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Record W2066334259 · doi:10.1016/j.bbmt.2007.09.014

A Roadmap to Safe, Efficient, and Stable Lentivirus-Mediated Gene Therapy with Hematopoietic Cell Transplantation

2007· review· en· W2066334259 on OpenAlexaff
Anton Neschadim, J. Andrea McCart, Armand Keating, Jeffrey A. Medin

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsPrincess Margaret Cancer CentreToronto General HospitalUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsGenetic enhancementLentivirusViral vectorTransgeneTransplantationVector (molecular biology)MedicineHaematopoiesisStem cellHematopoietic stem cell transplantationGene transferGenetically modified organismComputational biologyGeneHematopoietic stem cellClinical trialCancer researchBioinformaticsHuman immunodeficiency virus (HIV)VirologyBiologyGeneticsViral diseaseInternal medicineRecombinant DNA

Abstract

fetched live from OpenAlex

Hematopoietic stem cells comprise a prominent target for gene therapy aimed at treating various genetic and acquired disorders. A number of limitations associated with hematopoietic cell transplantation can be circumvented by the use of cells stably modified by retroviral gene transfer. Oncoretroviral and lentiviral vectors offer means for generating efficient and stable transgene expression. This review summarizes the state of the field today in terms of vector development and clinical experimentation. In particular, concerns with the safety of retroviral vectors intended for clinical gene transfer, applicability of preclinical data in directing clinical trial design, and recent research aimed at resolving some of these issues are addressed. Finally, this review underlines the specific advantages offered by lentiviral gene-transfer vectors for gene therapy in stem cells.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.024
GPT teacher head0.302
Teacher spread0.278 · 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

Citations31
Published2007
Admission routes1
Has abstractno

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