Microneedle-Based Portable Immunosensor for the On-Site Determination of Microcystin-LR without Sample Preparation in Aquatic Products
Bibliographic record
Abstract
Microcystin-LR (MC-LR) contamination in aquatic products poses significant health risks, necessitating efficient, rapid field detection methods. In this work, a field-deployable, sample preparation-free MC-LR biosensor was first fabricated based on antibody-functionalized swellable microneedles (MNs), which proactively absorbed the biofluids from aquatic samples and selectively concentrated potential MC-LR molecules. Afterward, the remaining active sites of MNs were combined with known amounts of a horseradish peroxidase-labeled MC-LR antigen. Finally, the leftover free enzyme-labeled antigen was transferred to catalyze TMB/H 2 O 2 substrates for signal responses, where the color intensity was proportionally correlated with the concentration of MC-LR. Sensitive analytical performance (LOD 0.22 μg/kg), reasonable recovery in spiked samples (87.6–103.4%, CVs < 10%), and accurate detection in 45 blind samples confirmed its feasibility in the real-word application. This work hybridizes the specific, swellable CS-Ab MNs with a miniaturized homemade analyzer, providing a unique, convenient, and point-of-need analytical technique for MC-LR within 30 min.
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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".