Evidence map›Paper›PMID 31194178›Full record

ArticleBiomolecular detection and quantification2019

qPCR data analysis: Better results through iconoclasm.

Joel Tellinghuisen, Andrej-Nikolai Spiess

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomolecular detection and quantification, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. 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

2 authors.

Joel TellinghuisenDepartment of Chemistry, Vanderbilt University Nashville, TN, 37235, USA.
Andrej-Nikolai SpiessDepartment of Andrology, University Hospital Hamburg-Eppendorf, Hamburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The standard approach for quantitative estimation of genetic materials with qPCR is calibration with known concentrations for the target substance, in which estimates of the quantification cycle (

Indexed as

CalibrationChi-squareCq, quantification cycleCt, threshold cycle, where y = yqCy0, intersection of a straight line tangent to the curve at the FDM with the baseline-corrected x-axisData analysisE, amplification efficiencyFDM and SDM, cycles where y reaches its maximal first and second derivatives, respectivelyLS, least squaresN0, initial number of target molecules in sampleqPCRqPCR, quantitative polymerase chain reactionSD, standard deviationSE, parameter standard errorS, sum of weighted, squared residuals (= "Chisq" in KaleidaGraph fit results, = Χ2 when wi = 1/σi2)Statistical errorsWeighted least squareswi, statistical weight for ith data pointy and y0, fluorescence signal above baseline at cycle x and at cycle 0yq, signal at x = Cqν, statistical degrees of freedom, = # of data points - # of adjustable parametersσ2a and σ, variance and standard deviationΧ2, chi-square

Identifiers

PMID31194178
PMCPMC6554483

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.