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Ultrasound Imaging of Apoptosis: Spectroscopic Detection of DNA-Damage Effects at High and Low Frequencies

2010· article· en· W108200661 on OpenAlexafffund
Roxana Vlad, Michael C. Kolios, Gregory J. Czarnota

Bibliographic record

VenueMethods in molecular biology · 2010
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreToronto Metropolitan UniversityPrincess Margaret Cancer CentreUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsUltrasoundProgrammed cell deathApoptosisIn vivoEx vivoCellMedicinePathologyBiomedical engineeringRadiologyBiology

Abstract

fetched live from OpenAlex

A new noninvasive method for the detection of DNA damage using mid-to high-frequency ultrasound (10-60 MHz) has been developed. Ultrasound imaging and quantitative analysis methods are used to detect cell death occurring in response to anticancer therapies in cell samples in vitro, in rat brain tissue ex vivo, and in cancer mouse models in vivo. Experimental evidence indicates that the mechanism behind this ultrasonic detection is linked to changes in the size and acoustic properties of the cell nucleus occurring with forms of cell death, and in particular apoptosis. Nuclear changes associated with cell death can result in up to 16-fold increase in ultrasound backscatter intensity and changes in spectral slope that are consistent with theoretical predictions. Furthermore, color-coded images can be generated based on specific ultrasound parameters in order to identify the regions of cell death in tumor ultrasound images with treatments. These results provide a foundation for future investigations regarding the use of ultrasound in preclinical and clinical settings to noninvasively monitor tumor responses to specific interventions that induce cell death.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.274
Teacher spread0.269 · 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

Citations27
Published2010
Admission routes2
Has abstractyes

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