Evidence map›Paper›PMID 41936134›Full record

ArticleGM crops & food2026

Can GM soybean be reliably quantified after screening? A risk-based approach for optimizing GMO testing workflow.

Davide La Rocca, Katia Spinella, Pietro Franceschi, Daniela Verginelli, Sara Ciuffa, Marisa Misto, Cinzia Quarchioni, Cristiana Fusco, Stefania Peddis, Pamela Bonini and 2 more

Abstract read
In one paragraph

Article in GM crops & food, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Davide La RoccaNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.
Katia SpinellaNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.ORCID 0000-0003-3839-7974
Pietro FranceschiResearch and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy.
Daniela VerginelliNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.
Sara CiuffaNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.
Marisa MistoNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.
Cinzia QuarchioniNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.
Cristiana FuscoNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.
Stefania PeddisNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.
Pamela BoniniNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.
Lorella PeroniNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.
Ugo MarchesiNational Reference Laboratory for GM Food and Feed, GMO Unit, Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "Mariano Aleandri", Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

GMO testing laboratories operating within the current European Union (EU) regulatory framework governing the presence of genetically modified organisms (GMOs) in food and feed (EC Reg. 1829/2003 and EC Reg. 1830/2003) perform a stepwise workflow from DNA extraction to quantification of genetically modified events. A very valuable intermediate step in guiding and optimizing this workflow is the screening phase, where any positives require the laboratory to proceed to the subsequent identification and quantification steps. Very often, however, samples with low GM content result positive at screening but then the GM component is not quantifiable, wasting time and resources. In order to overcome this issue, in this study a statistical framework was developed to predict the presence of soybean GM events based on the difference between the quantification cycle (Cq, also known as threshold cycle (Ct) of the Real time PCR technique) values observed from screening elements as cauliflower mosaic virus 35S promoter (P35S), the nopaline synthase terminator (T-nos), the 5-enolpyruvylshikimate -3-phosphate synthase gene (CP4 epsps) and

Indexed as

Glycine maxPlants, Genetically ModifiedReal-Time Polymerase Chain ReactionWorkflowCut–off valueGMOqPCRscreening elements

Identifiers

PMID41936134
PMCPMC13051587

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.