Amplified visual immunosensor integrated with nanozyme for ultrasensitive detection of avian influenza virus
Bibliographic record
Abstract
Nanomaterial-based artificial enzymes or nanozymes exhibit superior properties such as stability, cost effectiveness and ease of preparation in comparison to conventional enzymes.However, the lower catalytic activity of nanozymes limits their sensitivity and thereby practical applications in the bioanalytical field.To overcome this drawback, herein we propose a very simple but highly sensitive, specific and low-cost dual enhanced colorimetric immunoassay for avian influenza A (H5N1) virus.3,3´,5,5´-Tetramethylbenzidine (TMBZ) was used as a reducing agent to produce gold nanoparticles (Au NPs) with blue colored solution from a viral target-specific antibody-gold ion mixture at first step.The developed blue color from the sensing design was further amplified through catalytic activity of Au NPs in presence of TMBZ-hydrogen peroxide (H 2 O 2 ) solution in second step.Hence, the developed dual enhanced colorimetric immunosensor enables the detection of avian influenza virus A (H5N1) with a limit of detection (LOD) of 1.11 pg/mL.Our results confirmed that the developed assay has superior sensitivity than the conventional ELISA method, plasmonic-based bioassay and commercial flu diagnostic kits.Proposed sensing method further showed its capability to detect viruses, avian influenza A (H4N6) and A (H9N2) virus, in blood samples with limit of detection of 0.0269 HAU and 0.0331 HAU respectively.
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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 | 0.001 | 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".