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Record W4396616763 · doi:10.3390/cimb46050255

Recent Therapeutic Gene Editing Applications to Genetic Disorders

2024· review· en· W4396616763 on OpenAlexafffund
Éric Deneault

Bibliographic record

VenueCurrent Issues in Molecular Biology · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsHealth Canada
FundersHealth CanadaGovernment of Canada
KeywordsGenome editingCRISPRCas9Computational biologyGenetic enhancementTranscription activator-like effector nucleaseBiologyComputer scienceBioinformaticsGeneGenetics

Abstract

fetched live from OpenAlex

Recent years have witnessed unprecedented progress in therapeutic gene editing, revolutionizing the approach to treating genetic disorders. In this comprehensive review, we discuss the progression of milestones leading to the emergence of the clustered regularly interspaced short palindromic repeats (CRISPR)-based technology as a powerful tool for precise and targeted modifications of the human genome. CRISPR-Cas9 nuclease, base editing, and prime editing have taken center stage, demonstrating remarkable precision and efficacy in targeted ex vivo and in vivo genomic modifications. Enhanced delivery systems, including viral vectors and nanoparticles, have further improved the efficiency and safety of therapeutic gene editing, advancing their clinical translatability. The exploration of CRISPR-Cas systems beyond the commonly used Cas9, such as the development of Cas12 and Cas13 variants, has expanded the repertoire of gene editing tools, enabling more intricate modifications and therapeutic interventions. Outstandingly, prime editing represents a significant leap forward, given its unparalleled versatility and minimization of off-target effects. These innovations have paved the way for therapeutic gene editing in a multitude of previously incurable genetic disorders, ranging from monogenic diseases to complex polygenic conditions. This review highlights the latest innovative studies in the field, emphasizing breakthrough technologies in preclinical and clinical trials, and their applications in the realm of precision medicine. However, challenges such as off-target effects and ethical considerations remain, necessitating continued research to refine safety profiles and ethical frameworks.

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 score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.441
Teacher spread0.416 · 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

Citations40
Published2024
Admission routes2
Has abstractyes

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