Unique transcriptomic response to sepsis is observed among patients of different age groups
Bibliographic record
Abstract
Sepsis is a major cause of morbidity and mortality, especially at the extremes of age. To understand the human age-specific transcriptomic response to sepsis, a multi-cohort, pooled analysis was conducted on adults, children, infants, and neonates with and without sepsis. Nine public whole-blood gene expression datasets (636 patients) were employed. Age impacted the transcriptomic host response to sepsis. Gene expression from septic neonates and adults was more dissimilar whereas infants and children were more similar. Neonates showed reductions in inflammatory recognition and signaling pathways compared to all other age groups. Likewise, adults demonstrated decreased pathogen sensing, inflammation, and myeloid cell function, as compared to children. This may help to explain the increased incidence of sepsis-related organ failure and death in adults. The number of dysregulated genes in septic patients was proportional to age and significantly differed among septic adults, children, infants, and neonates. Overall, children manifested a greater transcriptomic intensity to sepsis as compared to the other age groups. The transcriptomic magnitude for adults and neonates was dramatically reduced as compared to children and infants. These findings suggest that the transcriptomic response to sepsis is age-dependent, and diagnostic and therapeutic efforts to identify and treat sepsis will have to consider age as an important variable.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".