SARS-CoV-2 severity classification from plasma sample using confocal Raman spectroscopy.

Area of Science:

• Biomedical Spectroscopy
• Medical Diagnostics
• Biochemistry

Background:

• The ongoing threat of coronavirus disease (COVID-19) necessitates improved diagnostic tools.
• Current methods like RT-PCR and clinical parameters are insufficient for accurately assessing COVID-19 patient severity.
• Rapid patient triage is crucial for managing healthcare resources during viral outbreaks.

Purpose of the Study:

• To investigate confocal Raman microspectroscopy for a rapid blood test to classify COVID-19 disease severity.
• To develop a cost-effective and efficient method for differentiating severe from non-severe COVID-19 cases.
• To identify specific biochemical markers in plasma indicative of disease severity.

Main Methods:

• Utilized confocal Raman microspectroscopy on plasma samples from 65 COVID-19 patients.
• Developed a blood test using a small volume (8 μl) of inactivated whole plasma without additional solvents.
• Applied principal component based linear discriminant analysis (PC-LDA) for spectral data analysis and classification.

Main Results:

• The developed model achieved 83.87% sensitivity, 70.60% specificity, and 76.92% classification efficiency in distinguishing severe from non-severe COVID-19.
• Identified differential expression of Raman bands associated with lipids, proteins (amide I, amide III, phenylalanine), and nucleic acids in severe cases.
• Demonstrated the potential for rapid, simple, and less expensive triage of COVID-19 patient severity.

Conclusions:

• Confocal Raman microspectroscopy is a promising tool for developing rapid blood tests for COVID-19 severity triage.
• The technique can differentiate disease severity based on biochemical profiles in plasma.
• This approach offers a potential solution for efficient patient management during future outbreaks.