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Record W4378902943 · doi:10.3390/v15061294

Combination of Oncolytic Measles Virus and Ursolic Acid Synergistically Induces Oncolysis of Hepatocellular Carcinoma Cells

2023· article· en· W4378902943 on OpenAlexaff
Ching‐Hsuan Liu, Chen‐Jei Tai, Yu‐Ting Kuo, Shen‐Shong Chang, Liang Lin

Bibliographic record

VenueViruses · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsDalhousie University
FundersTaipei Medical University HospitalTaipei Medical UniversityMinistry of Science and Technology, Taiwan
KeywordsOncolytic virusHepatocellular carcinomaVirotherapyMeasles virusHepatitis B virusCancer researchApoptosisUrsolic acidHepatitis C virusVirologyLiver cancerCancer cellCombination therapyCancerVirusMedicineBiologyMeaslesVaccinationPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Hepatocellular carcinoma (HCC) remains a difficult-to-treat cancer due to late diagnosis and limited curative treatment options. Developing more effective therapeutic strategies is essential for the management of HCC. Oncolytic virotherapy is a novel treatment modality for cancers, and its combination with small molecules merits further exploration. In this study, we combined oncolytic measles virus (MV) with the natural triterpenoid compound ursolic acid (UA) and evaluated their combination effect against HCC cells, including those harboring hepatitis B virus (HBV) or hepatitis C virus (HCV) replication. We found that the combination of MV and UA synergistically induced more cell death in Huh-7 HCC cells through enhanced apoptosis. In addition, increased oxidative stress and loss of mitochondrial potential were observed in the treated cells, indicating dysregulation of the mitochondria-dependent pathway. Similar synergistic cytotoxic effects were also found in HCC cells harboring HBV or HCV genomes. These findings underscore the potential of oncolytic MV and UA combination for further development as a treatment strategy for 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)0.000
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 score0.002
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.037
GPT teacher head0.302
Teacher spread0.265 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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