Evidence map›Paper›PMID 28367094›Full record

ReviewInternational journal of biological sciences2017

LncRNAs: From Basic Research to Medical Application.

Tao Wu, Yantao Du

Open access · goldAbstract readReview
In one paragraph

Review in International journal of biological sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
83citing papers in PubMed, 1 pooled it
4.5field-weighted citation impact, top 4% 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

83 citing papers in PubMed, 1 synthesis or guideline pooled it, 149 citations in OpenAlex.

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23 more citing papers are in PubMed but not listed here.

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 at 1 institution in 1 country.

Tao WuCardiovascular Department, The Affiliated Hospital of Medical College, Ningbo University, No.247, Renmin Road, Jiangbei District, Ningbo, China.
Yantao DuNingbo Institute of Medical Science, No.42-46, Yangshan Road, Jiangbei District, Ningbo, China.
Ningbo University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review aimed to summarize the current research contents about long noncoding RNAs (lncRNAs) and some related lncRNAs as molecular biomarkers or therapy strategies in human cancer and cardiovascular diseases. Following the development of various kinds of sequencing technologies, lncRNAs have become one of the most unknown areas that need to be explored. First, the definition and classification of lncRNAs were constantly amended and supplemented because of their complexity and diversity. Second, several methods and strategies have been developed to study the characteristic of lncRNAs, including new species identifications, subcellular localization, gain or loss of function, molecular interaction, and bioinformatics analysis. Third, based on the present results from basic researches, the working mechanisms of lncRNAs were proved to be different forms of interactions involving DNAs, RNAs, and proteins. Fourth, lncRNA can play different important roles during the embryogenesis and organ differentiations. Finally, because of the tissue-specific expression of lncRNAs, they could be used as biomarkers or therapy targets and effectively applied in different kinds of diseases, such as human cancer and cardiovascular diseases.

Indexed as

AnimalsBiomarkersBiomedical ResearchCardiovascular DiseasesHumansNeoplasmsRNA, Long NoncodingBiomarkersRNA, Long Noncodingcancercardiovascular diseaseslncRNAmolecular biomarkertherapy strategy

Identifiers

PMID28367094
PMCPMC5370437
OpenAlexW2591879072

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.