Animal Models of Liver Cancer: Current Status and Application in Pre-clinical Research
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide.HCC develops in various causes -Viral hepatitis infection, toxins, or other liver conditions -by activation of oncogenes and/or inactivation of tumor suppressors.Understanding of signal pathways and protein-protein interactions critical in tumor development may lead to novel treatment strategy.To evaluate the progression of HCC and effects of potential therapies, various animal models have been established.Experimental models of HCC provide valuable tools to investigate the risk factors, new treatment modalities and biologic characteristics.Subcutaneous xenograft models have been widely used in the past.However, with the advancement of in vivo imaging technology, investigators are more concerned with the orthotopic models nowadays.Genetically engineered mouse models have greatly facilitated studies of gene function in HCC development.Lately, a novel approach for stable gene expression in mouse hepatocytes by hydrodynamic injection has been developed.Each model has its own advantages and disadvantages.Therefore, selecting the optimal models based on study objectives is necessary.In this review, we highlight both the frequently used mouse models and some emerging ones with emphasis on their merits or defects, and give advices for investigators to choose a ''best-fit'' animal model in HCC research.(J Liver Cancer 2017;17:1-14)
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".