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Record W2004456466 · doi:10.1074/jbc.m212158200

Vascular Endothelial Growth Factor Receptor-2 Induces Survival of Hematopoietic Progenitor Cells

2003· article· en· W2004456466 on OpenAlexaff
Bruno Larrivée, David Lane, Ingrid L. Pollet, Peggy L. Olive, R. Keith Humphries, Aly Karsan

Bibliographic record

VenueJournal of Biological Chemistry · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsHaematopoiesisProgenitor cellBone marrowVascular endothelial growth factorBiologyCancer researchStem cell factorCell biologyPopulationHemangioblastStem cellImmunologyMedicineVEGF receptors

Abstract

fetched live from OpenAlex

Vascular endothelial growth factor (VEGF) and its receptors play an essential role in the formation and maintenance of the hematopoietic and vascular compartments. The VEGF receptor-2 (VEGFR-2) is expressed on a population of hematopoietic cells, although its role in hematopoiesis is still unclear. In this report, we have utilized a strategy to selectively activate VEGFR-2 and study its effects in primary bone marrow cells. We found that VEGFR-2 can maintain the hematopoietic progenitor population in mouse bone marrow cultured in the absence of exogenous cytokines. Maintenance of the hematopoietic progenitor population is due to increased cell survival with minimal effect on proliferation. Progenitor survival is mainly mediated by activation of the phosphatidylinositol 3'-kinase/Akt pathway. Although VEGFR-2 also activated Erk1/2 mitogen-activated protein kinase, it did not induce cell proliferation, and blockade of this pathway only partially decreased VEGFR-2-mediated survival of hematopoietic progenitors. Thus, the role of VEGFR-2 in hematopoiesis is likely to maintain survival of hematopoietic progenitors through the activation of antiapoptotic pathways.

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 score0.012
Threshold uncertainty score0.544

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 head0.253
Teacher spread0.230 · 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

Citations42
Published2003
Admission routes1
Has abstractyes

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