A Controlled Mouse Model for Neonatal Polymicrobial Sepsis
Bibliographic record
Abstract
Neonatal sepsis remains a global burden. A preclinical model to screen effective prophylactic or therapeutic interventions is needed. Neonatal mouse polymicrobial sepsis can be induced by injecting cecal slurry intraperitoneally into day of life 7 mice and monitoring them for the following week. Presented here are the detailed steps necessary for the implementation of this neonatal sepsis model. This includes making a homogeneous cecal slurry stock, diluting it to a weight- and litter-adjusted dose, an outline of the monitoring schedule, and a definition of observed health categories used to define humane endpoints. The generation of a homogeneous cecal slurry stock from pooled donors allows for the administration into many litters over time, reducing the variation between donors, and preventing the use of potentially toxic glycerol. The monitoring strategy used allows for the anticipation of survival outcome and the identification of mice that would later progress to death, allowing for an earlier identification of the humane endpoint. Two main behavioral features are used to define the health scores, namely, the ability of the neonatal mice to right themselves when placed on their back and their level of mobility. These criteria could potentially be applied to address humane endpoints in other studies of neonatal disease in mice, as long as a pilot study is performed to confirm accuracy. In conclusion, this approach provides a standardized method to model newborn sepsis in mice, while providing resources to assess animal welfare used to define early humane endpoints for challenged animals.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".