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Record W2081611049 · doi:10.2174/1566523052997460

Viral Vectors for Cancer Gene Therapy: Viral Dissemination and Tumor Targeting

2005· review· en· W2081611049 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Gene Therapy · 2005
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOncolytic virusVirotherapyGenetic enhancementVirusViral vectorCancerVector (molecular biology)VirologyClinical trialGeneMedicineCancer researchBiologyBioinformaticsGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Cancer gene therapy is the most promising and active field in gene therapy treatment. Although previous experimental and clinical trials have brought forward some exciting cases, in general, the clinical benefits have been limited. A major difference between virus-mediated gene therapy and other therapies is the poor physical diffusibility of viral vectors, which is also one of the major obstacles in cancer gene therapy. As safety is a prerequisite to enhanced viral dissemination, tumor-specific targeting becomes crucial. The present review focuses on questions related to efficient viral dissemination in tumor masses and how to sustain a high level of oncolytic virus targeting of tumor cells only. We will first consider two common reasons for limited virus spread in tumor masses and then discuss strategies for improving the tumor-specific oncolysis of currently used viral vectors and to comment on their advantages and potential problems.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.056
GPT teacher head0.415
Teacher spread0.359 · 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