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Record W2115365418 · doi:10.5582/bst.2011.v5.2.52

Effect of c-Met inhibitor SU11274 on hepatocellular carcinoma cell growth

2011· article· en· W2115365418 on OpenAlexaff
Yoshinori Inagaki, Fanghua Qi, Jianjun Gao, Xian‐Jun Qu, Kiyoshi Hasegawa, Yasuhiko Sugawara, Wei Tang, Norihiro Kokudo

Bibliographic record

VenueBioScience Trends · 2011
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsPancreas Centre (Canada)
FundersJapan Society for the Promotion of Science
KeywordsHepatocellular carcinomaCell growthCancer researchTyrosine-kinase inhibitorChemistryCellTyrosine kinaseMedicineReceptorInternal medicineCancerBiochemistry

Abstract

fetched live from OpenAlex

c-Met, a type of receptor tyrosine kinase, may be significantly associated with the progression of hepatocellular carcinoma (HCC). In addition, des-γ-carboxyprothrombin (DCP) has been found to interact with c-Met and activate HCC cell growth. Therefore, the functional inhibition of c-Met expressed on HCC cells should arrest HCC cell growth. The present study found that the c-Met inhibitor SU11274 suppressed HCC cell growth by inhibiting the activation of c-Met. Furthermore, this inhibitor also neutralized the activation of HCC cell growth resulting from the addition of DCP. These results suggest that the functional inhibition of c-Met might be a target for the development of chemotherapeutic agents for HCC, and especially those that are positive for expression of DCP.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.252
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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
Published2011
Admission routes1
Has abstractyes

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