Evidence map›Paper›PMID 33740891›Full record

ArticleBMC genomics2021

Differential microRNAs expression profiles in liver from three different lifestyle modification mice models.

Huan Gong, Ming Zhang, Yiwen Han, Ying Zhang, Jing Pang, Yanyang Zhao, Beidong Chen, Wei Wu, Ruomei Qi, Tiemei Zhang

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 58% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 3 institutions in 1 country.

Huan GongThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China. gonghuan3861@bjhmoh.cn.ORCID http://orcid.org/0000-0002-3942-8958
Ming ZhangChinese People's Liberation Army General Hospital, Beijing, People's Republic of China.
Yiwen HanThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Ying ZhangSchool of Sport Science, Beijing Sport University, Beijing, People's Republic of China.
Jing PangThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Yanyang ZhaoThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Beidong ChenThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Wei WuThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Ruomei QiThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Tiemei ZhangThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China. tmzhang126@126.com.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNBeijing Sport University · CNChinese People's Liberation Army · CN

Funding

BEIJING HOSPITAL BJH-2019-185National Natural Science Foundation of China 81300693Research Special Fund for Public Welfare Industry of Health 201302008
6 · The paper itself

Abstract

backgroundMicroRNAs play an important role in many fundamental biological and pathological processes. Defining the microRNAs profile underlying the processes by beneficial and detrimental lifestyles, including caloric restriction (CR), exercise and high-fat diet (HF), is necessary for understanding both normal physiology and the pathogenesis of metabolic disease. We used the microarray to detect microRNAs expression in livers from CR, EX and HF mice models. After predicted potential target genes of differentially expressed microRNAs with four algorithms, we applied GO and KEGG to analyze the function of predicted microRNA targets.

resultsWe describe the overall microRNAs expression pattern, and identified 84 differentially expressed microRNAs changed by one or two or even all the three lifestyle modifications. The common and different enriched categories of gene function and main biochemical and signal transduction pathways were presented.

conclusionsWe provided for the first time a comprehensive and thorough comparison of microRNAs expression profiles in liver among these lifestyle modifications. With this knowledge, our findings provide us with an overall vision of microRNAs in the molecular impact of lifestyle on health as well as useful clues for future and thorough research of the role of microRNAs.

Indexed as

LiverMicroRNAsAnimalsDiet, High-FatGene Expression ProfilingLife StyleMiceSignal TransductionMicroRNAsCaloric restrictionExerciseHigh-fat dietLifestyle modificationsmicroRNA

Identifiers

PMID33740891
PMCPMC7977600
OpenAlexW3138751014

What Socratic 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 Socratic graph.