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Record W4402859315 · doi:10.3390/photonics11090891

Characterizing Normal and Tumour Blood Microcirculatory Systems Using Optical Coherence Tomography

2024· article· en· W4402859315 on OpenAlexafffund
Héctor A. Contreras-Sánchez, W. Jeffrey Zabel, Costel Flueraru, Lothar Lilge, Edward Taylor, I. Alex Vitkin

Bibliographic record

VenuePhotonics · 2024
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkNational Research Council CanadaUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsOptical coherence tomographyMicrocirculationBlood flowPathologySpeckle patternAngiogenesisBiomedical engineeringMedicineBlood vesselCancer researchRadiologyComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Blood vessels transport blood and deliver essential molecules to cells. In cancer, the tumour vasculature evolves very differently from healthy tissue, not only in morphology but also in functionality. Comprehensive 3D in vivo imaging of the tumour microcirculatory system thus might lead to a clearer understanding of the vascular role in tumour growth and response to cancer therapy. Here, we employed two optical coherence tomography (OCT) extensions, speckle-variance and Doppler, to study the microvascular architecture and blood flow in normal murine and xenograft tumour tissue. We explored the biophysics of blood flow in microcirculation and extracted relevant quantification features such as vessel diameters and velocity histograms, and combined diameter–velocity plots. It was demonstrated that tumour microcirculation evolves in time and differs from healthy tissue, impacting tumour growth kinetics. This proof-of-principle study demonstrates the potential of OCT to provide an understanding of the temporal evolution of the tumour vasculature and its relationship with tumour development.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.013
GPT teacher head0.226
Teacher spread0.212 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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