Development of an immunosuppressed orthotopic hepatocellular carcinoma rat model for the evaluation of chemo- and radioembolization therapies
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is widely known to be chemo-resistant and presents with significant liver disease resulting in low tolerability to systemic chemotherapy. As a counter measure, more targeted therapies such as trans-arterial chemoembolization (TACE) and trans-arterial radioembolization (TARE) have been developed. To further optimize these therapies, animal models are critical in elucidating the molecular events in disease progression and test new treatment options. The present study focuses on the development of a hepatoma bearing rat model. N1S1 rat hepatoma cells were transfected by a lentiviral method and injected into the liver of Sprague Dawley (SD) and Rowett Nude (RNU) rats. Longitudinal tumor growth was observed by bioluminescence imaging (BLI) and liver/tumor histology. In both models, tumors were visible within 4 days post cell inoculation. Tumor take rates were 52 % and 73 % for male and female SD rats, respectively, and 100 % for male RNU rats. By day 12 and 15 post inoculation, we recorded complete tumor regression in male and female SD rats. Liver histology showed advanced fibrosis in the tumor regressed SD rats, whilst RNU rats exhibited the characteristic sheet pattern of Novikoff tumor with mild liver fibrosis. Increased CD3 and TUNEL staining observed in SD rat livers may be key factors for tumor regression. Our data reveal that the immunocompetent SD rats are not recommended as a model for therapeutic investigations. The immunosuppressed RNU rats, however, are characterized by consistent and reliable tumor growth and thus a desirable model for future therapeutic investigations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".