Survival and detection of bivalve transmissible neoplasia from the soft-shell clam <i>Mya arenaria</i> (MarBTN) in seawater
Bibliographic record
Abstract
ABSTRACT Many pathogens can cause cancer, but cancer itself does not normally act as an infectious agent. However, transmissible cancers have been found in a few cases in nature: in Tasmanian devils, dogs, and several bivalve species. The transmissible cancers in dogs and devils are known to spread through direct physical contact, but the exact route of transmission of bivalve transmissible neoplasia (BTN) has not yet been confirmed. It has been hypothesized that cancer cells could be released by diseased animals and spread through the water column to infect/engraft into other animals. To test the feasibility of this proposed mechanism of transmission, we tested the ability of BTN cells from the soft-shell clam ( Mya arenaria BTN, or MarBTN) to survive in artificial seawater. We found that BTN cells are highly sensitive to salinity, with acute toxicity at salinity levels lower than those found in their environment. BTN cells also survive longer at lower temperatures, with >48% of cells surviving a week in seawater at temperatures from 4°C to 16°C, and 49% surviving for more than two weeks at 4°C. With one clam donor, living cells were observed for more than eight weeks at 4°C. We also used qPCR of environmental DNA (eDNA) to detect the presence of BTN-specific DNA in the environment. We observed release of BTN-specific DNA into the water of aquaria from tanks with highly BTN-positive clams, and we detected BTN-specific DNA in seawater samples collected from BTN-endemic areas, although the level detected was much lower. Overall, these data show that BTN cells can survive well in seawater, and they are released into the water by diseased animals, supporting the hypothesis that BTN is spread from animal-to-animal by cells through seawater.
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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 | 0.000 |
| 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".