Recent advances in the biosensing platforms for sepsis diagnosis

Area of Science:

• Biomedical Engineering
• Infectious Disease Diagnostics
• Sensor Technology

Background:

• Sepsis causes significant global mortality and organ damage, highlighting the urgent need for timely diagnosis and treatment.
• Existing pathogen detection methods vary in sensitivity and limit of detection (LOD), with challenges in blood biomarker extraction and preservation.
• Resource-limited settings require accessible, low-energy diagnostic tools, such as paper-based or microfluidic devices.

Purpose of the Study:

• To comprehensively review recent advancements and challenges in sepsis-related biosensors.
• To discuss various biosensor categories, biomarkers, LODs, and sensing mechanisms for sepsis detection.
• To explore the impact of artificial intelligence and machine learning on sepsis diagnostics.

Main Methods:

• Review of current literature on sepsis biosensors, encompassing neonatal sepsis.
• Analysis of six categories of sepsis biosensors, detailing biomarkers, LODs, and sensing mechanisms.
• Discussion of challenges in blood biomarker detection and preservation.

Main Results:

• Sepsis biosensors offer diverse sensing mechanisms, impacting reliability and durability.
• Key performance metrics include sensitivity and limit of detection (LOD), crucial for early diagnosis.
• Emerging AI and machine learning techniques are enhancing diagnostic processes for sepsis.

Conclusions:

• Advancements in biosensor technology are crucial for improving sepsis detection rates and patient outcomes.
• Optimizing sensing mechanisms and addressing biomarker challenges are vital for developing robust sepsis diagnostic tools.
• Integrating AI and machine learning holds promise for innovative and efficient sepsis diagnosis.