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Record W2123818500 · doi:10.7863/jum.2007.26.6.731

Insonation of the Eye in the Presence of Microbubbles

2007· article· en· W2123818500 on OpenAlexaff
Tomihiko Hirokawa, Raffi Karshafian, Charles J. Pavlin, Peter N. Burns

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2007
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of TorontoMount Sinai HospitalSunnybrook Health Science CentreHealth Sciences Centre
Fundersnot available
KeywordsMedicineMicrobubblesFluoresceinUltrasoundMechanical indexVascular permeabilityFundus photographyFundus (uterus)Fluorescein angiographyRadiologyNuclear medicineBiomedical engineeringOphthalmologyPathologyFluorescence

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to investigate the presence and duration of vascular permeability changes induced by the combination of ultrasound and an intravascular microbubble contrast agent in the rabbit eye. METHODS: Five eyes were studied in 8 anaesthetized rabbits. Insonation was performed with a diagnostic B-mode system (center frequency = 2 MHz; mechanical index [MI] = 0.2 and 1.7) for 5 minutes after administration of perflutren microbubbles (0.07 mL/kg). Fluorescein fundus angiography was performed before and 3 minutes after insonation; at 6 minutes, color fundus photography was used to assess the dye leakage, bleeding, and alteration of the diameter of fundus vessels. RESULTS: Alteration of fundus vessel diameters was observed in 1 of 5 cases at a low MI and in 4 of 5 cases at a higher MI. In 1 case, leakage of fluorescein indicated increased permeability at the higher MI. No bleeding was detected in any case. CONCLUSIONS: The permeability change induced by insonation and this dose of an ultrasound contrast agent appears to be transient under the conditions studied, although the time delay between insonation and optical assessment limits the completeness of the findings. This preliminary study may be relevant to drug delivery strategies using ultrasound and microbubbles.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.128

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.010
GPT teacher head0.255
Teacher spread0.244 · 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 designObservational
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

Citations15
Published2007
Admission routes1
Has abstractyes

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