Weekly Sepsis Research Analysis
This week’s sepsis literature emphasized rapid diagnostics and early prediction, mechanistic insights into organ-specific injury, and precision interventions. High-impact reports validated time-series deep learning to predict bloodstream infections before culture results and prospectively validated a plasma ddPCR panel that dramatically increases Gram-negative detection. Mechanistic studies (tissue-resident bladder macrophages; S100A8/A9–RAGE–Drp1 mitochondrial pathway) nominate new prevention a
Summary
This week’s sepsis literature emphasized rapid diagnostics and early prediction, mechanistic insights into organ-specific injury, and precision interventions. High-impact reports validated time-series deep learning to predict bloodstream infections before culture results and prospectively validated a plasma ddPCR panel that dramatically increases Gram-negative detection. Mechanistic studies (tissue-resident bladder macrophages; S100A8/A9–RAGE–Drp1 mitochondrial pathway) nominate new prevention and therapeutic targets, while trials and guidelines push toward microcirculation-guided resuscitation and standardized outcome measures for neonates.
Selected Articles
1. A bladder-blood immune barrier constituted by suburothelial perivascular macrophages restrains uropathogen dissemination.
This mechanistic study identifies suburothelial perivascular macrophages (suPVMs) in the bladder that capture uropathogenic E. coli, maintain inflamed vessel integrity, and deploy METosis with MMP-13 to sequester bacteria and recruit neutrophils. Monocyte-derived replenishment of suPVMs after infection confers protection against recurrence, defining a tissue-resident bladder–blood immune barrier that limits systemic dissemination and suggests prevention strategies for urosepsis.
Impact: Reveals a previously unrecognized tissue-resident immune barrier that mechanistically limits UTI-to-urosepsis transition, opening new preventive and immunomodulatory strategies (macrophage training, METosis/MMP-13 modulation).
Clinical Implications: Translational work should validate suPVM signatures in human bladder tissue and test interventions to boost barrier function (vaccination, monocyte priming, targeted modulation of METosis/MMP-13) to reduce urosepsis risk.
Key Findings
- Identified suburothelial perivascular macrophages (suPVMs) that capture UPEC and preserve inflamed vessel integrity.
- suPVMs undergo METosis releasing extracellular DNA traps and MMP-13 to sequester bacteria and promote neutrophil transuroepithelial migration.
- Monocyte-derived replenishment of suPVMs after prior infection confers protection against recurrent UTIs.
2. Elevated levels of S100A8 and S100A9 exacerbate muscle mitochondrial fragmentation in sepsis-induced muscle atrophy.
This translational study links sepsis-associated skeletal muscle atrophy to an S100A8/A9–RAGE–Drp1 pathway that drives Drp1 phosphorylation, mitochondrial fission, and myocyte atrophy. The work integrates retrospective clinical association (ΔSMI linked to 60-day mortality) with mouse CLP models where inhibition of S100A8/A9, RAGE ablation, or Drp1 inhibition restored mitochondrial function and reduced atrophy, nominating a druggable axis to prevent ICU-acquired weakness.
Impact: Identifies a mechanistic, pharmacologically tractable pathway underlying septic myopathy, bridging clinical risk signal to validated in vivo and in vitro interventions.
Clinical Implications: Supports development of biomarker strategies (S100A8/A9) and early-phase trials of RAGE or Drp1 modulators to prevent or mitigate ICU-acquired muscle wasting and weakness in sepsis survivors.
Key Findings
- ΔSMI (change in skeletal muscle index) was independently associated with 60-day mortality in septic patients.
- Sepsis increased S100a8/a9 expression and mitochondrial dysfunction in mouse skeletal muscle; blocking S100a8/a9 or inhibiting Drp1 improved mitochondrial morphology and reduced atrophy.
- S100a8/a9 binds RAGE to induce Drp1 phosphorylation and mitochondrial fragmentation; RAGE ablation mitigates these effects.
3. Utilising routinely collected clinical data through time series deep learning to improve identification of bacterial bloodstream infections: a retrospective cohort study.
In a large single-system retrospective cohort (n=20,850), an LSTM time-series model using up to 14 days of prior laboratory data predicted pathogenic bloodstream infections with AUROC 0.97 on a temporal hold-out set, substantially outperforming static models. CRP, eosinophil, and platelet trajectories were important predictors. The approach offers a path to earlier, individualized diagnostic decision support to guide expedited testing and stewardship.
Impact: Demonstrates that routinely collected longitudinal data can power high-performance, clinically actionable models to predict bloodstream infection ahead of culture, enabling earlier targeted diagnostics and antimicrobial stewardship interventions.
Clinical Implications: Integrating time-series LSTM models into EHR workflows could triage patients for expedited cultures/therapy and reduce unnecessary empiric antibiotics for low-risk patients; prospective impact and external multi-center validation are needed prior to deployment.
Key Findings
- LSTM using up to 14 days of prior labs achieved AUROC 0.97 and AUPRC 0.65 in temporal hold-out testing, outperforming static logistic models (AUROC 0.74).
- Time-series dynamics (CRP, eosinophils, platelets) were key predictors, and removing temporal information degraded performance, especially for hospital-acquired BSIs.
- Large-scale retrospective dataset (n=20,850) with temporal hold-out validation demonstrates clinical feasibility.