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Record W1798925125 · doi:10.1139/cjpp-2014-0032

Vitronetcin promotes cell growth and inhibits apoptotic stimuli in a human hepatoma cell line via the activation of caspases

2014· article· en· W1798925125 on OpenAlexvenueno aff
Wei Zhu, Yingzhi Liu, Konghe Hu, Wenxue Li, Jianling Chen, Juntao Li, Guang‐Yu Yang, Jinyin Wu, Chuanxi Fu, Qiansheng Hu

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2014
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicBioactive Compounds and Antitumor Agents
Canadian institutionsnot available
Fundersnot available
KeywordsApoptosisCell biologyCell growthBiologyCell cultureCellFlow cytometryChemistryMolecular biologyBiochemistry

Abstract

fetched live from OpenAlex

This study sought to understand the effects of vitronectin (VTN) on the growth of SMMC-7721 hepatoma cells. In addition, this study examined how VTN inhibits the induction of apoptosis in SMMC-7721 cells by 3,3'-diindolylmethane (DIM), a metabolite of natural phytochemicals, and preliminarily investigated the signaling molecules involved in this process. A cell proliferation reagent was used to observe the effects of VTN on cell proliferation rates. Laser scanning confocal microscopy was performed to observe the effects of VTN on the morphology of tubulin, a component of the cytoskeleton. Flow cytometry and Western blotting assays were used to observe the inhibitory effects of VTN on DIM-induced apoptosis in SMMC-7721 cells and changes in the expression levels of the signaling molecules involved in this process. VTN promoted tumor cell growth in a concentration-dependent manner and inhibited apoptosis caused by the effects of apoptosis-inducing agents. Under in vitro experimental conditions, VTN contributed to the growth of SMMC-7721 hepatoma cells and protected them from the effects of an apoptosis-inducing agent. These findings suggest that during hepatocellular carcinogenesis, VTN may promote tumor cell growth and inhibit chemically induced apoptosis.

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)0.001
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 score0.010
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.345
Teacher spread0.294 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Physiology and PharmacologySame topicBioactive Compounds and Antitumor AgentsFrench-language works237,207