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Record W3040374018 · doi:10.1017/s1431927600037569

Ultrasound Biomicroscopy of Cancer Therapy Effects: Correlation between Light and Electron Microscopy, and a New Non-Invasive Ultrasound Imaging Method for Detecting Apoptosis

2000· article· en· W3040374018 on OpenAlexaff
Gregory J. Czarnota, Michael C. Kolios, Y. M. Heng, Kiran Devaraj, Chun‐Wai Tam, Laura Ling Ying Tan, F.P. Ottensmeyer, J.W. Hunt, M.D. Sherar

Bibliographic record

VenueMicroscopy and Microanalysis · 2000
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsApoptosisUltrasoundSonodynamic therapyUltrasound biomicroscopyPhotodynamic therapyIn vivoMedicineCancerProgrammed cell deathPathologyCancer cellCancer researchBiomedical engineeringChemistryBiologyRadiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract We have discovered that high-frequency ultrasound imaging, or ultrasound biomicroscopy, can be used to detect apoptosis in a number of experimental systems. We have shown that such detection with 30-40 MHz ultrasound is possible using cells in an in vitro system (AML-3 leukemia cells) made to undergo apoptosis in response to treatment with a variety of cancer killing chemotherapeutic drugs. We have shown that ultrasound biomicroscopy can also detect programmed cell death in tissues made to undergo apoptosis in response to photodynamic therapy, currently an experimental cancer treating regimen. Lastly, we have shown that this ultrasound imaging approach works in vivo, using living animals where apoptosis has been induced similarly using photodynamic therapy. Specifically, apoptotic cells and regions of apoptosis in tissues exhibit up to a 36-fold increase in ultrasound backscatter intensity permitting this type of cell death to be readily discriminated from surrounding viable tissue.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.278
Teacher spread0.272 · 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 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

Citations2
Published2000
Admission routes1
Has abstractyes

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