Development of an effective in vitro epithelial cell infection model to study Edwardsiella tarda infections
Bibliographic record
Abstract
Edwardsiella tarda is an enteric pathogen that infects both warm‐water fish and humans. While it is known that E. tarda utilizes syringe‐like secretion systems to deliver bacterially‐derived pathogenic proteins into macrophages during infection, little is known about E. tarda pathogenesis in epithelial cells. To enable the investigation of E. tarda infection mechanisms, we hypothesized that an effective and reliable E. tarda infection model could be developed by utilizing epithelial cells that originate from organs that are targeted during in vivo infections. To develop this in vitro model, isolates of E. tarda from various locations around the world were used to infect 4 different epithelial cell lines derived from fish, using various bacterial loads and durations of the infections. We found that E. tarda infected 10–15% of BF‐2 (Bluegill fry caudal trunk cells) when used at a multiplicity of infection of 10 for three hours at 30°C. The E. tarda infection rate was measured by counting infected cells per slide after fixation and visualization by fluorescence microscopy. Our results constitute the most effective E. tarda infection model ever developed and provides us the foundation to elucidate the strategies E. tarda utilize to cause disease. Grant Funding Source : Natural Sciences and Engineering Research Council
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".