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Record W2564523006 · doi:10.1038/srep39460

The Angiogenic Secretome in VEGF overexpressing Breast Cancer Xenografts

2016· article· en· W2564523006 on OpenAlexfundno aff
Louis Doré-Savard, Esak Lee, Samata Kakkad, Aleksander S. Popel, Zaver M. Bhujwalla

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Cancer InstituteBreast Cancer Society of Canada
KeywordsAngiogenesisBreast cancerTriple-negative breast cancerCancer researchCancerVEGF receptorsTumor microenvironmentBiologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

The plasticity of cancer cells and the fluidity of the tumor microenvironment continue to present major challenges in the comprehensive understanding of cancer that is essential to design effective treatments. The tumor interstitial fluid (TIF) encompasses the secretome and holds the key to several of the phenotypic characteristics of cancer. Difficulties in sampling this fluid have resulted in limited characterization of its components. Here we have sampled TIF from triple negative and estrogen receptor (ER)-positive human breast tumor xenografts with or without VEGF overexpression. Angiogenesis-related factors were characterized in the TIF and plasma, to understand the relationship between the TIF and plasma secretomes. Clear differences were observed between the TIF and plasma angiogenic secretomes in triple negative MDA-MB-231 breast cancer xenografts compared to ER-positive MCF-7 xenografts with or without VEGF overexpression that provide new insights into TIF components and the role of VEGF in modifying the angiogenic secretome.

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 score0.617
Threshold uncertainty score0.274

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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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

Citations29
Published2016
Admission routes1
Has abstractyes

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