A rapid time-resolved host gene expression signature predicts responses to antibiotic treatment in neonatal bacterial sepsis
Bibliographic record
Abstract
Sepsis is a leading cause of mortality and morbidity in neonates yet remains difficult to diagnose. This leads to widespread empiric antibiotic therapy, which can facilitate the development of antimicrobial resistance. How the dysregulated host response to infection and sepsis evolves after antibiotic treatment is poorly understood. Temporal gene expression in neonates with microbiologically confirmed sepsis, treated with the antibiotic vancomycin as part of a randomized controlled trial, was profiled to reveal a treatment-responsive gene signature. The signature exhibited a rapid reversal of the septic state, observable within 24 hours of the initiation of therapy. Unexpectedly, response rates associated with the adaptive immune system were among the fastest, and these changes were reproduced in both pediatric and adult patients with sepsis, indicating conservation and reversibility of sepsis signatures across the life course. We demonstrated how these treatment-responsive genes could be translated into a prognostic clinical measure, exhibiting strong agreement with clinical assessments. Network modeling of sepsis-responsive genes identified a signature associated with treatment comprising an early transient elevation of antimicrobial defensive genes, suggesting an impaired bactericidal response in neonatal sepsis. These findings suggest that the host response is regulated in sepsis and offer insights into early prognostic approaches for reducing antibiotic overuse.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".