Lipoprotein profiles in SCID/uPA mice transplanted with human hepatocytes become human-like and correlate with HCV infection success
Bibliographic record
Abstract
Although multiple determinants for hepatitis C virus (HCV) infection are known, it remains partly unclear what determines the human specificity of HCV infection. Presumably, the presence of appropriate entry receptors is essential, and this may explain why HCV is unable to infect nonhuman hepatocytes. However, using mice with chimeric human livers, we show in this study that the presence of human hepatocytes, and therefore human entry receptors, is not sufficient for HCV infection. In successfully transplanted SCID/Alb-uPA mice, infection with HCV is reliable only when ∼70-80% of the liver consists of human hepatocytes. We show that chimeric mice, which are hard to infect with HCV, have significant groups of human hepatocytes that are readily infected with hepatitis B virus. Thus it is unlikely that the lack of infection with HCV can simply be attributed to low hepatocyte numbers. We investigated whether the humanization of lipoprotein profiles is positively associated with infection success. We show that the lipoprotein profiles of chimeric mice become more human-like at high levels of engraftment of human hepatocytes. This and expression of markers of human lipoprotein biosynthesis, human apolipoprotein B (ApoB) and cholesterol ester transfer protein (CETP), show a strong positive correlation with successful infection. Association of HCV in the blood of chimeric mice to ApoB-containing lipoproteins is comparable to association of HCV in patient serum and provides further support for a critical role for ApoB-containing lipoproteins in the infectious cycle of HCV. Our data suggest that the weakest link in the HCV infection chain does not appear to be the presence of human hepatocytes per se. We believe that HCV infection also depends on the presence of sufficient levels of human lipoproteins.
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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 | 0.001 | 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) | 0.001 | 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".