Evidence map›Paper›PMID 33936161›Full record

ArticleFrontiers in genetics2021

Tao Wang, Yuanyuan Guo, Shengwei Liu, Chaoxin Zhang, Tongyan Cui, Kun Ding, Peng Wang, Xibiao Wang, Zhipeng Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

10 citing papers in PubMed, 12 citations in OpenAlex.

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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 1 country.

Tao WangCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
Yuanyuan GuoCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
Shengwei LiuCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
Chaoxin ZhangCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
Tongyan CuiCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
Kun DingCollege of Computer Science and Technology, Inner Mongolia Normal University, Hohhot, China.
Peng WangHeiLongJiang Provincial Husbandry Department, Harbin, China.
Xibiao WangCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
Zhipeng WangCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
Northeast Agricultural University · CNEducation Department of Heilongjiang Province · CNInner Mongolia Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tibetan pigs are native mammalian species on the Tibetan Plateau that have evolved distinct physiological traits that allow them to tolerate high-altitude hypoxic environments. However, the genetic mechanism underlying this adaptation remains elusive. Here, based on multitissue transcriptional data from high-altitude Tibetan pigs and low-altitude Rongchang pigs, we performed a weighted correlation network analysis (WGCNA) and identified key modules related to these tissues. Complex network analysis and bioinformatics analysis were integrated to identify key genes and three-node network motifs. We found that among the six tissues (muscle, liver, heart, spleen, kidneys, and lungs), lung tissue may be the key organs for Tibetan pigs to adapt to hypoxic environment. In the lung tissue of Tibetan pigs, we identified

Indexed as

gene networkhypoxia adaptationmultitissueTibetan pigtranscriptome

Identifiers

PMID33936161
PMCPMC8082500
OpenAlexW3155875635

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.