Evidence map›Paper›PMID 30613384›Full record

ReviewF1000Research2018

Making gene editing a therapeutic reality.

Irina Conboy, Niren Murthy, Jessy Etienne, Zachery Robinson

Abstract readReview
In one paragraph

Review in F1000Research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Review
  6. Novel vectors and approaches for gene therapy in liver diseases.JHEP reports : innovation in hepatology · 2021
    Review
  7. Article
  8. Effective restoration of dystrophin expression in iPSCComputational and structural biotechnology journal · 2020
    Article
  9. Is Alzheimer's Disease a Liver Disease of the Brain?Journal of Alzheimer's disease : JAD · 2020
    Review
  10. Review
  11. CRISPR/Cas Applications in Myotonic Dystrophy: Expanding Opportunities.International journal of molecular sciences · 2019
    Review
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Irina ConboyBioengineering, UC Berkeley, Berkeley, CA, 94720, USA.ORCID 0000-0002-4276-0644
Niren MurthyBioengineering, UC Berkeley, Berkeley, CA, 94720, USA.
Jessy EtienneBioengineering, UC Berkeley, Berkeley, CA, 94720, USA.
Zachery RobinsonBioengineering, UC Berkeley, Berkeley, CA, 94720, USA.

Funding

New Generation Blood Exchange Devices for Enhancing Tissue Regeneration and HealthR01HL139605 · NHLBI · UNIVERSITY OF CALIFORNIA BERKELEY · PI ARAN, KIANA, CONBOY, IRINA M · 2018 to 2021
$1.6M
Treatment of Duchenne Muscular Dystrophy with Cas9 Protein Complexed to Gold NanoparticlesR01EB023776 · NIBIB · UNIVERSITY OF CALIFORNIA BERKELEY · PI CONBOY, IRINA M, MURTHY, NIREN · 2017 to 2020
$1.4M
NHLBI NIH HHS R01 HL139605NIBIB NIH HHS R01 EB023776
6 · The paper itself

Abstract

This review discusses current bottlenecks in making CRISPR-Cas9-mediated genome editing a therapeutic reality and it outlines recent strategies that aim to overcome these hurdles as well as the scope of current clinical trials that pioneer the medical translation of CRISPR-Cas9. Additionally, this review outlines the specifics of disease-modifying gene editing in recessive versus dominant genetic diseases with the focus on genetic myopathies that are exemplified by Duchenne muscular dystrophy and myotonic dystrophies.

Indexed as

AnimalsCRISPR-Cas SystemsGene EditingGenetic TherapyHumansMuscular Dystrophy, DuchenneMyotonic DystrophyAAVAlpha1 Antitrypsin deficiencyClinical trialsCRISPRDMDDNA damageHDRMyotonic DystrophynanoparticlesNHEJ

Identifiers

PMID30613384
PMCPMC6305220

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.