Effects of quantum dot zinc oxide, nano zinc oxide and zinc oxide on genes expression and aflatoxine production due to Aspergillus flavus ATCC50041
Bibliographic record
Abstract
Aflatoxin, which is mainly produced by Aspergillus flavus and Aspergillus parasiticus, is a severe problem and threat in the fields of medicine and agriculture and is classified as the first class human carcinogen due to its carcinogenic, mutagenic, teratogenic, hepatotoxicity, and immune system defects. This toxin contaminates many agricultural products around the world. Considering the high toxicity of aflatoxin and the destructive effects of this metabolite, achieving low-risk and cost-effective methods to control toxin production is of great importance. In this study, the effects of different concentrations of quantum dot zinc oxide, nano zinc oxide, and zinc oxide on the expression of important key genes (aflR, aflP, aflM, aflD) in the gene cluster of aflatoxin production and also the amount of aflatoxin production (B1, B2) in Aspergillus flavus was studied. The fungus was cultured in different concentrations of quantum dot zinc oxide, nano zinc oxide, and zinc oxide (2000, 4000, 6000 ppm) for 3 days at 30 ◦C. The results showed that quantum dot zinc oxide significantly reduced toxin production and gene expression compared to other groups, in which the concentration of aflatoxin was decreased by increasing quantum dot zinc oxide. Regarding the obtained results, nano zinc oxide was effective just at 6000 ppm, and zinc oxide is suggested because nano zinc oxide and zinc oxide cannot completely dissolve in water. Hence, nano zinc oxide and zinc oxide showed weak activity in reducing aflatoxin concentration. All three agents under study significantly caused low gene expression, but quantum-dot zinc oxide showed more activity than the others. The formulation of highly active quantum dot zinc oxide to reduce aflatoxin may be a potential feed additive for the future management of mycotoxins.
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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 | 0.000 | 0.000 |
| 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".