Multiphasic hepatitis B virus kinetic patterns in humanized chimeric mice can be explained via stochastic agent-based modeling of intracellular virion production cycles
Bibliographic record
Abstract
Abstract Serum hepatitis B virus (HBV) kinetics in urokinase-type plasminogen activator/severe combined immunodeficient (uPA-SCID) mice reconstituted with humanized livers from inoculation to steady state is highly dynamic despite the absence of an adaptive immune response. We developed a stochastic agent-based model that includes virion production cycles in individual infected human hepatocytes. The model was calibrated using a genetic algorithm approach with the serum HBV kinetics observed in mice inoculated with 10 8 HBV genome equivalents and fit the data well when the following viral production parameters were assumed: (1) An eclipse phase lasting 5-50 hours and (2) a post-eclipse phase production rate that is based on increasing production cycles initially starting with a long production cycle of 1 virion per 20 hours that gradually reaches 1 virion per hour after approximately 3-4 days before virion production increases dramatically to reach to a steady state production rate of 4 virions per hour per cell. The model was then validated by showing it could accurately simulate the viral kinetics observed with lower HBV inoculation doses (10 4 -10 7 genome equivalents) in which similar, but delayed patterns were observed. Together, modeling suggests that it is the cyclic nature of the virus lifecycle combined with an initial slow but increasing rate of HBV production from each cell that plays a role in generating the observed multiphasic HBV kinetic patterns in humanized mice.
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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 | 0.000 | 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) | 0.001 | 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".