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Record W2101994706 · doi:10.1158/0008-5472.can-09-3592

Vascular Endothelial Growth Factor—A Positive and Negative Regulator of Tumor Growth

2010· article· en· W2101994706 on OpenAlexafffund
Laura M. Vecchiarelli-Federico, David Cervi, Mehran Haeri, Yanmei Li, András Nagy, Yaacov Ben‐David

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsHealth Sciences CentreLunenfeld-Tanenbaum Research InstituteUniversity of TorontoSunnybrook Health Science CentreMount Sinai Hospital
FundersCanadian Institutes of Health Research
KeywordsAngiogenesisTumor progressionVascular endothelial growth factorCancer researchBiologyTumor microenvironmentMetastasisHaematopoiesisContext (archaeology)Genetically modified mouseTumor initiationRegulatorVascular endothelial growth factor ACancerImmunologyTransgeneCell biologyVEGF receptorsStem cellTumor cellsGeneticsGene

Abstract

fetched live from OpenAlex

Over the past decade, the well-documented role of vascular endothelial growth factor (VEGF) in tumor angiogenesis has led it to become one of the leading therapeutic targets for the treatment of cancer. Emerging evidence from genetically modified animal models, however, suggests that elevated levels of VEGF, or a proangiogenic phenotype, may impede, rather than promote, early tumor development and progression. For example, hypermorph VEGF transgenic mice display delayed progression of a retroviral-induced murine leukemia, and knockdown of VEGF expression within the myeloid compartment accelerates tumor progression. Several mechanisms have been proposed to explain this paradox, whereby VEGF induces changes within the hematopoietic compartment and tumor microenvironment through recruitment of tumor inhibitory monocytic cells and the negative regulation of tumor angiogenesis. Thus, it is apparent that the levels of VEGF expression in both tumor and nontumor tissues, as well as the context and timing of its modulation relative to cancer induction, play an important role in determining the effects of VEGF expression on tumorigenicity. In light of these recent findings, the various mechanisms underlying the negative role of VEGF during early tumor development, progression, and metastasis will be discussed.

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 score0.028
Threshold uncertainty score0.438

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 head0.023
GPT teacher head0.332
Teacher spread0.310 · 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

Citations34
Published2010
Admission routes2
Has abstractyes

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