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Record W4285240041 · doi:10.1016/j.csbj.2022.06.015

Development and clinical translation of <i>ex vivo</i> gene therapy

2022· review· en· W4285240041 on OpenAlexaff
Xiaomo Wu, Xiaorong He, Fahui Liu, Xiaochang Jiang, Ping Wang, Jinyan Zhang, Ju Jiang

Bibliographic record

VenueComputational and Structural Biotechnology Journal · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersScience Foundation of FuzhouNatural Science Foundation of Fujian Province
KeywordsGenetic enhancementGenome editingEx vivoMedicineViral vectorComputational biologyVector (molecular biology)Translational medicineBioinformaticsBiologyGeneBiotechnologyGenomeIn vivoPathologyGenetics

Abstract

fetched live from OpenAlex

Retroviral gene therapy has emerged as a promising therapeutic modality for multiple inherited and acquired human diseases. The capability of delivering curative treatment or mediating therapeutic benefits for a long-term period following a single application fundamentally distinguishes this medical intervention from traditional medicine and various lentiviral/γ-retroviral vector-mediated gene therapy products have been approved for clinical use. Continued advances in retroviral vector engineering, genomic editing, synthetic biology and immunology will broaden the medical applications of gene therapy and improve the efficacy and safety of the treatments based on genetic correction and alteration. This review will summarize the advent and clinical translation of ex vivo gene therapy, with the focus on the milestones during the exploitation of genetically engineered hematopoietic stem cells (HSCs) tackling a variety of pathological conditions which led to marketing approval. Finally, current statue and future prospects of gene editing as an alternative therapeutic approach are also discussed.

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)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.040
GPT teacher head0.366
Teacher spread0.326 · 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

Citations21
Published2022
Admission routes1
Has abstractyes

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