Time-Resolved Fluorescence Immunochromatographic Assay Developed Using Two Idiotypic Nanobodies for Rapid, Quantitative, and Simultaneous Detection of Aflatoxin and Zearalenone in Maize and Its Products
Bibliographic record
Abstract
Aflatoxins and zearalenone (ZEN) are highly common mycotoxins in maize and maize-based products. This study aimed to report a time-resolved fluorescence immunochromatographic assay (TRFICA) developed using two idiotypic nanobodies for rapid, quantitative, and simultaneous detection of aflatoxin B 1 (AFB 1 ) and ZEN in maize and its products. A novel Eu/Tb(III) nanosphere with enhanced fluorescence was prepared as a label and conjugated to anti-idiotypic nanobody (AIdnb) and monoclonal antibody (mAb). On the basis of nanosphere–antibody conjugation, two patterns of competitive time-resolved strip methods (AIdnb–TRFICA and mAb–TRFICA) were established and compared. The half inhibition concentration of AIdnb–TRFICA was 0.46 and 0.86 ng·mL –1 for AFB 1 and ZEN, which was 18.3- and 20.3-fold more sensitive than that of mAb–TRFICA for AFB 1 and ZEN, respectively. Under optimal conditions, AIdnb–TRFICA for dual mycotoxin was established and provided a quantitative relationship ranging from 0.13 to 4.54 ng·mL –1 for AFB 1 and 0.20 to 2.77 ng·mL –1 for ZEN, with a detection limit of 0.05 and 0.07 ng·mL –1 in the buffer solution, respectively. AIdnb–TRFICA showed good recoveries (72.6%–106.6%) in samples and was applied to detect dual mycotoxin in maize samples with satisfying results. To the best of our knowledge, it is the first report about a time-resolved strip method based on AIdnbs for dual mycotoxin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".