Some but not All <i>Tetrahymena</i> Species Destroy Monolayer Cultures of Cells from a Wide Range of Tissues and Species
Bibliographic record
Abstract
The activities of Tetrahymena corlissi, Tetrahymena thermophila, and Tetrahymena canadensis were studied in coculture with cell lines of insects, fish, amphibians, and mammals. These ciliates remained viable regardless of the animal cell line partner. All three species could engulf animal cells in suspension. However, if the animal cells were monolayer cultures, the monolayers were obliterated by T. corlissi and T. thermophila. Both fibroblast and epithelial monolayers were destroyed but the destruction of human cell monolayers was done more effectively by T. thermophila. By contrast, T. canadensis was unable to destroy any monolayer. At 4 °C T. thermophila and T. corlissi did not carryout phagocytosis and did not destroy monolayers, whereas T. canadensis was able to carryout phagocytosis but still could not destroy monolayers. Therefore, monolayer destruction appeared to require phagocytosis, but by itself this was insufficient. In addition, the ciliates expressed a unique swimming behavior. Tetrahymena corlissi and T. thermophila swam vigorously and repeatedly into the monolayer, which seemed to loosen or dislodge cells, whereas T. canadensis swam above the monolayer. Therefore, differences in swimming behavior might explain why T. corlissi has been reported to be a pathogen but T. canadensis has not.
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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 | 0.001 |
| 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".