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Record W2941016857 · doi:10.1080/07357907.2019.1601209

Antitumor and Antiangiogenic Activities of Lenvatinib in Mouse Xenograft Models of Vascular Endothelial Growth Factor-Induced Hypervascular Human Hepatocellular Carcinoma

2019· article· en· W2941016857 on OpenAlexfundno aff
Yusuke Adachi, Masahiro Matsuki, Hideki Watanabe, Kazuma Takase, Kotaro Kodama, Junji Matsui, Yasuhiro Funahashi, K. Nomoto

Bibliographic record

VenueCancer Investigation · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsnot available
FundersEisai CanadaEisai
KeywordsLenvatinibCancer researchVascular endothelial growth factorHepatocellular carcinomaMedicineAngiogenesisUmbilical veinReceptor tyrosine kinaseKinase insert domain receptorVascular endothelial growth factor AInternal medicineSorafenibVEGF receptorsReceptorBiologyIn vitro

Abstract

fetched live from OpenAlex

High expression of vascular endothelial growth factor (VEGF) in patients with hepatocellular carcinoma (HCC) is associated with poor prognosis. Here, we investigated the antitumor activity of lenvatinib, a multiple receptor tyrosine kinase inhibitor, in VEGF-overexpressing HCC models. In human umbilical vein endothelial cells, lenvatinib showed potent inhibitory activities against VEGF-induced proliferation and VEGF/basic fibroblast growth factor-induced tube formation. In VEGF-overexpressing HCC xenograft models, characterized by aggressive tumor growth and hypervascularity, lenvatinib had significant antitumor and antiangiogenic activities. These results suggest that potent activity of lenvatinib against VEGF signaling underlies its antitumor and antiangiogenic activities in the hypervascular HCC models.

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 score0.315
Threshold uncertainty score0.700

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 head0.032
GPT teacher head0.248
Teacher spread0.216 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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