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Record W2156636441 · doi:10.1158/0008-5472.can-06-1227

Therapeutic Expression of an Anti-Death Receptor 5 Single-Chain Fixed-Variable Region Prevents Tumor Growth in Mice

2006· article· en· W2156636441 on OpenAlexaff
Juan Shi, Yanxin Liu, Yong Zheng, Yabin Guo, Jinchun Zhang, Pik‐to Cheung, Ruian Xu, Dexian Zheng

Bibliographic record

VenueCancer Research · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
Fundersnot available
KeywordsMonoclonal antibodyRecombinant DNASingle-chain variable fragmentMolecular biologyTransduction (biophysics)AntibodyBiologyApoptosisGenetic enhancementIn vivoCancer researchIn vitroCell cultureVirologyGeneImmunologyBiochemistry

Abstract

fetched live from OpenAlex

The clinical use of the single-chain fixed-variable (scFv) fragments of recombinant monoclonal antibodies as credible alternatives for classic therapeutic antibodies has two limitations: rapid blood clearance and inefficient local expression of functional molecules. In attempt to address these issues, we have developed a novel gene therapy protocol in which the anti-death receptor 5 (DR5) scFv fragments were either in vitro expressed in several tumor cell lines, or in vivo expressed in mice, using recombinant adeno-associated virus (rAAV)-mediated gene transfer. Viral transduction using the rAAV-S3C construct, which encodes a scFv molecule (S3C scFv) specific to DR5, led to stable expression in tumor cell lines and showed apoptosis-inducing activity in vitro, which could be inhibited by recombinant DR5 but not by DR4. A single i.m. injection of rAAV-S3C virus in nude mice resulted in stable expression of DR5-binding S3C scFv proteins in mouse sera for at least 240 days. Moreover, the expression of S3C scFv was associated with significant suppression of tumor growth and the increase of tumor cell apoptosis in previously established s.c. human lung LTEP-sml and liver Hep3B tumor xenografts.

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)0.001
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 score0.003
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.356
Teacher spread0.305 · 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

Citations23
Published2006
Admission routes1
Has abstractyes

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