Reliable Detection and Identification of Genetically Modified Maize, Soybean, and Canola by Multiplex PCR Analysis
Bibliographic record
Abstract
Multiplex PCR procedures were developed for simultaneously detecting multiple target sequences in genetically modified (GM) soybean (Roundup Ready), maize (event 176, Bt11, Mon810, T14/25), and canola (GT73, HCN92/28, MS8/RF3, Oxy 235). Internal control targets (invertase gene in corn, lectin and beta-actin genes in soybean, and cruciferin gene in canola) were included as appropriate to assess the efficiency of all reactions, thereby eliminating any false negatives. Primer combinations that allowed the identification of specific lines were used. In one system of identification, simultaneous amplification profiling (SAP), rather than target specific detection, was used for the identification of four GM maize lines. SAP is simple and has the potential to identify both approved and nonapproved GM lines. The template concentration was identified as a critical factor affecting efficient multiplex PCRs. In canola, 75 ng of DNA template was more effective than 50 ng of DNA for the simultaneous amplification of all targets in a reaction volume of 25 microL. Reliable identification of GM canola was achieved at a DNA concentration of 3 ng/microL, and at 0.1% for GM soybean, indicating high levels of sensitivity. Nonspecific amplification was utilized in this study as a tool for specific and reliable identification of one line of GM maize. The primer cry1A 4-3' (antisense primer) recognizes two sites on the DNA template extracted from GM transgenic maize containing event 176 (European corn borer resistant), resulting in the amplification of products of 152 bp (expected) and 485 bp (unexpected). The latter fragment was sequenced and confirmed to be Cry1A specific. The systems described herein represent simple, accurate, and sensitive GMO detection methods in which only one reaction is necessary to detect multiple GM target sequences that can be reliably used for the identification of specific lines of GMOs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".