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Record W2147949348 · doi:10.3892/mmr.2015.3926

Berberine induces apoptosis by suppressing the arachidonic acid metabolic pathway in hepatocellular carcinoma

2015· article· en· W2147949348 on OpenAlexaff
Jing Li, Ou Li, Mujie Kan, Ming Zhang, Dan Shao, Yue Pan, Huilin Zheng, Xuewen Zhang, Li Chen, Songyan Liu

Bibliographic record

VenueMolecular Medicine Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicBerberine and alkaloids research
Canadian institutionsUniversity of Manitoba
FundersPeople's Government of Jilin Province
KeywordsArachidonic acidApoptosisPhospholipase A2BerberineOncogeneHepatocellular carcinomaProstaglandin E2ChemistryCell cycleCyclooxygenaseCancer researchPharmacologyBiologyBiochemistryEndocrinologyEnzyme

Abstract

fetched live from OpenAlex

Berberine (BBR) has been suggested as a potential candidate anticancer agent due to its high anticancer activity and multiple mechanisms. In the present study, the inhibitory effect of BBR on hepatocellular carcinoma (HCC) via the suppression of the arachidonic acid (AA) metabolic pathway was investigated. BBR was demonstrated to reduce the viabilities of H22, HepG2 and Bel‑7404 cells, in a dose‑ and time‑dependent manner, and increase the number of apoptotic cells. BBR induced the translocation of apoptosis‑inducing factor between the mitochondria and the nucleus, and had no effects on the protein expression levels of caspase‑3 or ‑9. In addition, BBR significantly suppressed the protein expression levels of cytosolic phospholipase A2 (cPLA2) and cyclooxygenase (COX)‑2 and elevated the content ratio of AA to prostaglandin E2 (PGE2). Furthermore, BBR reduced the volume and weight of tumors in a H22 transplanted tumor model in mice. The results of the present study demonstrated that elevation in the ratio of AA to PGE2 via suppression of the protein expression of cPLA2 and COX‑2 in the AA metabolic pathway is involved in the inhibitory effect of BBR in HCC.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.294
Teacher spread0.261 · 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.

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

Citations65
Published2015
Admission routes1
Has abstractyes

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