Impact of Calcineurin Inhibitors With or Without Interferon on Hepatitis C Virus Titers in a Chimeric Mouse Model of Hepatitis C Virus Infection
Bibliographic record
Abstract
Cyclosporine A (CSA) has potent effects against hepatitis C virus (HCV) in vitro, but its clinical efficacy after liver transplantation (LT) is uncertain. We evaluated the impact of CSA and tacrolimus (TAC) with or without concomitant interferon (IFN) therapy on serum HCV titers in a chimeric mouse model of HCV infection. Six groups of HCV-infected mice received only the vehicle, IFN, CSA, CSA and IFN, TAC, or TAC and IFN for 4 weeks. The quantitative HCV polymerase chain reaction levels were determined after 1, 2, and 4 weeks of drug administration. There were no significant differences in the HCV titers after 4 weeks of treatment between the non-IFN-treated groups (log HCV titers: 3.5 ± 0.3 for the vehicle group, 4.4 ± 0.6 for the CSA group, and 4.3 ± 0.4 for the TAC group, P = 0.3). Although IFN had a consistent effect of reducing HCV titers across the groups, there was no significant impact of CSA on HCV levels when it was used alone or in combination with IFN at any time point. The 4-week HCV titers were as follows: 3.2 ± 0.3 for the IFN group, 4.7 ± 0.4 for the CSA/IFN group, and 4.0 ± 0.5 for the TAC/IFN group (P = 0.07). The CSA/IFN and TAC/IFN groups did not differ significantly (P = 0.6). Six of the 7 animals in the IFN group (85.7%) had an HCV titer decline ≥ 1 log, whereas in the test groups (CSA/IFN and TAC/IFN), 6 of 9 animals (66.7%) achieved a 1-log decline in the HCV titer (P = 1). Using this animal model, we could find no evidence supporting the routine use of CSA after LT in HCV-infected patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".