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Record W3182441860 · doi:10.1002/jbio.202100099

In vivo spectroscopic photoacoustic imaging and laser‐induced nanoparticle vaporization for <scp>anti‐HER2</scp> breast cancer

2021· article· en· W3182441860 on OpenAlexafffund
Yanjie Wang, Muhannad N. Fadhel, Eno Hysi, Maurice Pastenak, Krishnan Sathiyamoorthy, Michael C. Kolios

Bibliographic record

VenueJournal of Biophotonics · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsSunnybrook HospitalSunnybrook Health Science CentreToronto Metropolitan UniversitySt. Michael's Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Foundation for InnovationRyerson University
KeywordsIn vivoBreast cancerNanoparticlePhotoacoustic imaging in biomedicineMolecular imagingCancer researchChemistryCancerMaterials scienceMicrobubblesBiomedical engineeringNanomedicineNanotechnologyUltrasoundMedicineInternal medicineOpticsRadiology

Abstract

fetched live from OpenAlex

This study reports on the development and application of theragnostic agents targeting the HER2 receptors in breast tumors. The agent was constructed by loading silica-coated gold nanorods (GNRs) and a perfluorohexane liquid into PLGA-PEG nanoparticles, followed by surface conjugation with antibody Herceptin. The particle uptake in human breast cancer MDA-MB-231 (HER2-negative) and BT474 (HER2-positive) cell lines was tested. A proof of principle in vivo study was also performed using a xenograft mouse bilateral tumor model (16 mice, 32 tumors). Photoacoustic imaging was performed using a VevoLAZR device at 720/750/850 nm illuminations and 21 MHz central frequency. The relative concentrations of GNRs in the tumor were quantified using a linear spectral unmixing technique. The therapeutic efficacy of these nanoparticles was evaluated through optical droplet vaporization, and cell damage was confirmed using tissue immunofluorescence and histology. Our results demonstrate the potential of PLGA-GNRs as theragnostic agents for anti-HER2 breast cancer therapy.

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 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 score0.229
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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 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

Citations13
Published2021
Admission routes2
Has abstractyes

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