Development of Low-Cost In-House Assays for Quantitative Detection of HBsAg, HBeAg, and HBV DNA to Enhance Hepatitis B Virus Diagnostics and Antiviral Screening in Resource-Limited Settings
Bibliographic record
Abstract
Globally, an estimated 254 million people are living with chronic hepatitis B virus (HBV) infection, yet only 10.5% have been diagnosed, underscoring the urgent need to expand testing to meet the World Health Organization’s HBV elimination targets by 2030. Many HBV diagnostic tests remain expensive and inaccessible in resource-limited settings. In this study, we demonstrate how individually sourced, commercially available reagents can be used to develop cost-effective in-house assays for total DNA isolation, HBV viral load quantification by (q)PCR, and qHBsAg and qHBeAg measurement using sandwich ELISA. These assays were validated using known HBV-positive and HBV-negative plasma samples (genotypes A–F) and HepAD38 cells treated with tenofovir disoproxil fumarate (TDF). DNA isolation using a commercial column-based kit was compared to a high-throughput, column-free method, allowing for HBV quantification from 50 µL of plasma with lower limits of detection (LLOD) of 1.8 × 103 and 1.8 × 104 HBV DNA copies IU/mL, respectively. Both commercial and in-house DNA isolation methods yielded comparable half-maximal effective concentration (EC50) values in TDF-treated HepAD38 cells. Additionally, in-house sandwich ELISA assays were developed for quantitative HBsAg and HBeAg detection, with LLOD values of 0.78 IU/mL and 0.38 PEI U/mL (Paul Ehrlich Institute), respectively. The in-house reagents for DNA isolation, molecular testing, and serological detection of HBV were estimated to be at least 10 times more cost-effective than commercially available kits, highlighting their potential for broader application in resource-limited regions.
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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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".