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Record W2909736431 · doi:10.1002/tox.22705

Coronarin D induces human oral cancer cell apoptosis though upregulate JNK1/2 signaling pathway

2019· article· en· W2909736431 on OpenAlexaff
Yen‐Tze Liu, Ming‐Ju Hsieh, Jen‐Tsun Lin, Gene Chen, Chia‐Chieh Lin, Yu‐Sheng Lo, Yi‐Ching Chuang, Yi‐Ting Hsi, Mu‐Kuan Chen, Ming‐Chih Chou

Bibliographic record

VenueEnvironmental Toxicology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsSurrey Memorial Hospital
FundersChanghua Christian HospitalNational Science Council
KeywordsApoptosisCancerCancer cellCancer researchViability assayProgrammed cell deathChemistryNasopharyngeal carcinomaBiologyMedicineInternal medicineBiochemistryRadiation therapy

Abstract

fetched live from OpenAlex

The incidence of oral cancer is increasing all over the world, with rates particularly high in Southeast Asian countries, such as Taiwan. Coronarin D (CD) has been confirmed to have anti-inflammatory, anti-bacterial effects, and anti-apoptotic effects in human hepatocellular carcinoma and nasopharyngeal carcinoma. The purpose of this study is to explore whether CD has a suppression effect on oral cancer cells and the mechanisms involved. The results of our study revealed the significantly decreased cancer cell viability and increased activation of apoptosis via increased loss of mitochondrial membrane potential, increased death receptors, leading to the activation of caspase-8, -9, -3. Moreover, the rate of apoptosis of cells treated with CD plus JNK inhibitors was decreased compared to CD-treated cells. This is the first study to demonstrate that CD induces apoptosis in human oral cancer cells and can be expected to be a promising anticancer agent for oral cancer treatment.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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