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Record W2332183765 · doi:10.1515/plm-2014-0027

Imaging the electro-kinetic response of biological tissues with phase-resolved optical coherence tomography

2014· article· en· W2332183765 on OpenAlexaff
Valentin Demidov, Vladislav Toronov, Yuan Xu, Barry Vuong, Carry Sun, Victor X. D. Yang, I. Alex Vitkin

Bibliographic record

VenuePhotonics and Lasers in Medicine · 2014
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsSpeckle patternOptical coherence tomographyBrightnessElectric fieldCoherence (philosophical gambling strategy)OpticsPhase (matter)Materials sciencePhase coherencePhysics

Abstract

fetched live from OpenAlex

Abstract In this study, the electro-kinetic phenomena (EKP) induced in biological tissue by external electric field, while not directly visible in optical coherence tomography (OCT) images, were detected by analyzing their textural speckle features. During application of a low-frequency electric field to the tissue, speckle patterns changed their brightness and shape depending on the local tissue EKP. Since intensities of OCT image speckle patterns were analyzed and discussed in our previous publications, this work is mainly focused on OCT signal phase analysis. The algorithm for extracting local spatial phase variations from unwrapped phases is introduced. The detection of electrically induced optical changes manifest in OCT phase images shows promise for monitoring the fixed charge density changes within tissues through their electro-kinetic responses. This approach may help in the identification and characterization of morphology and function of healthy and pathologic tissues.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.259
Teacher spread0.248 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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