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Record W3159905002 · doi:10.1016/j.jconrel.2021.04.023

Non-viral gene delivery of the oncotoxic protein NS1 for treatment of hepatocellular carcinoma

2021· article· en· W3159905002 on OpenAlexaff
Dominik Witzigmann, Philip Grossen, Cristina Quintavalle, Manuela Lanzafame, Susanne Schenk, Xue-Ting Tran, Bernhard Englinger, Patrick Hauswirth, David Grünig, Sushilla van Schoonhoven, Stephan Krähenbühl, Luigi Terracciano, Walter Berger, Salvatore Piscuoglio, Luca Quagliata, Jean Rommelaere, Jürg P. F. Nüesch, Jörg Huwyler

Bibliographic record

VenueJournal of Controlled Release · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of British Columbia
FundersOncosuisseSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNovartis
KeywordsHepatocellular carcinomaCancer researchGenetic enhancementBiologyKinaseGeneProgrammed cell deathBiomarkerGene expressionImmunologyMedicineApoptosisCell biologyGenetics

Abstract

fetched live from OpenAlex

Hepatocellular carcinoma (HCC) is related to increasing incidence rates and poor clinical outcomes due to lack of efficient treatment options and emerging resistance mechanisms. The aim of the present study is to exploit a non-viral gene therapy enabling the expression of the parvovirus-derived oncotoxic protein NS1 in HCC. This anticancer protein interacts with different cellular kinases mediating a multimodal host-cell death. Lipoplexes (LPX) designed to deliver a DNA expression plasmid encoding NS1 are characterized using a comprehensive set of in vitro assays. The mechanisms of cell death induction are assessed and phosphoinositide-dependent kinase 1 (PDK1) is identified as a potential predictive biomarker for a NS1-LPX-based gene therapy. In an HCC xenograft mouse model, NS1-LPX therapeutic approach results in a significant reduction in tumor growth and extended survival. Data provide convincing evidence for future studies using a targeted NS1 gene therapy for PDK1 overexpressing 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 score0.008
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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