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Record W2059806537 · doi:10.1158/1538-7445.am2013-3920

Abstract 3920: In vivo tyrosinase reporter gene imaging with multispectral photoacoustic technology.

2013· article· en· W2059806537 on OpenAlexaffabout
Andrew Heinmiller, Minalini Lakshman, Dave Bates, Andrew Needles, Catherine Theodoropoulos, Robert J. Paproski, Roger J. Zemp

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of AlbertaFujiFilm VisualSonics (Canada)
Fundersnot available
KeywordsTyrosinaseMelaninIn vivoPhotoacoustic imaging in biomedicineReporter geneChemistryGene expressionBiomedical engineeringPathologyMaterials scienceGeneMedicineBiologyOpticsBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Abstract Photoacoustic imaging is a powerful tool for assessing tumor vasculature and oxygen saturation as well as detecting contrast agents for molecular imaging. Another exciting possibility is to visualize gene expression by using a reporter which produces a photoacoustic contrast agent. Tyrosinase is such a reporter in that its expression results in the production of melanin, which gives a strong photoacoustic signal. Here we describe the use of a commercially available photoacoustic (PA) imaging system (Vevo LAZR, VisualSonics, Toronto) to image inducible gene expression in subcutaneous xenograft tumors using tyrosinase as a reporter gene. The photoacoustic imaging system generated light from a tunable laser (680 - 970 nm) which was delivered through fiber optic bundles integrated into a linear array transducer (LZ-250, fc = 21 MHz), mounted to a linear stepper motor for 3D imaging. Animals (n=3) having MCF-7 xenograft tumors on either flank transfected with (+TYR) or without (-TYR) doxycycline-regulated tyrosinase were imaged before and one week after the induction of tyrosinase expression. 3D images of the tumors were acquired using multiple wavelengths (680, 750, 800, 850, 900 and 950nm) and 2D images were acquired across the entire wavelength range of the laser to generate absorption curves for blood and melanin. To confirm the presence of melanin and distinguish it from the endogenous blood signal, one animal was exsanguinated and the tumor was imaged again. Approximately 1mm-thick slices of the tumors were taken and photographed for visual confirmation of the presence of melanin. Comparison of pre- and post-doxycycline images of +TYR tumors clearly showed enhanced contrast in the tumor which closely matched the absorption spectrum of melanin. This signal persisted upon exsanguination. -TYR tumors showed signal which corresponded with the spectra of oxy and deoxy hemoglobin and changed little over the course of the experiment. Visual inspection of the excised sliced tumors revealed pigmentation in the +TYR tumors which was absent in the -TYR tumors. Here we have shown the ability of the Vevo LAZR photoacoustic imaging system to visualize inducible reporter gene expression in vivo. This has implications for assessing genetically controlled cellular processes and their response to anti-cancer drugs but also for monitoring gene therapy for cancer treatment non-invasively. Citation Format: Andrew Heinmiller, Minalini Lakshman, Dave Bates, Andrew Needles, Catherine Theodoropoulos, Robert J. Paproski, Roger J. Zemp. In vivo tyrosinase reporter gene imaging with multispectral photoacoustic technology. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3920. doi:10.1158/1538-7445.AM2013-3920

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 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)0.001
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.017
GPT teacher head0.296
Teacher spread0.279 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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