Determination of Aspergillus pathogens in agricultural products by a specific nanobody-polyclonal antibody sandwich ELISA
Bibliographic record
Abstract
Abstract Aspergillus and its poisonous mycotoxins are distributed worldwide throughout the environment and are of particular interest in agriculture and food safety. In order to develop a specific method for rapid detection of Aspergillus flavus to forecast diseases and control aflatoxins, a nanobody, PO8-VHH, highly reactive to A. flavus was isolated from an immunized alpaca nanobody library by phage display. The nanobody was verified to bind to the components of extracellular and intracellular antigen from both A. flavus and A. parasiticus. To construct a sandwich format immunoassay, polyclonal antibodies against Aspergillus were raised with rabbits. Finally, a highly selective nanobody-polyclonal antibody sandwich enzyme-linked immunosorbent assay was optimized and developed. The results revealed that the detection limits of the two fungi were as low as 1 μg mL−1, and that it is able to detect fungal concentrations below to 2 μg mg−1 of peanut and maize grains in both artificially and naturally contaminated samples. Therefore, we here provided a rapid and simple method for monitoring Aspergillus spp. contamination in agricultural products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".