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Record W2310351445 · doi:10.1002/wnan.1398

Multimodal micro, nano, and size conversion ultrasound agents for imaging and therapy

2016· review· en· W2310351445 on OpenAlexafffund
Elizabeth Huynh, Maneesha A. Rajora, Gang Zheng

Bibliographic record

VenueWiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology · 2016
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchProstate Cancer Canada
KeywordsMicroscale chemistryDrug deliveryMultimodalityNanotechnologyUltrasound imagingPreclinical imagingComputer scienceMedicineIn vivoMedical physicsUltrasoundMaterials scienceRadiologyPsychologyBiology

Abstract

fetched live from OpenAlex

Ultrasound (US) is one of the most commonly used clinical imaging techniques. However, the use of US and US-based intravenous agents extends far beyond imaging. In particular, there has been a surge in the fabrication of multimodality US contrast agents and theranostic US agents for cancer imaging and therapy. The unique interaction of US waves with microscale and nanoscale agents has attracted much attention in the development of contrast agents and drug-delivery vehicles. The dimensions of the agent not only dictate how it behaves in vivo, but also how it interacts with US for imaging and drug delivery. Furthermore, these agents are also unique due to their ability to convert from the nanoscale to the microscale and vice versa, having imaging and therapeutic utility in both dimensions. Here, we review multimodality and multifunctional US-based agents, according to their size, and also highlight recent developments in size conversion US agents. WIREs Nanomed Nanobiotechnol 2016, 8:796-813. doi: 10.1002/wnan.1398 For further resources related to this article, please visit the WIREs website.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.302
Teacher spread0.282 · 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.

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

Citations29
Published2016
Admission routes2
Has abstractyes

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