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Blockade of in vivo VEGF-mediated angiogenesis by antisense gene therapy: role of Flk-1 and Flt-1 receptors

2002· article· en· W1836530012 on OpenAlexaff
Geneviève S. Marchand, Nicolas Noiseux, Jean‐François Tanguay, Martin G. Sirois

Bibliographic record

VenueAmerican Journal of Physiology-Heart and Circulatory Physiology · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsAngiogenesisVascular endothelial growth factorBiologyEndocrinologyNeovascularizationReceptorVascular endothelial growth factor AKinase insert domain receptorIn vivoInternal medicineCancer researchMedicineVEGF receptors

Abstract

fetched live from OpenAlex

Angiogenesis, the formation of new blood vessels from preexisting ones, is a critical component of various pathologies such as tumor progression, rheumatoid arthritis, and retinopathies. Vascular endothelial growth factor (VEGF) is a mitogenic and chimiotactic factor capable of inducing angiogenesis through the activation of its receptors, fetal liver kinase-1 (Flk-1) and fms-like tyrosine kinase-1 (Flt-1), expressed on endothelial cells. The purpose of the present study was to assess if a treatment with antisense (AS) oligonucleotides directed against VEGF receptors Flk-1 or Flt-1 mRNA could prevent VEGF-mediated angiogenesis. With the use of miniosmotic pumps, phosphate-buffered saline, VEGF, or VEGF combined with AS-Flk-1, AS-Flt-1, or AS-scrambled oligonucleotides were released in mouse testis for 14 days. VEGF (1, 2.5, and 5 microg) increased the formation of new capillary blood vessels by 236, 246, and 287%, respectively. The combination of AS-Flk-1 or AS-Flt-1 (200 microg) to VEGF (2.5 microg) reduced by 87 and 85% the formation of new blood vessels, respectively, and the expression of their corresponding proteins. These data demonstrate the therapeutical potential of AS-Flk-1 or AS-Flt-1 to prevent VEGF-mediated angiogenesis in vivo.

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 score0.292
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations43
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Physiology-Heart and Circulatory PhysiologySame topicAngiogenesis and VEGF in CancerFrench-language works237,207