Telomerase activity (TMA) in tumour and peritumoural tissues in a rat liver cancer model
Bibliographic record
Abstract
PURPOSE: To study the levels of telomerase activity (TMA) in tumour and peritumoural tissues in a liver cancer model in rats, and to study the change in TMA expression over time. METHODS: Using the telomeric repeated amplification protocol (TRAP), TMA was measured in tumour tissue, peritumoural tissue and normal liver tissue of Walker-256 tumour-bearing rats at 4, 6 and 8 days after tumour implantation. RESULTS: TMA at day 4, 6 and 8 was 0.767+/-0.117, 0.768+/-0.118 and 0.774+/-0.111 in tumour tissue, 0.389+/-0.263, 0.492+/-0.253 and 0.584+/-0.239 in peritumoural tissue, and 0.231+/-0.022, 0.229+/-0.022 and 0.233+/-0.021 in normal liver tissue, respectively. TMA in tumour tissue was higher than that in peri-tumour and normal liver tissues at all time points of measurement (P < 0.05). The TMA levels in tumour tissue and normal liver tissue did not show any change over time. TMA level in the peritumoural tissue increased with time; TMA level in animals sacrificed at day 8 was higher than that seen in animals sacrificed at day 4 (P < 0.05). CONCLUSION: TMA in walker-256 tumour-bearing rats was higher than that in normal and peritumoural tissues. TMA level in the peritumoural tissue increased with time suggesting that TMA activation in peritumoural tissue may be an important factor promoting tumour growth.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".