Impact of age on the host response to sepsis in a murine model of fecal-induced peritonitis
Bibliographic record
Abstract
Abstract Introduction Despite older adults being more vulnerable to sepsis, most preclinical research on sepsis has been conducted using young animals. This results in decreased scientific validity since age is an independent predictor of poor outcome. In this study, we explored the impact of aging on the host response to sepsis using the fecal-induced peritonitis (FIP) model developed by the National Preclinical Sepsis Platform (NPSP). Methods C57BL/6 mice (3 or 12 months old) were injected intraperitoneally with rat fecal slurry (0.75 mg/g) or a control vehicle. To investigate the early stage of sepsis, mice were culled at 4 h, 8 h, or 12 h to investigate disease severity, immunothrombosis biomarkers, and organ injury. Mice received buprenorphine at 4 h post-FIP. A separate cohort of FIP mice were studied for 72 h (with buprenorphine given at 4 h, 12 h, and then every 12 h post-FIP and antibiotics/fluids starting at 12 h post-FIP). Organs were harvested, plasma levels of Interleukin (IL)-6, IL-10, monocyte chemoattract protein (MCP-1)/CCL2, thrombin-antithrombin (TAT) complexes, cell-free DNA (CFDNA), and ADAMTS13 activity were quantified, and bacterial loads were measured. Results In the 12 h time course study, aged FIP mice demonstrated increased inflammation and injury to the lungs compared to young FIP mice. In the 72 h study, aged FIP mice exhibited a higher mortality rate (89%) compared to young FIP mice (42%) (p < 0.001). Aged FIP non-survivors also exhibited a trend towards elevated IL-6, TAT, CFDNA, CCL2, and decreased IL-10, and impaired bacterial clearance compared to young FIP non-survivors. Conclusion To our knowledge, this is the first study to investigate the impact of age on survival using the FIP model of sepsis. Our model includes clinically-relevant supportive therapies and inclusion of both sexes. The higher mortality rate in aged mice may reflect increased inflammation and worsened organ injury in the early stage of sepsis. We also observed trends in impaired bacterial clearance, increase in IL-6, TAT, CFDNA, CCL2, and decreased IL-10 and ADAMTS13 activity in aged septic non-survivors compared to young septic non-survivors. Our aging model may help to increase the scientific validity of preclinical research and may be useful for identifying mechanisms of age-related susceptibility to sepsis as well as age-specific treatment strategies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".