The <i>Galleria mellonella</i>-Enteropathogenic <i>Escherichia coli</i> Model System: Characterization of Pathogen Virulence and Insect Immune Responses
Bibliographic record
Abstract
The use of Galleria mellonella (Linnaeus) (Lepidoptera: Pyralidae), an economical insect model, for the study of enteropathogenic Escherichia coli (Migula) (EPEC), a diarrheagenic human pathogen, has been demonstrated previously but remains poorly understood. The present study characterizes the Galleria-EPEC system extensively for future studies using this system. We found that EPEC causes disease in G. mellonella larvae when injected intrahemocoelically but not orally. Disease manifests as increased mortality, decreased survival time, delayed pupation, decreased pupal mass, increased pupal duration, and hemocytopenia. Disease symptoms are dose-dependent and can be used as metrics for measuring EPEC virulence in future studies. The type III secretion system was only partially responsible for EPEC virulence in G. mellonella while the majority of the virulence remains unknown in origin. EPEC elicits insect anti-bacterial immune responses including melanization, hemolymph coagulation, nodulation, and phagocytosis. The immune responses were unable to control EPEC replication in the early stage of infection (≤3 h post-injection). EPEC clearance from the hemocoel does not guarantee insect survival. Overall, this study provided insights into EPEC virulence and pathogenesis in G. mellonella and identified areas of future research using this system.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".