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Record W2198755400 · doi:10.18632/oncoscience.235

Quantitative evaluation of cell death response in vitro and in vivo using conventional-frequency ultrasound

2015· article· en· W2198755400 on OpenAlexaff
Ali Sadeghi‐Naini, Stephanie Zhou, Mehrdad J. Gangeh, Zahra Jahedmotlagh, Omar Falou, Shawn Ranieri, Muhammad Azrif, Anoja Giles, Gregory J. Czarnota

Bibliographic record

VenueOncoscience · 2015
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsIn vivoUltrasoundApoptosisIn vitroMyeloid leukemiaPathologyMedicineNuclear medicineChemistryBiologyCancer researchRadiology

Abstract

fetched live from OpenAlex

Previous studies using high-frequency ultrasound have suggested that radiofrequency (RF) spectral analysis can be used to quantify changes in cell morphology to detect cell death response to therapy non-invasively. The study here investigated this at conventional-frequencies, frequently used in clinical settings. Spectral analysis was performed using ultrasound RF data collected with a clinical ultrasound platform. Acute myeloid leukemia (AML-5) cells were exposed to cisplatinum for 0-72 hours in vitro and prepared for ultrasound data collection. Preclinical in vivo experiments were also performed on AML-5 tumour-bearing mice receiving chemotherapy. The mid-band fit (MBF) spectral parameter demonstrated an increase of 4.4 ± 1.5 dBr for in vitro samples assessed 48 hours after treatment, a statistically significant change (p < 0.05) compared to control. Further, in vitro concentration-based analysis of a mixture of apoptotic and untreated cells indicated a mean change of 10.9 ± 2.4 dBr in MBF between 0% and 40% apoptotic cell mixtures. Similar effects were reproduced in vivo with an increase of 4.6 ± 0.3 dBr in MBF compared to control, for tumours with considerable apoptotic areas within histological samples. The alterations in the size of cells and nuclei corresponded well with changes measured in the quantitative ultrasound (QUS) parameters.

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)0.001
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 score0.624
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.078
GPT teacher head0.325
Teacher spread0.247 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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