Preliminary study on the efficacy and safety of lamivudine and interferon α therapy in decreasing serum HBV DNA level in HBV positive transgenic mice during pregnancy
Bibliographic record
Abstract
Previous studies reported that the HBV DNA level in maternal serum is an important risk factor for intrauterine infection. Two antiviral drugs, lamivudine (3TC) and interferon alpha (IFNalpha), are used extensively clinically to reduce maternal HBV DNA level, However, because of a lack of evidence on the efficacy and safety of these drugs during pregnancy, they are categorized as grade C which prevents their use during pregnancy. This study provides new data on the efficacy and safety of lamivudine and IFNalpha in HBV positive transgenic pregnant mice. In this study, transgenic mice with high titers of hepatitis B virus (HBV) were employed to study the antiviral effects of 3TC and IFNalpha during different gestation periods. The study also examined changes in several serological HBV markers, the effects of perinatal exposure to antiviral drugs on the mother and offspring, drug efficacy in reducing the level of HBV DNA in maternal blood, and the safety to both the mother and offspring. The main conclusion of the study is that a significant decrease in HBV DNA level can be obtained after treatment with lamivudine but not with IFNalpha. No adverse effects were observed in the maternal mice and the offsprings. This finding may provide a rationale for the potential use of lamivudine for the treatment of pregnant women as a safe and effective measure to reduce the level of maternal viremia.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".