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Record W2135151919 · doi:10.1139/bcb-2012-0077

Enhanced 5-fluorouracil cytotoxicity in high COX-2 expressing hepatocellular carcinoma cells by wogonin via the PI3K/Akt pathway

2013· article· en· W2135151919 on OpenAlexvenueno aff
Li Na Zhao, Yunying Sha, Qing Zhao, Jing Yao, Binbin Zhu, Zhijian Lu, Qidong You, Qinglong Guo

Bibliographic record

VenueBiochemistry and Cell Biology · 2013
Typearticle
Languageen
FieldMedicine
TopicFlavonoids in Medical Research
Canadian institutionsnot available
FundersState Key Laboratory of Natural Medicines
KeywordsWogoninIn vivoCytotoxicityPI3K/AKT/mTOR pathwayProtein kinase BHepatocellular carcinomaPharmacologyCancer researchCombination therapyMedicineIn vitroChemistrySignal transductionBiologyScutellaria baicalensisTraditional Chinese medicineBiochemistryPathology

Abstract

fetched live from OpenAlex

Combination therapies may increase the antitumor effects and reduce the adverse effects for the treatment of hepatocellular carcinoma. In this study, we determined the effects of 5-fluorouracil alone or in combination with wogonin in vitro and in vivo, and we investigated the possible mechanisms. The combination of these 2 drugs led to a decrease in survival and a significant synergistic inhibitory effect on high COX-2 expression in SMMC-7721 hepatocellular carcinoma (HCC) cells. Furthermore, the results show that this combination inhibits COX-2 expression and increases sensitivity to chemotherapeutic agents partly through regulating the PI3K/Akt signaling pathway. Moreover, the combination treatment caused a significant growth inhibition of human tumor xenografts in vivo. In conclusion, wogonin may increase the cytotoxicity of some antineoplastic agents and it can be used in combination with these agents as a novel therapeutic regimen for HCC 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 score0.003

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 head0.011
GPT teacher head0.244
Teacher spread0.232 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

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