Long-term immune dysfunction induced by sepsis is dependent on age
Bibliographic record
Abstract
Background and Purpose: Sepsis-surviving adult individuals commonly develop immunosuppression and increased susceptibility to secondary infections, outcome mediated by the axis IL-33/ILC2s/M2 macrophages/Tregs. Nonetheless, the long-term immune consequences of pediatric sepsis are indeterminate. We sought to investigate the role of age in the genesis of immunosuppression following sepsis. Experimental Approach: Here, we compared the frequency of Tregs, the activation of the IL33/ILC2s axis in M2 macrophages, and the DNA methylation of epithelial lung cells from post-septic infant and adult mice. Likewise, sepsis-surviving mice were inoculated intranasally with Pseudomonas aeruginosa or by subcutaneous inoculation of the B16 melanoma cell line. Finally, blood samples from sepsis-surviving patients were collected and the concentrations of IL-33 and Tregs frequency were assessed. Key Results: In contrast to 6-week-old, 2-week-old mice were resistant to secondary infection and did not show impairment in tumour controls upon melanoma challenge. Mechanistically, increased IL-33 levels, Tregs expansion, and activation of ILC2s and M2-macrophages were observed in 6-week-old but not 2-week-old post-septic mice. Moreover, impaired IL-33 production in 2-week-old post-septic mice was associated with increased DNA methylation in lung epithelial cells. Notably, IL-33 treatment boosted the expansion of Tregs and induced immunosuppression in 2-week-old mice. Clinically, adults but not pediatric post-septic patients exhibited higher counts of Tregs and sera IL-33 levels. Conclusion and Implications: These findings demonstrate a crucial and age-dependent role for IL-33 in post-sepsis immunosuppression. Thus, a better understanding of this process could lead to differential treatments for adult and pediatric sepsis.
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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) | 0.000 | 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 | 0.001 |
| 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".