Rapid and simultaneous detection of human hepatitis B virus and hepatitis C virus antibodies based on a protein chip assay using nano-gold immunological amplification and silver staining method
Bibliographic record
Abstract
BACKGROUND: Viral hepatitis due to hepatitis B virus and hepatitis C virus are major public health problems all over the world. Traditional detection methods including polymerase chain reaction (PCR)-based assays and enzyme-linked immunosorbent assays (ELISA) are expensive and time-consuming. In our assay, a protein chip assay using Nano-gold Immunological Amplification and Silver Staining (NIASS) method was applied to detect HBV and HCV antibodies rapidly and simultaneously. METHODS: Chemically modified glass slides were used as solid supports (named chip), on which several antigens, including HBsAg, HBeAg, HBcAg and HCVAg (a mixture of NS3, NS5 and core antigens) were immobilized respectively. Colloidal nano-gold labelled staphylococcal protein A (SPA) was used as an indicator and immunogold silver staining enhancement technique was applied to amplify the detection signals, producing black image on array spots, which were visible with naked eyes. To determine the detection limit of the protein chip assay, a set of model arrays in which human IgG was spotted were structured and the model arrays were incubated with different concentrations of anti-IgG. A total of 305 serum samples previously characterized with commercial ELISA were divided into 4 groups and tested in this assay. RESULTS: We prepared mono-dispersed, spherical nano-gold particles with an average diameter of 15 +/- 2 nm. Colloidal nano-gold-SPA particles observed by TEM were well-distributed, maintaining uniform and stable. The optimum silver enhancement time ranged from 8 to 12 minutes. In our assay, the protein chips could detect serum antibodies against HBsAg, HBeAg, HBcAg and HCVAg with the absence of the cross reaction. In the model arrays, the anti-IgG as low as 3 ng/ml could be detected. The data for comparing the protein chip assay with ELISA indicated that no distinct difference (P > 0.05) existed between the results determined by our assay and ELISA respectively. CONCLUSION: Results showed that our assay can be applied with serology for the detection of HBV and HCV antibodies rapidly and simultaneously in clinical detection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".