MétaCan
Menu
Back to cohort
Record W2909850450 · doi:10.1089/cbr.2018.2566

Lentivirus-Based Virus-Like Particles Mediate Delivery of Caspase 8 into Breast Cancer Cells and Inhibit Tumor Growth

2019· article· en· W2909850450 on OpenAlexaff
Zhujun Ao, Wei Chen, Jun Tan, Yu-Ling Cheng, Yanlan Xu, Lijun Wang, Xiaojian Yao

Bibliographic record

VenueCancer Biotherapy and Radiopharmaceuticals · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsApoptosisCarcinogenesisCancer researchCancer cellCancerProgrammed cell deathBreast cancerVesicular stomatitis virusCell growthBiologyCaspaseVirusChemistryImmunologyBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: Apoptosis plays an important role in both carcinogenesis and cancer treatment. Drugs or treatment strategies that can restore the apoptotic signaling pathways have the potential to eliminate cancer. Caspase 8 (CASP8) plays a vital role in the propagation of an enzymatic cascade that results in cell apoptosis. METHODS AND RESULTS: In this study, the authors investigated the inhibitory effects of a HIV Gag virus-like particles (VLPs) that are incorporated with an active CASP8 (Gag-CASP8-VLPs) on the growth of breast cancer. Their data have shown that Gag-CASP8-VLPs, pseudotyped by the stomatitis virus G protein (VSV-G), can efficiently enter and deliver active CASP8 into breast cancer cells, leading to massive cell apoptosis and death. Interestingly, an injection of Gag-CASP8-VLPs in the tumor tissues of a 4T1 mouse breast cancer model can effectively inhibit tumor growth, and the earlier the Gag-CASP8-VLPs is administered, the more profoundly the tumor growth is inhibited. CONCLUSIONS: Overall, Gag-CASP8-VLPs can deliver CASP8 into breast cancer cells, induce cell apoptosis, and inhibit tumor growth.

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 categoriesInsufficient payload (model declined to judge)
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 score0.050
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.286
Teacher spread0.274 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCancer Biotherapy and RadiopharmaceuticalsSame topicinterferon and immune responsesFrench-language works237,207