An adaptable platform for in-house hepatitis C serology
Bibliographic record
Abstract
Serology-based diagnosis remains one of the major tools for diagnosis and surveillance of infectious diseases. However, for many neglected diseases no or only few commercial assays are available and often with prices prohibiting large scale testing in low and middle-income countries (LMICs). We developed an adaptable enzyme-linked immunoassay (ELISA) using hepatitis C virus (HCV) as a proof-of-concept application. By combining the maltose-binding-protein with a multiepitope HCV protein, we were able to obtain a high concentration of protein suitable for downstream applications. Following optimization, the assay was verified using previously tested human samples from Canada, Denmark and Gabon in parallel with the use of a commercial protein. Sensitivity and specificity were calculated to 98 % and 97 % respectively, after accounting for non-specific binding and assay optimization. This study provides a thorough description of the development, and validation of a multiepitope ELISA-based diagnostic assay against HCV, which could be implemented at low cost. The described methodology can be readily adapted to develop novel ELISA-based diagnostic assays for other infectious pathogens with well-described immunogenic epitopes. This method could improve the diagnosis of neglected diseases for which affordable diagnostic assays are lacking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".