Direct immunosensing of avian influenza A virus in whole blood using hybrid nanocomposites
Bibliographic record
Abstract
Abstract A sandwich-based electrochemical immunosensor was designed for detection of avian influenza virus (AIV) strains H5N1 and H4N6. This sensor was developed using gold-graphene nanocomposites, immobilized viral antibodies, and CdTe quantum dot electrochemical tagging. The nanocomposites were formed by the simultaneous reduction of a gold salt and graphene using hydroquinone as the reducing agent, thus producing non-spherical gold nanoparticles on graphene sheets. Viral antibodies were immobilized on nanocomposites and CdTe quantum dots through N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide and N-hydroxysuccinimide chemistry. Cyclic voltammetry studies were used to validate the detection of H5N1 surface protein and H4N6 inactivated virus. The immunosensor detected H5 protein in phosphate buffer solution (pH 7.4) with a limit of detection (LOD) of 10 fg/mL and a linear detection range was established for 10 ng/mL to 10 pg/mL. The biosensor detected H4N6 in three parts diluted whole chicken blood with a LOD of 1.28×10 −7 hemagglutinating units (HAU). Commercial ELISA testing for H5N1 and H4N6 showed limits of detection of 10 ng/mL and 0.128 HAU, respectively. The sensor showed 10 6 -fold increased detection of H4N6 virus in blood in comparison to its commercial ELISA kit counterpart. The developed immunosensor effectively change the way avian influenza is detected, monitored, and controlled; transforming time-consuming reactive methods, into rapid predictive technology.
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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) | 0.001 | 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".