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Record W2015517688 · doi:10.1158/1538-7445.am2014-2060

Abstract 2060: Quantifying vascular biomarkers with contrast-enhanced molecular ultrasound imaging

2014· article· en· W2015517688 on OpenAlexaff
Janet M. Denbeigh, Brian A. Nixon, John J. Y. Lee, Mirjana Jerkić, Philip A. Marsden, Michelle Letarte, Mira C. Puri, F. Stuart Foster

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHospital for Sick ChildrenSt. Michael's HospitalUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsMicrobubblesEndoglinAngiogenesisMolecular imagingIn vivoPathologyPreclinical imagingUltrasoundMedicineCancer researchCell biologyBiologyChemistryStem cellGenetics

Abstract

fetched live from OpenAlex

Abstract Introduction: Molecular imaging has the potential to dramatically impact all facets of patient care, from early disease detection to treatment monitoring and follow-up, as a tool for the characterization and measurement of key biomolecules in vivo. In ultrasound, functional and molecular imaging is possible through the use of microbubbles (MB), a contrast agent that can be transformed into targeting agents that bind to vascular biomarkers of interest. In this study, we evaluate whether targeted ultrasound contrast enhanced imaging can provide a quantitative measure of surface receptor expression in endothelial cell populations. Methods: The biomarker endoglin (Eng) was selected for targeting due to its involvement in the processes of development, vascular regulation and disease, including tumor angiogenesis. Endoglin wildtype (Eng+/+), heterozygous null (Eng+/-) and null (Eng-/-) mouse embryonic endothelial cells were cultured on glass slides and mounted in parallel plate flow chambers. MicroMarker microbubbles (endoglin targeted: MBE, isotype control: MBC or untargeted: MBU at 1x107 MB/mL in PBS) were perfused across the cells at 4 mL/min, corresponding to a shear stress of 2 dynes/cm2. Cell and bubble numbers were determined from bright field and phase images (Nikon, 40x), with adhesion quantified as the number of MB/cell. Binding of microbubbles was also assessed in late-gestational stage, isolated, living embryos (Eng+/+, Eng+/-). The highly regulated and controlled activities of normal angiogenesis and vasculogenesis in the mouse embryo make it an excellent surrogate for complex and heterogeneous tumor microenvironments, while genetic manipulation enables the generation of a variety of useful transgenic models. Nonlinear contrast-specific ultrasound imaging, performed at 21MHz with a Vevo-2100 scanner (VisualSonics Inc.), was used to collect contrast mean power ratios (CMPR, representative measure of MB binding) within the brains of each embryo 4 minutes after a bolus injection of MBE, MBC or MBU. Results: Expression levels in cells and embryos were significantly different across genotypes, with endoglin reduced by half in Eng+/- and totally absent in Eng-/- samples. In vitro, microbubble adhesion was found to vary significantly (p<0.05) across genotype populations, with minimal attachment of MBC and MBU compared to MBE. Endoglin-targeted binding was approximately two-fold higher (median = 0.96 MBE/cell) in Eng+/+ compared to Eng+/- (median = 0.42 MBE/cell) cells. In embryo studies, we observed minimal signal from MBC and MBU, while MBE binding was found to be significantly higher in Eng+/+ embryos (CMPR+/+ = 9.71 + 0.66, 95% CI) compared to Eng+/- embryos (CMPR+/- = 5.51 + 0.64, 95% CI). In conclusion, these results suggest that molecular ultrasound is capable of reliably differentiating between molecular genotypes and relating receptor densities to quantifiable molecular ultrasound levels. Citation Format: Janet M. Denbeigh, Brian A. Nixon, John J.Y. Lee, Mirjana Jerkic, Philip A. Marsden, Michelle Letarte, Mira C. Puri, F. Stuart Foster. Quantifying vascular biomarkers with contrast-enhanced molecular ultrasound imaging. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2060. doi:10.1158/1538-7445.AM2014-2060

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.320
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2014
Admission routes1
Has abstractyes

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