Survival and Growth of <i>Tetrahymena thermophila</i> in Media that are Conventionally Used for Piscine and Mammalian Cells
Bibliographic record
Abstract
The transfer of Tetrahymena thermophila from normosmotic solutions (~20-80 mOsm/kg H(2)O) to hyperosmotic solutions (> 290 mOsm/kg H(2)O) was investigated. During the first 24 h of transfer from proteose peptone yeast extract (PPYE) to either 10 mM HEPES or PPYE with added NaCl to give ~300 mOsm/kg H(2)O, most ciliates died in HEPES but survived in PPYE. Supplementing hyperosmotic HEPES or PPYE with fetal bovine serum (FBS) enhanced survival. When ciliates were transferred from PPYE to a basal medium for vertebrate cells, L-15 (~320 mOsm/kg H(2)O), only a few survived the first 24 h but many survived when the starting cell density at transfer was high (100,000 cells/ml) or FBS was present. These results suggest that nutrients and/or osmolytes in either PPYE or FBS helped ciliates survive the switch to hyperosmotic solutions. FBS also stimulated T. thermophila growth in normosmotic HEPES and PPYE and in hyperosmotic L-15. In L-15 with 10% FBS, the ciliates proliferated for several months and could undergo phagocytosis and bacterivory. These cell culture systems and results can be used to explore how some Tetrahymena species function in hyperosmotic hosts and act as opportunistic pathogens of vertebrates.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".