A novel double antibody sandwich-lateral flow immunoassay for the rapid and simple detection of hepatitis C virus
Bibliographic record
Abstract
The objective of this study was to screen for antigens of the hepatitis C virus (HCV) to establish a new double antibody sandwich-lateral flow immunoassay (DAS-LFIA) method for testing the presence of anti-HCV antibodies in human serum or plasma. A series of different recombinant HCV proteins in Escherichia coli cells were constructed, expressed, purified and the new DAS-LFIA strip was developed. The sensitivity and specificity of new the DAS-LFIA strip were evaluated by detecting 23 HCV-positive sera, a set of quality control references for anti-HCV detection that contain known amounts of anti-HCV antibodies, and 8 HCV-negative sera. A total of 300 clinical serum samples was examined by both the new DAS-LFIA strip and enzyme-linked immunosorbent assay (ELISA). Data were analyzed using SPSS 11.5 software. The sensitivity and specificity of the new DAS-LFIA strip were 100%. The lowest test line of the HCV DAS-LFIA strips was 2 NCU/ml. Additionally, the concordance between the new DAS-LFIA strip and ELISA methods was 94.33%. In conclusion, our new testing method is rapid, simple, sensitive and specifically detects the presence of anti-HCV antibodies in human serum or plasma. Therefore, it may be used for monitoring HCV.
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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 | 0.002 | 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".