Development of an ELISA-Based Method for Testing Aflatoxigenicity and Aflatoxigenic Variability among <i>Aspergillus</i> species in Culture
Bibliographic record
Abstract
Abstract Aflatoxins contaminate foodstuff posing a severe threat to human health because chronic exposure is linked to liver cancer while acute exposure may cause death. Therefore, it is of interest to reduce the contamination of crops by aflatoxins in the field and post-harvest. Among the current technologies being developed is the deployment of non-aflatoxigenic strains of Aspergillus species to competitively exclude aflatoxigenic conspecifics from crops in the field thereby curtailing aflatoxin production by the former. The success in this endeavor makes the non-aflatoxigenic fungi good candidates for biological control programs. However, the current techniques for segregating non-aflatoxigenic from aflatoxigenic fungi suffer two main drawbacks: they are based on morphological and chemical tests with a combination of visual color changes detected in a culture plate which suffer some degree of inaccuracy. Secondly, the existing methods are incapable of accurately quantifying aflatoxin production by fungi in culture. We developed a culture system for inducing aflatoxin production by Aspergillus using maize kernels as growth substrate followed by quantification using ELISA. The method was compared to the Dichlorvos-Ammonia (DV-AM) method for determining aflatoxigenicity. Our findings encapsulate a method more robust than the currently used DV-AM approach because, for the first time, we are able to assess aflatoxigenicity and aflatoxigenic variability among Aspergillus species earlier classified as non-aflatoxigenic by the DV-AM method. Furthermore, the new method presents an opportunity to attribute toxin production by actively growing fungal cultures. We believe this method when further developed presents a chance to study and predict fungal behavior prior to field trials for biological control programs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".