mRNA-LNP vaccines against hepatitis B virus induce protective immune responses in preventive and chronic mouse challenge models
Bibliographic record
Abstract
Over 300 million people worldwide suffer from chronic hepatitis B virus (HBV) infections that can cause serious liver damage and hepatocellular carcinoma. Ineffective innate and adaptive immune responses characterize these chronic infections, making the development of a therapeutic vaccine an urgent medical need. While current vaccines can prevent HBV infections, they are ineffective in treating chronic disease. This study investigated lipid nanoparticle (LNP)-formulated nucleoside-modified mRNA vaccines encoding hepatitis B surface antigen (HBsAg) for prophylactic and therapeutic applications. We found that HBsAg mRNA-LNP vaccines induced robust humoral and cellular immune responses, outperforming the protein-based vaccine approved for human use. The incorporation of a major histocompatibility complex class I (MHC class I) signal peptide further enhanced Th1-biased responses preventing HBV infections in a mouse model. Importantly, mRNA-LNP vaccination led to seroconversion, HBsAg clearance, and strong T cell responses in a chronically infected mouse model. These findings highlight the potential of mRNA-LNP as an alternative and effective vaccine modality for HBV prophylaxis and therapeutic use in treating chronic infections.
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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) | 0.001 | 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".