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Record W4297998510 · doi:10.32920/ryerson.14636985.v2

An Investigation of Backscatter Power Spectra from Cells, Cell Pellets and Microspheres

2022· preprint· en· W4297998510 on OpenAlexafffund
Michael C. Kolios, L. Taggart, Ralph E. Baddour, F. S. Foster, J. W. Hunt, G. J. Czarnota, Michael D. Sherar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity of TorontoToronto Metropolitan UniversityOntario Institute for Cancer Research
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchOntario Innovation Trust
KeywordsBackscatter (email)PelletsUltrasoundSpectral lineMaterials scienceTransducerMicrospherePolystyrenePower (physics)Biomedical engineeringOpticsAcousticsPhysicsComputer scienceMedicineTelecommunicationsComposite material

Abstract

fetched live from OpenAlex

It has been previously shown that high frequency ultrasound (20 - 100 MHz) can be used to detect cellular structure changes in tissues and cell ensembles. However, the changes seen in the backscattered ultrasound intensity and frequency spectrum are not fully understood. In this paper we attempt to better understand the nature of these changes by examination of the backscatter power spectra from cell ensembles (in pellet form) that have undergone two different types of cell death: by exposure to the chemotherapeutic cisplatin and by withdrawal of nutrients (decay). Three different ultrasound transducers were used, centered at 20MHz and 40MHz. In both death pathways, an increase of the midband fit of 10-12dB was measured, and there were significant changes in the spectral slopes. Furthermore, our initial analysis of the backscatter from single cells and polystyrene microspheres demonstrates the potential of the technique to assess scatterer size.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.193
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2022
Admission routes2
Has abstractyes

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Same topicUltrasound and Hyperthermia ApplicationsFrench-language works237,207