Evidence map›Paper›PMID 34414222›Full record

ArticleFrontiers in veterinary science2021

Identification of Photoperiod-Induced LncRNAs and mRNAs in Pituitary Pars Tuberalis of Sheep.

Qing Xia, Mingxing Chu, Xiaoyun He, Qiuyue Liu, Xiaosheng Zhang, Jinlong Zhang, Xiaofei Guo, Ran Di

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in veterinary science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  6. Characterization of theAnimal biotechnology · 2023
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  7. International journal of molecular sciences · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Qing XiaKey Laboratory of Animal Genetics and Breeding and Reproduction of the Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.
Mingxing ChuKey Laboratory of Animal Genetics and Breeding and Reproduction of the Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.
Xiaoyun HeKey Laboratory of Animal Genetics and Breeding and Reproduction of the Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.
Qiuyue LiuTianjin Institute of Animal Sciences, Tianjin, China.
Xiaosheng ZhangTianjin Institute of Animal Sciences, Tianjin, China
Jinlong ZhangTianjin Institute of Animal Sciences, Tianjin, China
Xiaofei GuoTianjin Institute of Animal Sciences, Tianjin, China
Ran DiKey Laboratory of Animal Genetics and Breeding and Reproduction of the Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pituitary pars tuberalis (PT) is the regulating center of seasonal reproduction, which can sense the melatonin signal and eventually cause downstream changes of GnRH secretion through TSHβ. Recently, lncRNAs have been identified in animal reproductive-related tissues, and they play important roles in reproductive regulation. Therefore, in this study, we expect to identify photoperiod-induced lncRNAs and genes in pituitary PT of sheep by comparison of expression profiles between short photoperiod (SP) and long photoperiod (LP). Through RNA-Seq, a total of 55,472 lncRNAs were identified in pituitary PT of Sunite ewes. The number of differentially expressed (DE) genes and lncRNAs between SP and LP increased gradually with the extension of LP (from LP7 to LP42). The notable LP-induced candidate genes included

Indexed as

lncRNAsmRNAsphotoperiodpituitary pars tuberalissheep

Identifiers

PMID34414222
PMCPMC8369575

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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.