Multimodal Raman-Gene Framework for Interpretable Phenotypic Antibiotic Resistance and Health Risk Stratification

Area of Science:

• Microbiology
• Spectroscopy
• Bioinformatics

Background:

• Antibiotic resistance poses a significant clinical challenge due to slow, culture-based testing methods.
• Conventional antimicrobial susceptibility testing (AST) lacks insights into resistance dissemination and pathogenicity.
• Rapid diagnostics are crucial for effective clinical decision-making and infection control.

Purpose of the Study:

• To develop a culture-free strategy for rapid phenotypic antibiotic resistance profiling.
• To enable direct resistance risk assessment of antibiotic-resistant bacteria in clinical samples.
• To integrate phenotypic and genetic data for comprehensive bacterial characterization.

Main Methods:

• A multimodal Raman-gene-deep learning approach was developed.
• Surface-enhanced Raman spectroscopy (SERS) captured phenotypic fingerprints.
• Targeted genetic information on resistance genes and virulence factors was integrated.
• The method was applied directly to clinical urine samples without bacterial culture.

Main Results:

• Accurate phenotypic resistance results were obtained in approximately 2.5 hours.
• The approach successfully characterized antibiotic resistance directly from urine samples.
• Health risk assessment, including clinical impact and pathogenicity, was simultaneously provided.
• The method demonstrated reduced operational complexity compared to traditional AST.

Conclusions:

• A novel culture-free framework for rapid phenotypic resistance prediction and risk assessment was established.
• This approach offers enhanced informational depth beyond conventional AST.
• The technology holds potential for improving antimicrobial decision-making and infection control strategies.