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Record W1986065775 · doi:10.1111/jeu.12214

Some but not All <i>Tetrahymena</i> Species Destroy Monolayer Cultures of Cells from a Wide Range of Tissues and Species

2015· article· en· W1986065775 on OpenAlexaff
Marcel D.O. Pinheiro, Niels C. Bols

Bibliographic record

VenueJournal of Eukaryotic Microbiology · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTetrahymenaBiologyPhagocytosisMonolayerProtozoaCell biologyMicrobiologyBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.239
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2015
Admission routes1
Has abstractyes

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