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Record W2067454244 · doi:10.1089/vim.2004.17.327

Adenoviral Vectors for Gene Replacement Therapy

2004· review· en· W2067454244 on OpenAlexaff
Huibi Cao, David R. Koehler, Jim Hu

Bibliographic record

VenueViral Immunology · 2004
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of TorontoCanadian Institutes of Health Research
Fundersnot available
KeywordsLimitingGenetic enhancementViral vectorGeneVector (molecular biology)Gene deliveryComputational biologyBiologyImmune systemVirologyImmunologyGenetics

Abstract

fetched live from OpenAlex

Adenovirus-based vectors are promising vehicles for gene replacement therapy due to their ability to efficiently transduce a wide variety of proliferating and non-proliferating cells. Over the past decade, different versions of adenoviral vectors (Ads) have been developed. These vectors can be classified into two major categories, based on whether the viral coding sequences are partially (first or second-generation Ads) or completely deleted (helper-dependent or gutted Ads). Both types of Ads have been tested in a variety of gene delivery studies, and major obstacles to their clinical application have been identified. Currently, innate and adaptive host immune responses to Ads remain major challenges, limiting both the initial viral dose and the effectiveness of subsequent administrations. Recent developments in vector design and delivery methods have improved the potential of Ads for successful gene therapy application.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.000
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 head0.047
GPT teacher head0.365
Teacher spread0.318 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations83
Published2004
Admission routes1
Has abstractyes

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