Age-dependent roles of cardiac remodeling in sepsis defense and pathogenesis
Bibliographic record
Abstract
Summary Disease tolerance is a defense strategy essential for survival of infections, limiting physiological damage without killing the pathogen. The disease course and pathology a pathogen may cause can change over the lifespan of a host due to the structural and functional physiological changes that accumulate with age. Since successful disease tolerance responses require the host to engage mechanisms that are compatible with the disease course and pathology caused by an infection, we predicted that this defense strategy would change with age. Animals infected with a lethal dose 50 (LD 50 ) of a pathogen often display distinct health and sickness trajectories due to differences in disease tolerance, and thus can be used to delineate tolerance mechanisms. Using a polymicrobial sepsis model, we found that despite having the same LD 50 , old and young susceptible mice exhibited distinct disease courses. Young survivors employed a cardioprotective mechanism via FoxO1-mediated regulation of the ubiquitin-proteosome system that was necessary for survival and protection from cardiomegaly. This same mechanism was a driver of sepsis pathogenesis in aged hosts, causing catabolic remodeling of the heart and death. Our findings have implications for the tailoring of therapy to the age of an infected individual and suggest that disease tolerance alleles may exhibit antagonistic pleiotropy.
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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) | 0.000 | 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".