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Record W1992601419 · doi:10.1002/jgm.288

Antisense <i>MBD2</i> gene therapy inhibits tumorigenesis

2002· article· en· W1992601419 on OpenAlexaff
Andrew Slack, Veronica Bovenzi, Pascal Bigey, M. A. Ivanov, Shyam Ramchandani, Sanjoy K. Bhattacharya, Benjamin R. tenOever, Badia Lamrihi, Daniel Scherman, Moshe Szyf

Bibliographic record

VenueThe Journal of Gene Medicine · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcGill University
FundersNational Cancer Institute
KeywordsCarcinogenesisTransfectionDemethylaseSense (electronics)BiologyDNA methylationCancer researchMolecular biologyAntisense RNAGenetic enhancementViral vectorVectors in gene therapyGeneGene expressionChemistryEpigeneticsGeneticsRecombinant DNA

Abstract

fetched live from OpenAlex

BACKGROUND: Aberration in the pattern of DNA methylation is one of the hallmarks of cancer. We present data suggesting that dysregulation of MBD2, a recently characterized member of a novel family of methylated DNA binding proteins, is involved in tumorigenesis. Two functions were ascribed to MBD2, DNA demethylase activity and repression of methylated genes. METHODS: Multiple antisense expression and delivery systems, transfection, electrotransfer and adenoviral were employed to demonstrate that MBD2 is essential in tumorigenesis, both ex vivo and in vivo. RESULTS: Inhibition of MBD2 by antisense expression resulted in inhibition of anchorage-independent growth of antisense transfected cancer cells or cells infected with an adenoviral vector expressing MBD2 antisense. Xenograft tumors treated with an adenoviral vector expressing MBD2 antisense or xenografts treated with electrotransferred plasmids expressing MBD2 antisense showed reduced growth. CONCLUSIONS: These results support the hypothesis that one or both of the functions described for MBD2 are critical in tumorigenesis and that MBD2 is a potential anticancer target.

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 score0.029
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.256
Teacher spread0.230 · 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

Citations56
Published2002
Admission routes1
Has abstractyes

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