Magnetic Nanozyme-Linked Immunosorbent Assay for Ultrasensitive Influenza A Virus Detection
Bibliographic record
Abstract
Rapid and sensitive detection of influenza virus is of soaring importance to prevent further spread of infections and adequate clinical treatment. Herein, an ultrasensitive colorimetric assay called magnetic nano(e)zyme-linked immunosorbent assay (MagLISA) is suggested, in which silica-shelled magnetic nanobeads (MagNBs) and gold nanoparticles are combined to monitor influenza A virus up to femtogram per milliliter concentration. Two essential strategies for ultrasensitive sensing are designed, i.e., facile target separation by MagNBs and signal amplification by the enzymelike activity of gold nanozymes (AuNZs). The enzymelike activity was experimentally and computationally evaluated, where the catalyticity of AuNZ was tremendously stronger than that of normal biological enzymes. In the spiked test, a straightforward linearity was presented in the range of 5.0 × 10 –15 –5.0 × 10 –6 g·mL –1 in detecting the influenza virus A (New Caledonia/20/1999) (H1N1). The detection limit is up to 5.0 × 10 –12 g·mL –1 only by human eyes, as well as up to 44.2 × 10 –15 g·mL –1 by a microplate reader, which is the lowest record to monitor influenza virus using enzyme-linked immunosorbent assay-based technology as far as we know. Clinically isolated human serum samples were successfully observed at the detection limit of 2.6 PFU·mL –1 . This novel MagLISA demonstrates, therefore, a robust sensing platform possessing the advances of fathomable sample separation, enrichment, ultrasensitive readout, and anti-interference ability may reduce the spread of influenza virus and provide immediate clinical treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".