RNA Interference inhibits Hepatitis B Virus of different genotypes in Vitro and in Vivo
Bibliographic record
Abstract
BACKGROUND: Hepatitis B virus (HBV) infection increases the risk of liver disease and hepatocellular carcinoma. Small interfering RNA (siRNA) can be a potential new tool for HBV therapy. Given the high heterogeneity of HBV strains and the sensitivity towards sequences changes of siRNA, finding a potent siRNA inhibitor against the conservative site on the HBV genome is essential to ensure a therapeutic application. RESULTS: Forty short hairpin RNA (shRNA) expression plasmids were constructed to target conserved regions among nine HBV genotypes. HBV 1.3-fold genome plasmids carrying various genotypes were co-transfected with shRNA plasmids into either Huh7 cells or mice. The levels of various viral markers were examined to assess the anti-HBV efficacy of siRNA. Four (B245, B376, B1581 and B1789) were found with the ability to potently inhibit HBV RNA, DNA, surface antigen (HBsAg), e antigen (HBeAg) and core antigen (HBcAg) expression in HBV genotypes A, B, C, D and I (a newly identified genotype) in Huh7 cells and in mice. No unusual cytotoxicity or off-target effects were noted. CONCLUSIONS: Such siRNA suggests an alternate way of inhibiting various HBV genotypes in vitro and in vivo, promising advances in the treatment of HBV.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".