Pangenomic antiviral effect of REP 2139 in CRISPR/Cas9 engineered cell lines expressing hepatitis B virus surface antigen
Bibliographic record
Abstract
Chronic hepatitis B remains a global health problem with 296 million people living with chronic HBV infection and being at risk of developing cirrhosis and hepatocellular carcinoma. Non-infectious subviral particles (SVP) are produced in large excess over infectious Dane particles in patients and are the major source of Hepatitis B surface antigen (HBsAg). They are thought to exhaust the immune system, and it is generally considered that functional cure requires the clearance of HBsAg from blood of patient. Nucleic acid polymers (NAPs) antiviral activity lead to the inhibition of HBsAg release, resulting in rapid clearance of HBsAg from circulation in vivo. However, their efficacy has only been demonstrated in limited genotypes in small scale clinical trials. HBV exists as nine main genotypes (A to I). In this study, the HBsAg ORFs from the most prevalent genotypes (A, B, C, D, E, G), which account for over 96% of human cases, were inserted into the AAVS1 safe-harbor of HepG2 cells using CRISPR/Cas9 knock-in. A cell line producing the D144A vaccine escape mutant was also engineered. The secretion of HBsAg was confirmed into these new genotype cell lines (GCLs) and the antiviral activity of the NAP REP 2139 was then assessed. The results demonstrate that REP 2139 exerts an antiviral effect in all genotypes and serotypes tested in this study, including the vaccine escape mutant, suggesting a pangenomic effect of the NAPs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".