Xenografts of human hepatocellular carcinoma: A tool for screening drugs used in treatment of this disease
Bibliographic record
Abstract
1985 Hepatocellular carcinoma (HCC) is a common malignancy in Asia and Africa. There is currently no successful treatment for HCC. In the present study, we report the establishment and characterisation of 6 lines of xenografts from primary human HCC tumours. They were classified into 1318 and 5-1318 series. The 1318 series (3 lines) was a trabecular type, grew rapidly in SCID mice and doubled its volume every 48 to 60 hrs. The 5-1318 series (3 lines) possessed a pseudoglandular pattern, grew relatively slowly in SCID mice and required approximately 8 to 10 days to double its tumour volume. Although 1318 and 5-1318 xenografts expressed androgen receptor, estrogen receptor alpha and progesterone receptor, their growth rate did not affected by either castration or sex steroid hormone supplementation. Systemic delivery of cisplatin, oxaliplatin, vitamin D analog EB1089 and EGF-R inhibitor Iressa to mice bearing 1318 or 5-1318 xenografts resulted in no growth suppression. Studying their protein profile revealed that growth behaviour of HCC xenografts was positively correlated with the levels of cdc-2. Target inhibition of cdc-2 activity by intraperitoneal delivery of 2-chloroethyl-3-sarcosinamide-1-nitrosourea (SarCNU) to mice bearing either 1318 or 5-1318 xenografts resulted in a significant growth inhibition. The results indicate that 1318 and 5-1318 xenografts are useful tools for screening drugs used in treatment of HCC and suggest a potential use of SarCNU in treatment of this fatal disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".