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Record W1996142593 · doi:10.3109/02656736.2012.666709

Ultrasound enhanced drug delivery to the brain and central nervous system

2012· review· en· W1996142593 on OpenAlexaff
Meaghan A. O’Reilly, Kullervo Hynynen

Bibliographic record

VenueInternational Journal of Hyperthermia · 2012
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Biomedical Imaging and Bioengineering
KeywordsDrug deliveryMedicineMicrobubblesCentral nervous systemBlood–brain barrierDrug delivery to the brainUltrasoundDrugFocused ultrasoundNeurosciencePharmacologyBiologyInternal medicineChemistryRadiology

Abstract

fetched live from OpenAlex

There is an increasing interest in the use of ultrasound to enhance drug delivery to the brain and central nervous system. Disorders of the brain and CNS historically have had poor response to drug therapy due to the presence of the blood-brain barrier (BBB). Techniques for circumventing the BBB are typically highly invasive or involve disrupting large portions of the BBB, exposing the brain to pathogens. Ultrasound can be non-invasively delivered to the brain through the intact skull. When combined with preformed microbubbles, ultrasound can safely induce transient, localised and reversible disruption of the BBB, allowing therapeutics to be delivered. Investigations to date have shown positive response to ultrasound BBB disruption combined with therapeutic agent delivery in rodent models of primary and metastatic brain cancer and Alzheimer's disease. Recent work in non-human primates has demonstrated that the technique is feasible for use in humans. This review examines the current status of drug delivery to the brain and CNS both by disruption of the BBB, and by ultrasound enhancement of drug delivery through the already compromised BBB. Cellular and physical mechanisms of disruption are discussed, as well as treatment technique, safety and monitoring.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.254
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations80
Published2012
Admission routes1
Has abstractyes

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