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Record W2158209854 · doi:10.1177/0192623314525687

Hemodynamic Correlates of Drug-induced Vascular Injury in the Rat Using High-frequency Ultrasound Imaging

2014· article· en· W2158209854 on OpenAlexaff
Terri A. Swanson, Teri Conte, Ben Deeley, Susan Portugal, John M. Kreeger, Leslie Obert, E. Clive Joseph, Todd Wisialowski, Sharon Sokolowski, Catherine Rief, Paul J. Nugent, Michael P. Lawton, Bradley E. Enerson

Bibliographic record

VenueToxicologic Pathology · 2014
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsFujiFilm VisualSonics (Canada)
Fundersnot available
KeywordsFenoldopamTunica mediaMedicineUltrasoundBlood flowPathologyTunica intimaHyperaemiaHemodynamicsAdventitiaNecrosisInternal medicineRadiology

Abstract

fetched live from OpenAlex

Several classes of drugs have been shown to cause drug-induced vascular injury (DIVI) in preclinical toxicity studies. Measurement of blood flow and vessel diameter in numerous vessels and across various tissues by ultrasound imaging has the potential to be a noninvasive translatable biomarker of DIVI. Our objective was to demonstrate the utility of high-frequency ultrasound imaging for measuring changes in vascular function by evaluating blood flow and vessel diameter in the superior mesenteric arteries (SMA) of rats treated with compounds that are known to cause DIVI and are known vasodilators in rat: fenoldopam, CI-1044, and SK&F 95654. Blood flow, vessel diameter, and other parameters were measured in the SMA at 4, 8, and 24 hr after dosing. Mild to moderate perivascular accumulations of mononuclear cells, neutrophils in tunica adventitia, and superficial tunica media as well as multifocal hemorrhage and necrosis in the tunica media were found in animals 24 hr after treatment with fenoldopam and SK&F 95654. Each compound caused marked increases in blood flow and shear stress as early as 4 hr after dosing. These results suggest that ultrasound imaging may constitute a functional correlate for the microscopic finding of DIVI in the rat.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.016
GPT teacher head0.288
Teacher spread0.271 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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