Characterization of liver histopathology in a transgenic mouse model expressing genotype 1a hepatitis C virus core and envelope proteins 1 and 2
Bibliographic record
Abstract
Hepatitis C virus (HCV) is a major cause of chronic hepatitis and hepatocellular carcinoma worldwide. The purpose of this study was to determine how the HCV structural proteins affect the dynamic structural and functional properties of hepatocytes and measure the extra-hepatic manifestations induced by these viral proteins. A transgenic mouse model was established by expressing core, E1 and E2 proteins downstream of a CMV promoter. HCV RNA was detected using RT-PCR in transgenic mouse model tissues, such as liver, kidney, spleen and heart. Expression of the transgene was analysed by real-time PCR to quantify viral RNA in different tissues at different ages. Immunofluorescence analysis revealed the expression of core, E1 and E2 proteins predominantly in hepatocytes. Lower levels of protein expression were detected in spleen and kidneys. HCV RNA and viral protein expression increased in the liver with age. Histological analysis of liver cells demonstrated steatosis in transgenic mice older than 3 months, which was more progressed with age. Electron microscopy analysis revealed alterations in nuclei, mitochondria and endoplasmic reticulum. HCV structural proteins induce a severe hepatopathy in the transgenic mouse model. These mice became more prone to liver and lymphoid tumour development and hepatocellular carcinoma. In this model, the extra-hepatic effects of HCV, which included swelling of renal tubular cells, were mild. It is likely that the HCV structural proteins mediate some of the histological alterations in hepatocytes by interfering with lipid transport and liver metabolism.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".