Reverse transcriptase activity associated with haemic neoplasia in the soft-shell clam Mya arenaria
Bibliographic record
Abstract
Reverse transcriptase (RT) activity has been reported in bivalves affected by haemic neoplasia (HN). Since all retroviruses have RT, detection of RT activity was regarded as evidence for the retroviral etiology of HN. This study investigates the relationship between RT levels and the progress of HN as indicated by percentages of tetraploid cells in soft-shell clams Mya arenaria. The percentages of tetraploid cells were estimated by flow cytometry, and the RT levels were quantified using TaqMan product-enhanced RT (TM-PERT) assay. Results demonstrated that the amount of RT was positively correlated with the percentage of tetraploid cells circulating in clam haemolymph (R2 = 0.974, p < 0.001). Compared to HN-negative clams (<5% tetraploid cells), 2 stages with significantly elevated levels of RT activity were observed: the first stage at approximately 10 to approximately 20% tetraploid cells, and the second at approximately 30 to approximately 80% tetraploid cells (p < 0.01). These data support the well established fact from mammalian models that transformed cells express high levels of non-telomeric RT. The observed increase in RT levels at approximately 30% tetraploidy coincides with previously reported p53 gene expression. Taken together, this could indicate that using RT levels as an indicator of HN, > or = 30% tetraploidy is the stage at which the disease process undergoes a change, and perhaps becomes irreversible.
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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 | 0.001 | 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 | 0.000 |
| 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".