Experimental Infection with <em>Listeria monocytogenes</em> as a Model for Studying Host Interferon-&#947; Responses
Bibliographic record
Abstract
L. monocytogenes is a gram-positive bacterium that is a cause of food borne disease in humans. Experimental infection of mice with this pathogen has been highly informative on the role of innate and adaptive immune cells and specific cytokines in host immunity against intracellular pathogens. Production of IFN-γ by innate cells during sublethal infection with L. monocytogenes is important for activating macrophages and early control of the pathogen1-3. In addition, IFN-γ production by adaptive memory lymphocytes is important for priming the activation of innate cells upon reinfection4. The L. monocytogenes infection model thus serves as a great tool for investigating whether new therapies that are designed to increase IFN-γ production have an impact on IFN-γ responses in vivo and have productive biological effects such as increasing bacterial clearance or improving mouse survival from infection. Described here is a basic protocol for how to conduct intraperitoneal infections of C57BL/6J mice with the EGD strain of L. monocytogenes and to measure IFN-γ production by NK cells, NKT cells, and adaptive lymphocytes by flow cytometry. In addition, procedures are described to: (1) grow and prepare the bacteria for inoculation, (2) measure bacterial load in the spleen and liver, and (3) measure animal survival to endpoints. Representative data are also provided to illustrate how this infection model can be used to test the effect of specific agents on IFN-γ responses to L. monocytogenes and survival of mice from this infection.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".