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Lipid-Nanoparticle-Based Delivery of CRISPR/Cas9 Genome-Editing Components

2022· review· en· W4281253995 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMolecular Pharmaceutics · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCHuntington Society of Canada
KeywordsCRISPRGenome editingCas9Computational biologyFlexibility (engineering)GenomeComputer scienceBiologyGeneGenetics

Abstract

fetched live from OpenAlex

Gene editing mediated by CRISPR/Cas9 systems is due to become a beneficial therapeutic option for treating genetic diseases and some cancers. However, there are challenges in delivering CRISPR components which necessitate sophisticated delivery systems for safe and effective genome editing. Lipid nanoparticles (LNPs) have become an attractive nonviral delivery platform for CRISPR-mediated genome editing due to their low immunogenicity and application flexibility. In this review, we provide a background of CRISPR-mediated gene therapy, as well as LNPs and their applicable characteristics for delivering CRISPR components. We then highlight the challenges of CRISPR delivery, which have driven the significant development of new, safe, and optimized LNP formulations in the past decade. Finally, we discuss considerations for using LNPs to deliver CRISPR and future perspectives on clinical translation of LNP-CRISPR gene editing.

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.

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 score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
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 head0.062
GPT teacher head0.385
Teacher spread0.323 · 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