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Record W3008518894 · doi:10.1117/12.2545364

Assessment of cell death using dynamic light scattering with M-mode optical coherence tomography imaging

2020· article· en· W3008518894 on OpenAlexaff
Sehar Rija, Michael C. Kolios

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOptical coherence tomographyDynamic light scatteringLight scatteringOpticsBrownian motionDecorrelationScatteringMaterials scienceCoherence (philosophical gambling strategy)PhysicsNanotechnologyNanoparticleComputer scienceComputer vision

Abstract

fetched live from OpenAlex

Optical Coherence tomography (OCT) is a rapidly developing light based imaging modality that can provide functional information and visualization of tissue morphology and cell death by quantifying the motion of intracellular structures. In this work, Dynamic light scattering (DLS) with OCT, is used to compute the time-dependent fluctuations in scattered light intensity. Using the DLS-OCT method, we have previously shown a higher rate of intracellular motion detected in apoptotic cells compared to viable cells. In this study, we aim to probe the intracellular motion of cells at much higher scan-rates that we have attempted previously to detect sub-cellular motion with greater sensitivity. To validate the DLS-OCT approach, M-mode OCT images were acquired using Thorlabs SS-OCT system (sampling rate of 100 kHz) to measure the Brownian motion in monodisperse microsphere suspensions. The results demonstrate that ACF decays more rapidly for smaller size microspheres and the experimental decorrelation time values matched with the theoretical values calculated using the Einstein-Stokes equation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.790

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 head0.011
GPT teacher head0.258
Teacher spread0.246 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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