Sepsis, October 2 edition

This preclinical study developed chitosan-copper nanoclusters that selectively accumulate in bacteria and synergistically disrupt the tricarboxylic acid cycle. The resulting copper overload, impairment of lipoylated and iron-sulfur proteins, reactive oxygen species generation, and glutathione depletion produced cascading cuproptosis-like and ferroptosis-like bacterial death. The nanoclusters eradicated biofilm-embedded and multidrug-resistant bacteria and showed therapeutic efficacy in a mouse peritonitis-sepsis model.

Impact: The study proposes a distinct antimicrobial paradigm that targets bacterial central carbon metabolism rather than relying on conventional antibiotic pathways. Its mechanistic integration of copper toxicity, oxidative stress, and glutathione depletion, together with activity against multidrug-resistant bacteria in vivo, provides a strong foundation for translational development.

Clinical Implications: The platform may eventually provide an adjunct or alternative treatment for multidrug-resistant bacterial infections and sepsis, particularly infections involving biofilms. Clinical translation will require detailed evaluation of mammalian toxicity, pharmacokinetics, tissue distribution, resistance emergence, and comparative efficacy against standard antibiotics.

Future Directions: Future studies should define mammalian safety margins, pharmacokinetic and pharmacodynamic relationships, tissue penetration, optimal combination regimens, and efficacy in clinically representative polymicrobial and immunocompromised sepsis models. If these findings are confirmed, carefully designed first-in-human studies could assess safety and activity against difficult-to-treat infections.

This nationwide retrospective registry study provides the first Central-Eastern European national estimates of culture-positive early-onset neonatal sepsis and antibiotic exposure. The incidence of culture-positive early-onset sepsis was 0.41 per 1,000 live births, but 63.9% of neonates treated in neonatal intensive care units received antibiotics, generally for a median of 5 days. Escherichia coli was the leading pathogen and caused substantial mortality among affected infants.

Impact: The study quantifies the mismatch between a relatively low incidence of culture-positive early-onset sepsis and extensive empiric antibiotic exposure at a national level. These data directly support antimicrobial-stewardship interventions while identifying Escherichia coli as an important pathogen and high-risk target, particularly among preterm infants.

Clinical Implications: Neonatal units may need more refined risk-stratification and antibiotic-stewardship pathways to reduce unnecessary empiric treatment while preserving early therapy for high-risk infants. The findings also support continued surveillance of pathogen distribution and consideration of gestational-age-specific diagnostic and treatment algorithms.

Future Directions: Prospective multicenter studies should evaluate risk-based antibiotic algorithms, serial clinical reassessment, rapid diagnostics, and early discontinuation protocols. Future surveillance should also examine resistance patterns, long-term neurodevelopmental outcomes, and microbiome-related consequences of neonatal antibiotic exposure.

This systematic review synthesized nine studies evaluating prospectively deployed artificial intelligence clinical decision support systems in adult internal medicine and acute general medical care, including four sepsis studies and 110,452 patients. AI systems consistently improved process measures such as time to antibiotics and sepsis-bundle compliance, but mortality benefits were mainly reported in non-randomized studies or predefined high-risk subgroups. The review emphasizes that clinical benefit depends on alert routing and clinician response as much as on algorithmic discrimination.

Impact: This review addresses a major evidence gap between retrospective AI performance and real-world clinical effectiveness. By separating process outcomes from patient-important outcomes and highlighting implementation factors, it provides a rigorous framework for deciding when AI-based sepsis support is ready for broader adoption.

Clinical Implications: AI clinical decision support may improve recognition and treatment processes in sepsis and other acute medical conditions, but implementation should not rely on retrospective accuracy alone. Health systems should evaluate alert workflow, clinician adherence, unintended alert burden, equity, and patient-important outcomes through prospective multicenter randomized studies.

Future Directions: Future research should use multicenter randomized designs with patient-important primary outcomes, prespecified subgroup analyses, standardized reporting of alert exposure and clinician response, and evaluation of safety, equity, cost, and sustainability. Studies should also compare different alert-routing and human-in-the-loop strategies rather than treating AI deployment as a single intervention.