Comparing various epithelial cell lines as <i>in vitro</i> models for <i>Francisella tularensis</i> non‐phagocytic infections
Bibliographic record
Abstract
The potential bioterrorism agent Francisella tularensis subspecies tularensis ( F. tularensis ) causes the disease tularemia; if untreated the disease can result in up to 60% mortality. As an intracellular bacterium, F. tularensis invades and occupies non‐phagocytic epithelial cells of its host – a process critical to the development of disease. To study epithelial infections, many cell culture models have been developed; yet choosing a suitable model from the literature is challenging due to varied infection parameters and inaccurate assessments of intracellular bacterial loads. In our lab, we have developed an epithelial cell model using a murine surrogate of F. tularensis Francisella tularensis subspecies novicida ( F. novicida ) and the cultured hepatocyte cell line BNL CL.2. Since our model emulates the murine model of tularemia, we hypothesize BNL CL.2 cells will be more susceptible to F. novicida infection compared to other models currently present in the literature. We assessed 8 different models with the same parameters using antibiotic survival assays and an immunofluorescence strategy to label intracellular vs extracellular bacteria. We found that while COS‐7, CMT‐93, and HEK‐293 cell lines may be suitable for studying specific aspects of infection such as invasion or replication; overall, BNL CL.2 cells were the most appropriate cell line to study F. tularensis epithelial infections in vitro . Grant Funding Source : NSERC and CIHR
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".