Evidence map›Paper›PMID 34789146›Full record

ArticleBMC genomics2021

Evaluation validation of a qPCR curve analysis method and conventional approaches.

Yashu Zhang, Hongping Li, Shucheng Shang, Shuoyu Meng, Ting Lin, Yanhui Zhang, Haixing Liu

Abstract read
In one paragraph

Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Biology methods & protocols · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Detection of theOpen veterinary journal · 2024
    Article
  8. Introducing the fBMC bioinformatics · 2024
    Article
  9. Article
  10. Article
  11. 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

7 authors.

Yashu ZhangDepartment of Information Science and Engineering, Ocean University of China, Qingdao, China.ORCID http://orcid.org/0000-0002-7448-8809
Hongping LiDepartment of Information Science and Engineering, Ocean University of China, Qingdao, China. lhp@ouc.edu.cn.
Shucheng ShangDepartment of Information Science and Engineering, Ocean University of China, Qingdao, China.
Shuoyu MengDepartment of Information Science and Engineering, Ocean University of China, Qingdao, China.
Ting LinApexbio Biotechnology (Suzhou) Co., Ltd, Suzhou, China.
Yanhui ZhangApexbio Biotechnology (Suzhou) Co., Ltd, Suzhou, China.
Haixing LiuFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao, China.

Funding

National Natural Science Foundation of China U1806205
6 · The paper itself

Abstract

backgroundReverse Transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and reliable method for mRNA quantification and rapid analysis of gene expression from a large number of starting templates. It is based on the statistical significance of the beginning of exponential phase in real-time PCR kinetics, reflecting quantitative cycle of the initial target quantity and the efficiency of the PCR reaction (the fold increase of product per cycle).

resultsWe used the large clinical biomarker dataset and 94-replicates-4-dilutions set which was published previously as research tools, then proposed a new qPCR curve analysis method--C

conclusionsThe results show that C

Indexed as

Gene Expression ProfilingGenes, mycGene ExpressionHumansReal-Time Polymerase Chain ReactionReverse Transcriptase Polymerase Chain ReactionRNA, MessengerRNA, MessengerCqMANCurve analysis methodPerformance indicatorsReverse transcription quantitative polymerase chain reaction

Identifiers

PMID34789146
PMCPMC8596907

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.