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Record W3006011311 · doi:10.1038/s41598-020-59338-z

Ultrasound localization microscopy of renal tumor xenografts in chicken embryo is correlated to hypoxia

2020· article· en· W3006011311 on OpenAlexfundno aff
Matthew R. Lowerison, Chengwu Huang, Fabrice Lucien, Shigao Chen, Pengfei Song

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteFonds de Recherche du Québec - SantéNational Institutes of Health
KeywordsChorioallantoic membranePerfusionUltrasoundIn vivoPathologyMedicineBiomarkerHypoxia (environmental)Biomedical engineeringNuclear medicineBiologyAngiogenesisRadiologyChemistryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Ultrasound localization microscopy (ULM) permits the reconstruction of super-resolved microvascular images at clinically relevant penetration depths, which can be potentially leveraged to provide non-invasive quantitative measures of tissue hemodynamics and hypoxic status. We demonstrate that ULM microbubble data processing methods, applied to images acquired with a Verasonics Vantage 256 system, can provide a non-invasive imaging surrogate biomarker of tissue oxygenation status. This technique was applied to evaluate the microvascular structure, vascular perfusion, and hypoxia of a renal cell carcinoma xenograft model grown in the chorioallantoic membrane of chicken embryos. Histological microvascular density was significantly correlated to ULM measures of intervessel distance (R = −0.92, CI 95 = [−0.99,−0.42], p = 0.01). The Distance Metric, a measure of vascular tortuosity, was found to be significantly correlated to hypoxyprobe quantifications (R = 0.86, CI 95 = [0.17, 0.99], p = 0.03). ULM, by providing non-invasive in vivo microvascular structural information, has the potential to be a crucial clinical imaging modality for the diagnosis and therapy monitoring of solid tumors.

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 score0.093
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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

Citations85
Published2020
Admission routes1
Has abstractyes

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