Infection of rabbit kidney cells (RK13) by enteropathogenic<i>Escherichia coli</i>as a model to study the dynamics of actin cytoskeleton
Bibliographic record
Abstract
Enteropathogenic Escherichia coli (EPEC) colonizes the intestinal mucosa and causes a cell lesion known as attachment and effacement (A/E) lesion. The molecular mechanisms for A/E lesions include injection of Tir, which is a receptor for an adhesin named intimin. The Tir-intimin interaction causes rearrangement of the cytoskeleton forming actin-rich structures called pedestals. Unfortunately, the formation of the A/E lesions and the dynamics of the actin cytoskeleton during this rearrangement induced by EPEC cannot be studied in the natural host. However, there are EPEC strains that infect rabbit (REPEC) that are genetically and pathologically similar to EPEC. Here, we used REPEC for the infection of rabbit kidney epithelial cells, line RK13, as a model to understand the actin cytoskeleton dynamics during pedestal formation. Actin-rich pedestal formation during the infection of RK13 cells by REPEC was analyzed by electron and confocal microscopy. The kinetics of infection along with the use of antibiotics for eliminating the bacteria, as well as reinfection, evidenced the plasticity of the actin cytoskeleton during pedestal formation. Thus, this model is a helpful tool for studying the dynamics of actin cytoskeleton and for correlating the data with those observed in in vivo models in rabbits experimentally infected with REPEC.
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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) | 0.001 | 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 | 0.001 |
| 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".