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Record W2981778203 · doi:10.1002/biot.201900286

Adeno‐Associated Viral Vectors for Homology‐Directed Generation of CAR‐T Cells

2019· review· en· W2981778203 on OpenAlexafffund
Pablo Diego Moço, Noga Aharony, Amine Kamen

Bibliographic record

VenueBiotechnology Journal · 2019
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHomology (biology)Computational biologyVirologyBiologyGeneticsCell biologyGene

Abstract

fetched live from OpenAlex

Immunotherapy with T cells expressing chimeric antigen receptors (CAR) is an emerging and promising treatment against refractory cancers. However, the currently adopted methods of modification of T cells pose a risk of insertional oncogenesis because lentiviral and retroviral vectors integrate the CAR transgene in a semi-random fashion. In addition, this therapy is only available using autologous cells, which create problems in production and limit the access for patients who have their T cells depleted. One modification method that shows the ability to overcome both drawbacks is the knock-in of the CAR simultaneously knocking-out genes that prevent allogeneic therapy, such as the endogenous T cell receptor. In this mini-review, the authors present recent efforts to develop safer universal CAR-T cells. More specifically, the combined application of target-directed nucleases, which create a double-strand break at a specific genome locus, and the delivery of CAR DNA via adeno-associated viral vectors for subsequent integration via homologous recombination and silencing of the targeted gene is focused on.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.104
GPT teacher head0.376
Teacher spread0.272 · 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

Citations24
Published2019
Admission routes2
Has abstractyes

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