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Effects of Acoustic Parameters of Low-Intensity Ultrasound on Breast Cancer Cells and Applications in Neoadjuvant Chemotherapy

2024· article· en· W4405522051 on OpenAlexaff
Yan Chen, Kepeng Zhu, Wenzhi Chen, Alfred C. H. Yu, Xinxing Duan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
FundersNatural Science Foundation of Chongqing
KeywordsBreast cancerChemotherapyUltrasoundOncologyMedicineIntensity (physics)CancerInternal medicineRadiologyPhysicsOptics

Abstract

fetched live from OpenAlex

Low-intensity ultrasound (LIUS) combined with neoadjuvant chemotherapy drugs has been reported to have synergistic anti-breast cancer effects. However, very few studies revealed the relationship of ultrasound dose and its anti-cancer bioeffects. In this study, we examined the bioeffects of multiple acoustic parameters and drug concentrations. The optimal doses for both LIUS and the chemodrug epirubicin (EPI) were obtained and performed the maximal anti-cancer outcome. The expression levels of two functional genes— S100A6 and TAP1 genes were upregulated after the combined use of LIUS and EPI, indicating the significant impact of the treatment at the molecular level. The results also suggest that LIUS combined with EPI and microbubbles could potentially lead to a better prognosis of the breast cancer.

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 score0.003

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 head0.003
GPT teacher head0.203
Teacher spread0.200 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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