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Record W2335443846 · doi:10.1517/14712598.2016.1169268

Prospect of ultrasound-mediated gene delivery in cardiovascular applications

2016· review· en· W2335443846 on OpenAlexafffund
Hao H Chen, Pratiek N. Matkar, Michael A. Kuliszewski, Howard Leong‐Poi

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2016
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersInstitute of Circulatory and Respiratory HealthCanadian Institutes of Health Research
KeywordsGene deliveryMedicineGenetic enhancementComputational biologyGeneBiologyBioinformaticsGenetics

Abstract

fetched live from OpenAlex

INTRODUCTION: The field of regenerative medicine has evolved over the years, investigating gene and stem/progenitor cell therapies to help address the increasing burden of cardiovascular disease (CVD). While the lack of success of gene therapy in clinical trials has dampened enthusiasm, the search continues for a successful and translatable gene therapy strategy for CVD. Ultrasound-mediated gene delivery (UMGD) is a non-invasive technique for gene delivery that utilizes gene-bearing carrier microbubbles and high power ultrasound to facilitate transfection in vivo. Many pre-clinical studies have shown benefit in animal models of CVD, but this has yet to be translated to human applications. AREAS COVERED: In this review, the basic principles of UMGD will be examined along with an overview of pre-clinical studies to date in CVD, focusing on cardiac and vascular applications and key findings. In addition, the potential path to the clinical translation of UMGD is discussed. EXPERT OPINION: Ultrasound-mediated gene delivery holds promise as a non-invasive technique for gene delivery in CVD, with the ability to deliver multiple genes with repeated deliveries over time. If the substantial hurdles to clinical translation can be overcome, UMGD may prove to be a key aspect in the success of cardiovascular gene therapy in the future.

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 score0.998
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.059
GPT teacher head0.305
Teacher spread0.245 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

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