Evidence map›Paper›PMID 36864312›Full record

ReviewAnalytical and bioanalytical chemistry2023

Signal differentiation models for multiple microRNA detection: a critical review.

Yue Sun, Yinan Wang, Luo Fang, Tailin Xu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Analytical and bioanalytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

4 authors.

Yue SunDepartment of Pharmacy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, People's Republic of China.
Yinan WangSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518060, People's Republic of China.
Luo FangDepartment of Pharmacy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, People's Republic of China.
Tailin XuSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518060, People's Republic of China. xutailin@szu.edu.cn.ORCID http://orcid.org/0000-0003-4037-2856

Funding

National Natural Science Foundation of China 81903576
6 · The paper itself

Abstract

MicroRNAs (miRNAs) are a class of small, single-stranded non-coding RNAs which have critical functions in various biological processes. Increasing evidence suggested that abnormal miRNA expression was closely related to many human diseases, and they are projected to be very promising biomarkers for non-invasive diagnosis. Multiplex detection of aberrant miRNAs has great advantages including improved detection efficiency and enhanced diagnostic precision. Traditional miRNA detection methods do not meet the requirements of high sensitivity or multiplexing. Some new techniques have opened novel paths to solve analytical challenges of multiple miRNA detection. Herein, we give a critical overview of the current multiplex strategies for the simultaneous detection of miRNAs from the perspective of two different signal differentiation models, including label differentiation and space differentiation. Meanwhile, recent advances of signal amplification strategies integrated into multiplex miRNA methods are also discussed. We hope this review provides the reader with future perspectives on multiplex miRNA strategies in biochemical research and clinical diagnostics.

Indexed as

MicroRNAsBiomarkersHumansBiomarkersMicroRNAsMultiple microRNAsSignal amplificationSignal differentiationSimultaneous detection

Identifiers

PMID36864312

What OpenQuestion holds

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Registered trials

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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.