Differential disease tolerance mediates sex-biased illness severity in sepsis
Bibliographic record
Abstract
ABSTRACT Sepsis in humans, as well as mouse models of infection, demonstrates sex-biased outcomes in which males tend to have a higher incidence, higher severity, and higher mortality compared to females. Despite this important sex-bias in sepsis outcomes, little is known about its mechanistic drivers nor therapeutic implications, as much of the foundational data on sepsis pathogenesis is derived from animal studies that included only male subjects, potentially contributing to the notable paucity of successful mouse-to-human translation of sepsis therapeutics. In this study, we demonstrate that male-biased illness severity and organ dysfunction in mouse models of bacterial sepsis is mediated by impaired disease tolerance in males, involving impaired tolerogenic shifts in mitochondrial oxidative metabolism compared to females. Microbiological analyses and systems immunology characterization of sepsis between males and females revealed that sex-biased disease tolerance was independent of infection resistance mechanisms, as well as canonical immune/inflammatory dysregulation. Therapeutic potentiation of mitochondrial tolerance with doxycycline neutralized sexual dimorphism of illness severity and organ dysfunction through a male-predominant treatment effect. These data reveal that biological sex is a fundamental determinant of illness severity and treatment-responsiveness in sepsis through modulation of disease tolerance, which may be harnessed therapeutically to address sex-biased outcomes in sepsis. One sentence summary Sex-biased illness severity in bacterial sepsis is caused by impaired disease tolerance in males that can be rescued by therapeutic potentiation of mitochondrial tolerance
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".