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Record W4289550496 · doi:10.1186/s12951-022-01570-y

Stimuli-responsive nanoformulations for CRISPR-Cas9 genome editing

2022· review· en· W4289550496 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

VenueJournal of Nanobiotechnology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill University Health CentreMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCRISPRGenome editingCas9Computational biologyComputer scienceBiologyGeneticsGene

Abstract

fetched live from OpenAlex

The CRISPR-Cas9 technology has changed the landscape of genome editing and has demonstrated extraordinary potential for treating otherwise incurable diseases. Engineering strategies to enable efficient intracellular delivery of CRISPR-Cas9 components has been a central theme for broadening the impact of the CRISPR-Cas9 technology. Various non-viral delivery systems for CRISPR-Cas9 have been investigated given their favorable safety profiles over viral systems. Many recent efforts have been focused on the development of stimuli-responsive non-viral CRISPR-Cas9 delivery systems, with the goal of achieving efficient and precise genome editing. Stimuli-responsive nanoplatforms are capable of sensing and responding to particular triggers, such as innate biological cues and external stimuli, for controlled CRISPR-Cas9 genome editing. In this Review, we overview the recent advances in stimuli-responsive nanoformulations for CRISPR-Cas9 delivery, highlight the rationale of stimuli and formulation designs, and summarize their biomedical applications.

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)0.001
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 score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.038
GPT teacher head0.387
Teacher spread0.349 · 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