Evidence map›Paper›PMID 33385016›Full record

ArticleFrontiers in veterinary science2020

Longissimus Dorsi Muscle Transcriptomic Analysis of Simmental and Chinese Native Cattle Differing in Meat Quality.

Xiangren Meng, Ziwu Gao, Yusheng Liang, Chenglong Zhang, Zhi Chen, Yongjiang Mao, Bizhi Huang, Kaixing Kui, Zhangping Yang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in veterinary science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
4.0field-weighted citation impact, top 5% of its field
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

13 citing papers in PubMed, 21 citations in OpenAlex.

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  9. Evaluation of theAnimals : an open access journal from MDPI · 2023
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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

9 authors at 3 institutions in 2 countries.

Xiangren MengSchool of Tourism and Culinary Science, Yangzhou University, Yangzhou, China.
Ziwu GaoSchool of Tourism and Culinary Science, Yangzhou University, Yangzhou, China.
Yusheng LiangMammalian Nutrition Physiology Genomics, Department of Animal Sciences and Division of Nutritional Sciences, University of Illinois, Urbana, IL, United States.
Chenglong ZhangCollege of Animal Science and Technology, Yangzhou University, Yangzhou, China.
Zhi ChenCollege of Animal Science and Technology, Yangzhou University, Yangzhou, China.
Yongjiang MaoCollege of Animal Science and Technology, Yangzhou University, Yangzhou, China.
Bizhi HuangAcademy of Grassland and Animal Science, Yunnan, China.
Kaixing KuiAcademy of Grassland and Animal Science, Yunnan, China.
Zhangping YangCollege of Animal Science and Technology, Yangzhou University, Yangzhou, China.
Yangzhou University · CNAcademe of Grassland And Animal Science · CNUniversity of Illinois Urbana-Champaign · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of economy, the demand for beef, with regard to quantity and quality, by consumers has been increasing in China. Chinese native cattle are characterized by their abundant genetic resources, unique origins, large breeding stocks, and robust environmental adaptability. Thus, to explore the genetic mechanisms on regulating meat quality in Chinese native cattle is of great importance to satisfy increased requirements for beef production. In this study, we investigated three breeds of cattle, namely Yunling, Wenshan, and Simmental, at the age of 12 months. Animals were classified into three groups (

Indexed as

Chinese native cattlecooked degreemeat qualityripeningSimmental

Identifiers

PMID33385016
PMCPMC7770222
OpenAlexW3110898824

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.