Application of PCR in the Detection of Aflatoxinogenic and Non-aflatoxinogenic Strains of Aspergillus Flavus Group of Cattle Feed Isolated in Iran
Bibliographic record
Abstract
Aflatoxins are among the most important Mycotoxins that are mainly produced by various Aspergillus species, specially Aspergillus flavus and Aspergillus parasiticus. Aflatoxins are carcinogenetic and immunosuppressive, so that can lead to acute liver damage, cirrhosis of the liver and hepatocarcinoma induction. Consuming the feed contaminated by Aspergillus puts humans and animals under the danger of Aflatoxins that are considered as an important threats for human and animal health. The purpose of the present study was to make distinction between Aflatoxinogenetic and non-Aflatoxinogenetic strains and Aspergillus Flavus using PCR and TLC and the expression of five Aflatoxin biosynthesis genes including aflD (nor-1), aflP( omtA), aflO (omtB), aflQ(ordA), aflR in 40 strains was investigated using PCR. In this study, a number of 40 Aspergillus flavus strains from 67 species of cattle feed from 21 industrial warehouses of various areas of Tehran and Alborz were used. After isolation and culture in exclusive environment of yeast extract of sucrose agar, the isolated Aspergillus strains were investigated by microscopic and macroscopic methods. In order to make distinction between Aflatoxinogenetic and non-Aflatoxinogenetic strains, PCR method and TLC techniques were used. The results showed that only 7 strains (1, 3, 5, 14, 22, 34, and 38) were Aflatoxin-producers fungi and the rest 33 samples were non-Afatoxin-producers fungi. Since Aspergillus flavus is the main contaminator of cattle feed, there is a need to develop a simple, rapid and sensitive method to identify Aflatoxigenetic fungi, particularly between Aflatoxinogenetic and non-Aflatoxinogenetic strains of AF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".