A Homogenous Fluorescence Quenching Based Assay for Specific and Sensitive Detection of Influenza Virus A Hemagglutinin Antigen
Bibliographic record
Abstract
Influenza pandemics cause millions of deaths worldwide. Effective surveillance is required to prevent their spread and facilitate the development of appropriate vaccines. In this study, we report the fabrication of a homogenous fluorescence-quenching-based assay for specific and sensitive detection of influenza virus surface antigen hemagglutinins (HAs). The core of the assay is composed of two nanoprobes namely the glycan-conjugated highly luminescent quantum dots (Gly-QDs), and the HA-specific antibody-modified gold nanoparticle (Ab-Au NPs). When exposed to strain-specific HA, a binding event between the HA and the two nanoprobes takes place, resulting in the formation of a sandwich complex which subsequently brings the two nanoprobes closer together. This causes a decrease in QDs fluorescence intensity due to a non-radiative energy transfer from QDs to Au NPs. A resulting correlation between the targets HA concentrations and fluorescence changes can be observed. Furthermore, by utilizing the specific interaction between HA and glycan with sialic acid residues, the assay is able to distinguish HAs originated from viral subtypes H1 (human) and H5 (avian). The detection limits in solution are found to be low nanomolar and picomolar level for sensing H1-HA and H5-HA, respectively. Slight increase in assay sensitivity was found in terms of detection limit while exposing the assay in the HA spiked in human sera solution. We believe that the developed assay could serve as a feasible and sensitive diagnostic tool for influenza virus detection and discrimination, with further improvement on the architectures.
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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) | 0.001 | 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 | 0.001 |
| 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".