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Record W4387063536 · doi:10.1016/j.omtn.2023.102040

CRISPR-Cas9 delivery strategies with engineered extracellular vesicles

2023· review· en· W4387063536 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 Therapy — Nucleic Acids · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversité Laval
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchChina Scholarship Council
KeywordsCRISPRGenome editingCas9MicrovesiclesBiologyCell biologyExtracellular vesiclesComputational biologyExtracellular vesicleCellGenetic enhancementGenemicroRNAGenetics

Abstract

fetched live from OpenAlex

Therapeutic genome editing has the potential to cure diseases by directly correcting genetic mutations in tissues and cells. Recent progress in the CRISPR-Cas9 systems has led to breakthroughs in gene editing tools because of its high orthogonality, versatility, and efficiency. However, its safe and effective administration to target organs in patients is a major hurdle. Extracellular vesicles (EVs) are endogenous membranous particles secreted spontaneously by all cells. They are key actors in cell-to-cell communication, allowing the exchange of select molecules such as proteins, lipids, and RNAs to induce functional changes in the recipient cells. Recently, EVs have displayed their potential for trafficking the CRISPR-Cas9 system during or after their formation. In this review, we highlight recent developments in EV loading, surface functionalization, and strategies for increasing the efficiency of delivering CRISPR-Cas9 to tissues, organs, and cells for eventual use in gene therapies.

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 categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.287
Teacher spread0.261 · 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