PCR-based Detection of Genetically Modified Soybean at a Grain Receiving Port in Iran
Bibliographic record
Abstract
The detection of Genetically Modified (GM) organisms is becoming a legal necessity. This study was carried out to detect genetically modified events in soybeans imported into Iran using simplex and multiplex PCR. Therefore, five samples of imported soybean were obtained from Bandar Imam Khomeini customs. Modified CTAB method was used to extract DNA from soybean seeds. The result indicates that the modified method is suitable for DNA extraction from soybean seeds and probably can be used for other oilseeds. Using specific primers for CaMV 35S promoter, NOS terminator and epsps gene PCR reactions were performed. In this study soybean lectin gene was used as internal control. The results revealed that soybean samples imported from Canada and Paraguay were genetically modified and they had CaMV 35S promoter, NOS terminator and epsps gene in their genomes. The result of simplex PCR was the same as multiplex PCR, but multiplex PCR detected the GM soybeans very quickly and in a cost-saving and timeconsuming way. Based on PCR analysis using GM soybean event-specific primers, it is suggested that the soybean plants may be GTS 40-3-2. No fragment was amplified when the DNA of US or Non-GM soybeans were used as template in the PCR reaction. This is the first report that shows GM soybeans imported to Iran without use of the GMO label in the shipment's documentation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".