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Record W4229943617 · doi:10.32920/14637090

Detecting apoptosis using dynamic light scattering with optical coherence tomography

2021· preprint· en· W4229943617 on OpenAlexaff
Golnaz Farhat, Adrian Mariampillai, Victor X. D. Yang, Gregory J. Czarnota, Michael C. Kolios

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsPrincess Margaret Cancer CentreHealth Sciences CentreSunnybrook Health Science CentreToronto Metropolitan UniversityUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsOptical coherence tomographyIntracellularCoherence (philosophical gambling strategy)Frame rateApoptosisSpeckle patternDynamic light scatteringOpticsScatteringPhysicsChemistryBiologyCell biologyBiochemistry

Abstract

fetched live from OpenAlex

A dynamic light scattering technique is implemented using optical coherence tomography (OCT) to measure the change in intracellular motion as cells undergo apoptosis. Acute myeloid leukemia cells were treated with cisplatin and imaged at a frame rate of 166 Hz using a 1300 nm swept-source OCT system at various times over a period of 48 h. Time correlation analysis of the speckle intensities indicated a significant increase in intracellular motion 24 h after treatment. This rise in intracellular motion correlated with histological findings of irregularly shaped and fragmented cells indicative of cell membrane blebbing and fragmentation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.237
Teacher spread0.224 · 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.

Study designSimulation or modeling
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

Citations6
Published2021
Admission routes1
Has abstractyes

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