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Record W1973730551 · doi:10.1002/ijc.11398

Estradiol increases extracellular levels of vascular endothelial growth factor <i>in vivo</i> in murine mammary cancer

2003· article· en· W1973730551 on OpenAlexaff
Charlotta Dabrosin, Peter J. Margetts, Jack Gauldie

Bibliographic record

VenueInternational Journal of Cancer · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAngiogenesisVascular endothelial growth factorExtracellularIn vivoMicrodialysisExtracellular fluidBreast cancerCancerCancer researchMetastasisMammary tumorVascular endothelial growth factor AMedicineBiologyInternal medicineEndocrinologyPathologyVEGF receptorsCell biology

Abstract

fetched live from OpenAlex

Angiogenesis is essential for tumor growth and metastasis and an important prognostic factor in breast cancer. VEGF, a key factor for angiogenesis, has been correlated with tumor vessel density in breast cancer. Estrogen, another crucial factor in breast cancer, stimulates VEGF; and an ERE in the VEGF gene has been defined. VEGF is bioactive in the extracellular fluid, where it becomes available to endothelial cells. Whether E(2) affects VEGF levels in the extracellular fluid is not known. We show, using intratumoral microdialysis in vivo, that E(2) treatment increased tumor extracellular levels of VEGF in an estrogen-dependent breast cancer model. Moreover, extracellular levels of VEGF in the tumor showed a strong correlation with total tumor VEGF, contrary to plasma levels of VEGF. Ninety-three percent of measured VEGF in the extracellular fluid in the tumor was tumor-derived, while only 45% of VEGF in circulating plasma originated from the tumor. We conclude that E(2) increases extracellular VEGF and that microdialysis is a sensitive method for measurement of local VEGF production in vivo. Our results have potential application to the assessment of tumor characteristics in vivo in human tumors for individualized cancer therapy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.486

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 head0.014
GPT teacher head0.282
Teacher spread0.267 · 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

Citations76
Published2003
Admission routes1
Has abstractyes

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