Application of PCR in the Detection of Aflatoxinogenic and Non-aflatoxinogenic Strains of Aspergillus Flavus Group of Cattle Feed Isolated in Iran
Why this work is in the frame
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Bibliographic record
Abstract
<p class="1Body">Aflatoxins are among the most important Mycotoxins that are mainly produced by various <em>Aspergillus </em>species, specially <em>Aspergillus flavus</em> and <em>Aspergillus parasiticus</em>. 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 <em>Aspergillus</em> 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 <em>Aspergillus Flavus</em> using PCR and TLC and the expression of five Aflatoxin biosynthesis genes including <em>aflD (nor-1)</em>, <em>aflP( omtA)</em>, <em>aflO (omtB)</em>, <em>aflQ(ordA)</em>, <em>aflR</em> in 40 strains was investigated using PCR. In this study, a number of 40 <em>Aspergillus flavus</em> 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 <em>Aspergillus</em> 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 <em>Aspergillus flavus</em> 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.</p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it