Daily Sepsis Research Analysis
Analyzed 28 papers and selected 3 impactful papers.
Summary
Analyzed 28 papers and selected 3 impactful articles.
Selected Articles
1. Accelerated Increase in Candida auris Bloodstream Infections during COVID-19 Pandemic, South Africa.
Using national laboratory surveillance and interrupted time-series analysis, the study shows a marked rise in Candida auris among candidemia cases in South Africa during COVID-19, with peaks aligning to pandemic waves. Findings emphasize accelerated replacement toward C. auris and call for strengthened surveillance, diagnostics, and infection prevention.
Impact: Provides robust population-level evidence of a rapid epidemiologic shift toward C. auris candidemia during the pandemic, directly informing public health and hospital IPC strategies.
Clinical Implications: Hospitals should enhance C. auris screening, species-level identification, contact precautions, and antifungal stewardship, particularly during respiratory virus surges. Resource allocation for diagnostics and environmental decontamination should be prioritized.
Key Findings
- Among 15,393 candidemia cases, the proportion of C. auris increased from 17% (2019) to 31% (2021) (p<0.01).
- Post-pandemic onset, Candida BSIs increased by 11 cases per week (p=0.03), largely driven by C. auris (+5 cases/week; p<0.01).
- Candidemia peaks temporally coincided with COVID-19 waves, suggesting healthcare system pressures and antimicrobial use may have contributed.
Methodological Strengths
- Interrupted time-series with segmented regression on national laboratory data across public and private sectors.
- Large sample and species-level attribution enabling robust trend assessment.
Limitations
- Laboratory-based surveillance may under-ascertain cases and lacks detailed patient-level clinical and antifungal exposure data.
- Unmeasured confounding from pandemic-related care changes (ICU utilization, device use) cannot be fully excluded.
Future Directions: Link laboratory surveillance with patient-level clinical data to model risk factors and outcomes; evaluate targeted IPC bundles and antifungal stewardship interventions to curb C. auris transmission.
The COVID-19 pandemic coincided with rising secondary bloodstream infections (BSIs) from multidrug-resistant organisms, including Candida auris. To assess candidemia trends, we conducted a retrospective analysis of blood culture isolates from public and private laboratories in South Africa taken during January 2019-June 2022. We evaluated weekly aggregated Candida BSI counts and COVID-19 cases using segmented regression within an interrupted time-series framework. In total, 15,393 candidemia cases were identified, 70%
2. DDRL:Dyna-Based Discriminative Reinforcement Learning for Optimizing Sepsis Treatment Pathways in Offline Environments.
DDRL integrates simulated trajectories with real EMR data and uses a discriminator to suppress out-of-distribution actions, reducing Q-value overestimation in offline RL. Across two hospitals, DDRL improved expected returns over CQL and physician policies while achieving higher cosine similarity to clinicians’ actions.
Impact: Addresses a core failure mode of offline RL in healthcare (OOD overestimation) and demonstrates policy improvement without unsafe exploration, advancing AI translation to sepsis care.
Clinical Implications: If prospectively validated, DDRL-informed decision support could propose treatment actions consistent with clinician practice yet potentially more effective, aiding standardized sepsis care under resource constraints.
Key Findings
- DDRL combined EMR data with simulated episodes to mitigate limited exploration and employed a discriminator to suppress Q-values for out-of-distribution treatments.
- Expected return improved versus CQL by 3.4% (Asan) and 5.6% (Ajou), and exceeded physician policy by 18.7% and 8.3%, respectively.
- Policy cosine similarity to physicians reached 81.68% (Asan) and 90.90% (Ajou), surpassing CQL by 0.73% and 26.11%.
Methodological Strengths
- Hybrid Dyna framework leveraging both real EMR trajectories and model-generated simulations.
- Explicit OOD mitigation via discriminator to reduce Q-value overestimation; multi-institution evaluation.
Limitations
- Retrospective offline evaluation without prospective clinical deployment; expected return is an offline estimate.
- Generalizability beyond two Korean centers and robustness to dataset shift require further validation.
Future Directions: Prospective, interventional trials comparing DDRL-guided recommendations versus standard care; fairness, safety, and interpretability assessments across diverse ICUs.
Optimizing automated sepsis treatment policies using Reinforcement Learning (RL) has gained attention for improving quality of medical care and address physician shortages. However, in an offline setting, an RL agent cannot explore all possible treatment episodes, leading to an overestimation of Q-values for unexplored treatments. This causes a significant deviation from the physician's policy and results in the RL policy converging to a suboptimal policy. To address this problem, we propose Dyna-Based Discriminative Reinforcement Learning (DDRL), which aims to learn an optimal treatment policy that aligns with physician treatment policy. Our method utilizes both Electronic Medical Record (EMR) data and simulated treatment episodes to mitigate the limitations of restricted treatment exploration. Additionally, by leveraging a Discriminator, we suppress the Q-values of out-of-distribution treatments, preventing overestimation and reducing deviation from the physician treatment policies. The method was evaluated using data from Ajou University Hospital and Asan Medical Center. The expected return of the DDRL policy was 7.29 for Asan Medical Center and 4.55 for Ajou University Hospital, outperforming the Conservative Q-Learning (CQL) method by 3.4% and 5.6%, and surpassing the physician's policy by 18.7% and 8.3% respectively. The cosine similarity between DDRL and physician policies was 81.68% for Asan Medical Center and 90.90% for Ajou University Hospital, which is 0.73% and 26.11% higher, respectively, than the CQL method.
3. NIR-responsive prussian Blue/Fer-1 co-loaded nanozymes for synergistic therapy of acute lung injury.
An NIR-responsive Prussian blue/Fer-1 co-loaded nanozyme achieved synergistic ROS scavenging and ferroptosis inhibition, improving survival and lung function in sepsis-related ALI models. Transcriptomics implicated modulation of the CXCL2/ARF6/NCF1 axis as a mechanistic basis.
Impact: Introduces a precise, dual-modality nanotherapy with mechanistic validation for sepsis-associated ALI, opening a translational avenue where conventional options are limited.
Clinical Implications: Suggests a therapeutic strategy combining ferroptosis inhibition and ROS scavenging for inflammatory lung injury; clinical translation will require safety, dosing, and delivery studies in humans.
Key Findings
- Fer-1@PB nanozymes provided NIR-triggered, spatiotemporally controlled release, combining ROS scavenging and ferroptosis inhibition.
- In sepsis-related ALI models, treatment increased survival, reduced pulmonary edema and histopathologic damage, and restored lung function.
- RNA-seq and validation implicated modulation of the CXCL2/ARF6/NCF1 signaling axis as a mechanistic contributor.
Methodological Strengths
- Multi-tier validation across in vitro and in vivo ALI models with functional outcomes and survival.
- Mechanistic dissection via transcriptomics (RNA-seq) with experimental confirmation.
Limitations
- Preclinical nature limits direct generalization to humans; long-term safety and biodistribution under NIR exposure remain to be established.
- Manufacturing scalability, regulatory pathways, and clinical delivery to injured lung tissue require further development.
Future Directions: Pharmacokinetic, toxicology, and dose-ranging studies in large animals; evaluation in clinically relevant infection models; early-phase human safety trials.
Sepsis-associated acute lung injury (ALI) is a critical clinical problem owing to the complex interactions between inflammation and oxidative stress. It is often associated with high mortality rates and a lack of therapeutic options. To simultaneously combat the interconnected inflammatory and oxidative stress pathways in sepsis-associated ALI, we designed near-infrared (NIR)-responsive nanomedicine by co-loading ferroptosis inhibitor Ferrostatin-1 (Fer-1) with Prussian blue nanozymes (denoted as Fer-1@PB) to achieve synergistic therapy and enhanced efficacy. Fer-1@PB combines the multi-enzyme mimetic properties of PB nanoparticle-based nanozyme (PBzyme) for reactive oxygen species (ROS) scavenging and the ferroptosis-inhibitory function of Fer-1 with spatiotemporally controlled burst release under NIR irradiation. The 0.05), and the conclusion of colloidal stability remains completely unchanged. All the above revisions are limited to typographical adjustments, wording accuracy, and minor typo corrections. No experimental data, results, figures, or cistance. Sincerely, Xiaoran Liu (on behalf of all authors) Corresponding author"?> Fer-1@PB nanozymes exhibited strong therapeutic efficacy in cellular and animal sepsis-related ALI models, with an increased survival rate, reduced pulmonary edema and histopathological damage, and recovery of lung function. Mechanistic investigations using transcriptomic analysis (RNA-seq) and experimental validation revealed that the therapeutic effects were mediated by the modulation of the CXCL2/ARF6/NCF1 signaling axis. This study introduces Fer-1@PB as a highly efficient and biosafe nanotherapeutic approach for ALI via synergistic anti-oxidative and anti-inflammatory effects. It also outlines a new molecular mechanism of action, yielding new insights into the precise nanomedicine for inflammatory lung disease.