Evidence map›Paper›PMID 38311323›Full record

ReviewJournal of veterinary science2024

Outlook on genome editing application to cattle.

Gyeong-Min Gim, Goo Jang

Abstract readReview
In one paragraph

Review in Journal of veterinary science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

2 authors.

Gyeong-Min GimLARTBio Inco, Seoul 06221, Korea.ORCID https://orcid.org/0000-0002-9352-094X
Goo JangLARTBio Inco, Seoul 06221, Korea.ORCID https://orcid.org/0000-0003-3804-1871

Funding

Seoul National University 550-20220044SRC program NRF-2021R1A5A1 033157
6 · The paper itself

Abstract

In livestock industry, there is growing interest in methods to increase the production efficiency of livestock to address food shortages, given the increasing global population. With the advancements in gene engineering technology, it is a valuable tool and has been intensively utilized in research specifically focused on human disease. In historically, this technology has been used with livestock to create human disease models or to produce recombinant proteins from their byproducts. However, in recent years, utilizing gene editing technology, cattle with identified genes related to productivity can be edited, thereby enhancing productivity in response to climate change or specific disease instead of producing recombinant proteins. Furthermore, with the advancement in the efficiency of gene editing, it has become possible to edit multiple genes simultaneously. This cattle breed improvement has been achieved by discovering the genes through the comprehensive analysis of the entire genome of cattle. The cattle industry has been able to address gene bottlenecks that were previously impossible through conventional breeding systems. This review concludes that gene editing is necessary to expand the cattle industry, improving productivity in the future. Additionally, the enhancement of cattle through gene editing is expected to contribute to addressing environmental challenges associated with the cattle industry. Further research and development in gene editing, coupled with genomic analysis technologies, will significantly contribute to solving issues that conventional breeding systems have not been able to address.

Indexed as

Gene EditingGenetic EngineeringAnimalsBreedingCattleGenomeHumansLivestockRecombinant ProteinsRecombinant ProteinsBreedingcattlegenetically engineering cattlegenetic engineeringsomatic cell nuclear transfer

Identifiers

PMID38311323
PMCPMC10839183

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.