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Record W4393445057 · doi:10.1016/j.xpro.2024.102993

Protocol to detect and quantify tumor hypoxia in mice using photoacoustic imaging

2024· article· en· W4393445057 on OpenAlexaff
Tao Dai, Laurie J. Rich, Mukund Seshadri, Subhamoy Dasgupta

Bibliographic record

VenueSTAR Protocols · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsFujiFilm VisualSonics (Canada)
FundersNational Cancer InstituteNational Institutes of HealthBreast Cancer Coalition of RochesterSusan G. KomenRoswell Park Alliance Foundation, Roswell Park Cancer InstituteNational Institutes of Health, PakistanRoswell Park Cancer Institute
KeywordsPhotoacoustic imaging in biomedicineBreast tumorHypoxia (environmental)Protocol (science)Tumor hypoxiaComputer scienceModality (human–computer interaction)VisualizationUltrasound imagingUltrasoundBiomedical engineeringMedicineMedical physicsPathologyRadiologyArtificial intelligenceBreast cancerChemistryCancerInternal medicinePhysicsRadiation therapy

Abstract

fetched live from OpenAlex

Photoacoustic imaging (PAI) with co-registered ultrasound (US) is a hybrid non-invasive imaging modality that enables visualization and quantification of tumor hypoxia in live animals. Here, using a breast tumor xenograft model as an example, we present a stepwise protocol describing animal preparation, positioning, instrument setup, and US-PAI image acquisition procedures. This protocol also guides through detailed data analysis, explains functional readouts obtained from PAI, and discusses the potential application of the technology to study the hypoxic tumor microenvironment. For complete details on the use and execution of this protocol, please refer to Dai et al.1

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0210.009

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 head0.022
GPT teacher head0.312
Teacher spread0.290 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations2
Published2024
Admission routes1
Has abstractyes

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