviors. Further studies are necessary to establish cellular, molecular and long-term behavioral changes following treatment with nanoFUS.
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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) | 0.000 | 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 | 0.001 | 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".