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Record W2969276399 · doi:10.1017/s1460396919000633

A review of ultrasound-mediated microbubbles technology for cancer therapy: a vehicle for chemotherapeutic drug delivery

2019· review· en· W2969276399 on OpenAlexaff
Ernest Osei, Aladdin Al-Asady

Bibliographic record

VenueJournal of Radiotherapy in Practice · 2019
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of GuelphGrand River HospitalUniversity of Waterloo
Fundersnot available
KeywordsMicrobubblesDrug deliveryUltrasoundMedicineDrugGene deliveryBioavailabilityChemotherapeutic drugsPharmacologyBiomedical engineeringCancer researchChemotherapyGenetic enhancementNanotechnologyMaterials scienceChemistryRadiologyGeneSurgery

Abstract

fetched live from OpenAlex

Abstract Background: The unique behaviour of microbubbles under ultrasound acoustic pressure makes them useful agents for drug and gene delivery. Several studies have demonstrated the potential application of microbubbles as a non-invasive, safe and effective technique for targeted delivery of drugs and genes. The drugs can be incorporated into the microbubbles in several different approaches and then carried to the site of interest where it can be released by destruction of the microbubbles using ultrasound to achieve the required therapeutic effect. Methods: The objective of this article is to report on a review of the recent advances of ultrasound-mediated microbubbles as a vehicle for delivering drugs and genes and its potential application for the treatment of cancer. Conclusion: Ultrasound-mediated microbubble technology has the potential to significantly improve chemotherapy drug delivery to treatment sites with minimal side effects. Moreover, the technology can induce temporary and reversible changes in the permeability of cells and vessels, thereby allowing for drug delivery in a spatially localised region which can improve the efficiency of drugs with poor bioavailability due to their poor absorption, rapid metabolism and rapid systemic elimination.

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)0.001
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 score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.340
Teacher spread0.312 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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