Evidence map›Paper›PMID 26357624›Full record

ReviewJournal of clinical and translational hepatology2014

MicroRNAs in Drug-induced Liver Injury.

Li-Min Li, Dong Wang, Ke Zen

Abstract readReview
In one paragraph

Review in Journal of clinical and translational hepatology, 2014. 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
–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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Review
  9. Review
  10. MicroRNAs as Biomarkers for Liver Disease and Hepatocellular Carcinoma.International journal of molecular sciences · 2016
    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

3 authors.

Li-Min LiState Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China.
Dong WangState Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China.
Ke ZenState Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug-induced liver injury (DILI) is a leading cause of acute liver failure, and a major reason for the recall of marketed drugs. Detection of potential liver injury is a challenge for clinical management and preclinical drug safety studies, as well as a great obstacle to the development of new, effective and safe drugs. Currently, serum levels of alanine and aspartate aminotransferases are the gold standard for evaluating liver injury. However, these levels are assessed by nonspecific, insensitive, and non-predictive tests, and often result in false-positive results. Therefore, there is an urgent need for better DILI biomarkers to guide risk assessment and patient management. The discovery of microRNAs (miRNAs) as a new class of gene expression regulators has triggered an explosion of research, particularly on the measurement of miRNAs in various body fluids as biomarkers for many human diseases. The properties of miRNA-based biomarkers, such as tissue specificity and high stability and sensitivity, suggest they could be used as novel, minimally invasive and stable DILI biomarkers. In the current review, we summarize recent progress concerning the role of miRNAs in diagnosing and monitoring both clinical and preclinical DILI, and discuss the main advantages and challenges of miRNAs as novel DILI biomarkers.

Indexed as

BiomarkerDiagnosisDrug-induced liver injury (DILI)MicroRNA

Identifiers

PMID26357624
PMCPMC4521241

What OpenQuestion holds

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LicenceCC BY-NC
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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.