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Record W2160912691 · doi:10.5430/jst.v3n1p1

Novel anti-angiogenic agents for colorectal cancer. Are we moving on?

2012· article· en· W2160912691 on OpenAlexvenueno aff
Alexios S Strimpakos, Muhammad Wasif Saif, K. Syrigos

Bibliographic record

VenueJournal of Solid Tumors · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsPlacental growth factorAngiogenesisVascular endothelial growth factorCancer researchGrowth factorVascular endothelial growth factor CPlatelet-derived growth factor receptorVascular endothelial growth factor AGrowth factor receptor inhibitorInternal medicineMedicineBiologyVEGF receptorsEndocrinologyReceptor

Abstract

fetched live from OpenAlex

Tumours depend greatly on blood supply to grow, spread and metastasise. It has been proposed, for a long time now, that angiogenesis and neo-vasculature development are present in tumours.(1;2) Angiogenesis is subject to regulation by a number of pro-angiogenic growth factors such as the vascular endothelial growth factors (VEGFs), angiopoetin, platelet derived growth factor (PDGF), placental growth factor (PlGF) and others, which are counteracted by proangiogenic growth factors such as angiostatin, transforming growth factor-beta (TGF-β) and others.(1;3) Among those growth factors, the most studied and associated with tumour growth and neo-angiogenesis is the family of the vascular endothelial growth factor (VEGF), which includes VEGF-A (the archetypal member, often called as VEGF), placental growth factor (PlGF), VEGF-B, VEGF-C, and VEGF-D (known also as c-Fos-induced growth factor, FIGF), and the viral VEGF-E. These growth factors exert their biological function through the VEGF transmembrane receptors VEGFR-1, -2 and -3. In particular, VEGF binds to VEGFR-1 and -2, VEGF-B and PlGF only to VEGFR-1, VEGF-C and VEGF-D to VEGFR-2 and -3.(4-8)

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 score0.093
Threshold uncertainty score0.479

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 head0.033
GPT teacher head0.320
Teacher spread0.287 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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