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Record W4312849592 · doi:10.1121/10.0015605

Volatile nanodroplets for neurological applications

2022· article· en· W4312849592 on OpenAlexaff
Harriet Lea‐Banks, Sheng‐Kai Wu, Ying Meng, Flavia Venetucci Gouveia, Clement Hamani, Kullervo Hynynen

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2022
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsMental Health Research CanadaHospital for Sick ChildrenSunnybrook HospitalUniversity of TorontoSickKids FoundationSunnybrook Health Science Centre
Fundersnot available
KeywordsNeuromodulationNeurosciencePentobarbitalFocused ultrasoundMedicineUltrasoundCentral nervous systemComputer sciencePsychologyAnesthesiaRadiology

Abstract

fetched live from OpenAlex

While current neuromodulation techniques are invasive, inaccurate, and/or unable to penetrate deeply, focused ultrasound (FUS) offers a way to study and treat the nervous system that is non-invasive, precise, and able to reach targets deep within the brain. Using ultrasound to trigger the delivery of pharmaceuticals from nanodroplets combines the spatial precision of FUS with the temporal control of psychoactive drugs. Here, I will present our work developing ultrasound-responsive nanodroplets to deliver anesthetics in the brain for neuromodulation. This work has shown how nanodroplets, loaded with pentobarbital, can be vaporized with FUS and inhibit activity in specific brain regions. This platform is now being investigated for the treatment of several neurological and psychiatric disorders, including agitation and aggression in a model of Alzheimer’s disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.042
GPT teacher head0.321
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicVagus Nerve Stimulation ResearchFrench-language works237,207