Evidence map›Paper›PMID 34440168›Full record

ReviewBiomedicines2021

Non-Coding RNA-Based Biosensors for Early Detection of Liver Cancer.

Sedigheh Falahi, Hossain-Ali Rafiee-Pour, Mashaalah Zarejousheghani, Parvaneh Rahimi, Yvonne Joseph

Abstract readReview
In one paragraph

Review in Biomedicines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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.

Sedigheh FalahiFaculty of Materials Science and Materials Technology, Institute of Electronic and Sensor Materials, Technische Universität Bergakademie Freiberg, 09599 Freiberg, Germany.
Hossain-Ali Rafiee-PourDepartment of Cell and Molecular Biology, Faculty of Chemistry, University of Kashan, Kashan 8731753153, Iran.ORCID 0000-0002-0799-6547
Mashaalah ZarejousheghaniFaculty of Materials Science and Materials Technology, Institute of Electronic and Sensor Materials, Technische Universität Bergakademie Freiberg, 09599 Freiberg, Germany.ORCID 0000-0002-5346-8067
Parvaneh RahimiFaculty of Materials Science and Materials Technology, Institute of Electronic and Sensor Materials, Technische Universität Bergakademie Freiberg, 09599 Freiberg, Germany.ORCID 0000-0002-8858-5407
Yvonne JosephFaculty of Materials Science and Materials Technology, Institute of Electronic and Sensor Materials, Technische Universität Bergakademie Freiberg, 09599 Freiberg, Germany.ORCID 0000-0003-0697-9646

Funding

European Social Fund 100380877
6 · The paper itself

Abstract

Primary liver cancer is an aggressive, lethal malignancy that ranks as the fourth leading cause of cancer-related death worldwide. Its 5-year mortality rate is estimated to be more than 95%. This significant low survival rate is due to poor diagnosis, which can be referred to as the lack of sufficient and early-stage detection methods. Many liver cancer-associated non-coding RNAs (ncRNAs) have been extensively examined to serve as promising biomarkers for precise diagnostics, prognostics, and the evaluation of the therapeutic progress. For the simple, rapid, and selective ncRNA detection, various nanomaterial-enhanced biosensors have been developed based on electrochemical, optical, and electromechanical detection methods. This review presents ncRNAs as the potential biomarkers for the early-stage diagnosis of liver cancer. Moreover, a comprehensive overview of recent developments in nanobiosensors for liver cancer-related ncRNA detection is provided.

Indexed as

biosensorselectrochemicalelectromechanicalliver cancernanomaterialsnon-coding RNAs (ncRNAs)optical

Identifiers

PMID34440168
PMCPMC8391662

What OpenQuestion holds

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LicenceCC BY
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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.