Impact of combination antiretroviral therapy in the <scp>NOD</scp>.c3c4 mouse model of autoimmune biliary disease
Bibliographic record
Abstract
BACKGROUND & AIMS: The NOD.c3c4 mouse model develops autoimmune biliary disease characterized by spontaneous granulomatous cholangitis, antimitochondrial antibodies and liver failure. This model for primary biliary cirrhosis (PBC) has evidence of biliary infection with mouse mammary tumour virus (MMTV), suggesting that the virus may have a role in cholangitis development and progression of liver disease in this mouse model. We tested the hypothesis that MMTV infection is associated with cholangitis in the NOD.c3c4 mouse model by investigating whether antiretroviral therapy impacts on viral levels and liver disease. METHODS: NOD.c3c4 mice were treated with combination antiretroviral therapy. Response to treatment was studied by measuring MMTV RNA in the liver, liver enzyme levels in serum and liver histology using a modified Ishak score. RESULTS: Combination therapy with the reverse transcriptase inhibitors, tenofovir and emtricitabine, resulted in a significant reduction in serum liver enzyme levels, attenuation of cholangitis and decreased MMTV levels in the livers of NOD.c3c4 mice. Furthermore, treatment with the retroviral protease inhibitors, lopinavir and ritonavir, in addition to the reverse transcriptase inhibitors, resulted in further decrease in MMTV levels and attenuation of liver disease in this model. CONCLUSIONS: The attenuation of cholangitis with regimens containing the reverse transcriptase inhibitors, tenofovir and emtricitabine, and the protease inhibitors, lopinavir and ritonavir, suggests that retroviral infection may play a role in the development of cholangitis in this model.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".