A Neonatal Model of Intravenous Staphylococcus epidermidis Infection in Mice <24 h Old Enables Characterization of Early Innate Immune Responses
Bibliographic record
Abstract
Staphylococcus epidermidis (SE) causes late onset sepsis and significant morbidity in catheterized preterm newborns. Animal models of SE infection are useful in characterizing disease mechanisms and are an important approach to developing improved diagnostics and therapeutics. Current murine models of neonatal bacterial infection employ intraperitoneal or subcutaneous routes at several days of age, and may, therefore, not accurately reflect distinct features of innate immune responses to bacteremia. In this study we developed, validated, and characterized a murine model of intravenous (IV) infection in neonatal mice <24 hours (h) old to describe the early innate immune response to SE. C57BL/6 mice <24 h old were injected IV with 10(6), 10(7), 10(8) colony-forming units (CFU) of SE 1457, a clinical isolate from a central catheter infection. A prospective injection scoring system was developed and validated, with only high quality injections analyzed. Newborn mice were euthanized between 2 and 48 h post-injection and spleen, liver, and blood collected to assess bacterial viability, gene expression, and cytokine production. High quality IV injections demonstrated inoculum-dependent infection of spleen, liver and blood. Within 2 h of injection, SE induced selective transcription of TLR2 and MyD88 in the liver, and increased systemic production of plasma IL-6 and TNF-α. Despite clearance of bacteremia and solid organ infection within 48 h, inoculum-dependent impairment in weight gain was noted. We conclude that a model of IV SE infection in neonatal mice <24 h old is feasible, demonstrating inoculum-dependent infection of solid organs and a pattern of bacteremia, rapid and selective innate immune activation, and impairment of weight gain typical of infected human neonates. This novel model can now be used to characterize immune ontogeny, evaluate infection biomarkers, and assess preventative and therapeutic modalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".