MétaCan
Menu
Back to cohort

The beta‐globin locus control region versus gene therapy vectors: a struggle for expression

2001· review· en· W1578408211 on OpenAlexafffund
James Ellis, Dylan Pannell

Bibliographic record

VenueClinical Genetics · 2001
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of TorontoSickKids FoundationHospital for Sick Children
FundersMedical Research CouncilHospital for Sick Children
KeywordsLocus control regionGenetic enhancementBiologyTransgeneGene silencingRetrovirusGlobinGeneLocus (genetics)Viral vectorGeneticsVector (molecular biology)Gene expressionPromoter

Abstract

fetched live from OpenAlex

Developmental control of gene expression has a major impact on the design of beta-globin retrovirus vectors for hematopoietic stem cell gene therapy of beta-thalassemia. It is obvious that the endogenous locus control region (LCR) elements that drive beta-globin gene expression in transgenic mice must be included in these vectors. However, the specific elements to use are not clear and require an understanding of LCR action. Moreover, retrovirus vectors contain silencer elements that function in stem cells and are dominant to LCR function. Recent studies on LCRbeta-globin transgenes and retrovirus silencing suggest ways to overcome this silencing effect after transfer into stem cells and carefully designed lentivirus vectors have exciting therapeutic benefit in animal models of beta-thalassemia. By building on 15 years of development, LCRbeta-globin vectors are now being tested in preclinical animal models and may ultimately lead to the long-sought cure for this genetic disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.005

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 head0.171
GPT teacher head0.459
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueClinical GeneticsSame topicVirus-based gene therapy researchFrench-language works237,207