Neonicotinoid Microsphere Immunosensing for Profiling Applications in Honeybees and Bee-Related Matrices
Bibliographic record
Abstract
Neonicotinoids are the most commonly used insecticides due to their effectiveness. However, non-targeted insects, especially bees, are also affected by neonicotinoids. Therefore, neonicotinoid application can contribute to the declining bee populations worldwide. The presented study describes the development of novel competitive, fluorescent microsphere-based suspension immunoassays for neonicotinoid profiling and their application to bees and essential bee-related matrices, using the Multi-Analyte Profiling (xMAP) technology. For the construction of these neonicotinoid microsphere immunoassays (nMIAs), neonicotinoid-ovalbumin conjugates were coupled to unique fluorescent, paramagnetic microspheres, which competed with the free neonicotinoids that were present in test samples for interacting with the corresponding, specific antibodies. In total, five independent nMIA's were developed for the detection of imidacloprid, acetamiprid, clothianidin, thiacloprid, thiamethoxam, dinotefuran, nitenpyram and imidaclothiz with the limits of detection being for 0.01 ng/mL, 0.01 ng/mL, 0.02 ng/mL, 0.02 ng/mL, 0.003 ng/mL, 2.95 ng/mL, 0.09 ng/mL and 0.04 ng/mL, respectively. The developed nMIAs were applied to fortified matrices including surface water, pollen, honey and honeybees. All of the neonicotinoids, except dinotefuran, could be sensitively detected in all of the tested environmental matrices and bees, with there being sensitivities of 1 ng/mL in water and 10 ng/g in solid materials. These nMIAs provide a rapid profiling method for all of the common neonicotinoids, including those that are banned by the European Union for outdoor use. The developed method can contribute to healthy and sustainable beekeeping, globally, via its application in the apiary environment.
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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".