MétaCan
Menu
Back to cohort
Record W2810519364 · doi:10.1039/c8an00345a

Breast cancer subtype specific biochemical responses to radiation

2018· article· en· W2810519364 on OpenAlexaff
Phiranuphon Meksiarun, Pedro H. B. Aoki, Samantha J. Van Nest, Regivaldo G. Sobral-Filho, Julian J. Lum, Alexandre G. Brolo, Andrew Jirasek

Bibliographic record

VenueThe Analyst · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversity of VictoriaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusBC Cancer AgencyKelowna General HospitalUniversity of British Columbia
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsCancerBreast cancerCancer researchMedicineOncologyChemistryBiologyComputational biologyInternal medicine

Abstract

fetched live from OpenAlex

External beam radiotherapy is a common form of treatment for breast cancer. Among patients and across different breast cancer subtypes, the response to radiation is heterogeneous. Radiation-induced biochemical changes were examined by Raman spectroscopy using cell lines that represent a spectrum of human breast cancer. Principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) revealed unique Raman spectral features in the HER2 and Ki67 subtype. The changes in Raman spectral profiles to different doses of radiation (0-50 Gy) included variations in the levels of proteins, lipids, nucleic acids and glycogen. Importantly, the differences in radiation-induced changes on the normal breast epithelial cell line MCF10A could be discriminated within and across the various breast tumor cell lines. These results demonstrate a novel approach to uncover differences between breast cancer cell subtypes and surrounding normal tissues by their biochemical variations in response to radiation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.281

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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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 designNot applicable
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

Citations41
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe AnalystSame topicCancer, Lipids, and MetabolismFrench-language works237,207