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Record W2810171012 · doi:10.7567/jjap.57.07lf26

Reaction assessment of cultured breast cancer cells exposed to anticancer agents using microscale acoustic impedance profile

2018· article· en· W2810171012 on OpenAlexaff
Rahma Hutami Rahayu, Kyoichi Takanashi, Thomas Tiong Kwong Soon, Inna Seviaryna, Roman Gr. Maev, Kazuto Kobayashi, Naohiro Hozumi, Sachiko Yoshida

Bibliographic record

VenueJapanese Journal of Applied Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Windsor
FundersJapan Society for the Promotion of Science
KeywordsMicroscale chemistryBreast cancerElectrical impedanceCancerCancer researchMedicineChemistryInternal medicinePhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract The mechanical properties of living cells are known to be associated with disease states and cell function. In this study, acoustic impedance microscopy using a sapphire lens transducer with a center frequency of 320 MHz was employed to characterize the elasticity of the C127I cell line against an anticancer drug, nimustine hydrochloride (ACNU), and an anticancer agent, betulinic acid (BA). Confocal laser scanning microscopy was also used to investigate the drug affecting actin filaments, the nucleus, and mitochondria structures. Breast cancer cells were found to have significantly lower acoustic impedance after treatment with ACNU and BA than intact cells. Confocal images showed a significant difference in the localization of actin filaments and mitochondria structures, which suggested a difference in cell elasticity. An important insight emerging from this work is that the acoustic impedance of cells may potentially serve as a useful biomarker for anticancer drug efficacy tests, as diseases such as cancer have their own particular mechanical properties.

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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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