Evidence map›Paper›PMID 29314610›Full record

ReviewJournal of cellular and molecular medicine2018

Emerging role of Twist1 in fibrotic diseases.

Xiaoxuan Ning, Kun Zhang, Qingfeng Wu, Minna Liu, Shiren Sun

Open access · goldAbstract readReview
In one paragraph

Review in Journal of cellular and molecular medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
3.3field-weighted citation impact, top 6% 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

38 citing papers in PubMed, 80 citations in OpenAlex.

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  9. Ginsenoside RbPharmaceuticals (Basel, Switzerland) · 2025
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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

5 authors at 2 institutions in 1 country.

Xiaoxuan NingDepartment of Geriatrics, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Kun ZhangCollege of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi, China.
Qingfeng WuDepartment of Geriatrics, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Minna LiuDepartment of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Shiren SunDepartment of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.ORCID 0000-0001-5322-1454
Xijing Hospital · CNShaanxi Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epithelial-mesenchymal transition (EMT) is a pathological process that occurs in a variety of diseases, including organ fibrosis. Twist1, a basic helix-loop-helix transcription factor, is involved in EMT and plays significant roles in various fibrotic diseases. Suppression of the EMT process represents a promising approach for the treatment of fibrotic diseases. In this review, we discuss the roles and the underlying molecular mechanisms of Twist1 in fibrotic diseases, including those affecting kidney, lung, skin, oral submucosa and other tissues. We aim at providing new insight into the pathogenesis of various fibrotic diseases and facilitating the development of novel diagnostic and therapeutic methods for their treatment.

Indexed as

Epithelial-Mesenchymal TransitionFibrosisGene Expression RegulationHumansHypoxiaHypoxia-Inducible Factor 1, alpha SubunitKidneyLungNuclear ProteinsPulmonary FibrosisSignal TransductionSkinSnail Family Transcription FactorsTwist-Related Protein 1HIF1A protein, humanHypoxia-Inducible Factor 1, alpha SubunitNuclear ProteinsSnai1 protein, mouseSnail Family Transcription FactorsTWIST1 protein, humanTwist-Related Protein 1epithelial-mesenchymal transitionfibrotic diseasesTwist1

Identifiers

PMID29314610
PMCPMC5824384
OpenAlexW2784254353

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.