Quantification of Candida spp. using fluorescence and SERS spectroscopy for bloodstream infection diagnosis.

Bloodstream infections caused by Candida spp. are among the leading hospital-acquired infections, but their diagnosis remains slow and challenging with conventional culture-based methods, which often require days to deliver results. This study aimed to develop a rapid and sensitive diagnostic strategy for the detection and quantification of Candida spp. directly from blood samples. We designed a workflow combining antibody-modified magnetic beads for pathogen isolation, magnetic SERS (surface-enhanced Raman scattering)-encoded tags for multiplexed detection, and fluorescence microscopy for rapid prescreening. Data analysis was automated using machine learning, including convolutional neural networks for image classification and self-organizing maps for spectral analysis. This method enabled the detection and quantification of seven clinically relevant Candida species (C. albicans, C. glabrata, C. tropicalis, C. auris, C. haemulonii, C. dubliniensis, and C. parapsilosis) in 7.5 mL of whole blood at septicemia-relevant concentrations as low as 2 CFU mL-1, with the results obtained in 4-5 hours. High specificity was demonstrated, with minimal cross-reactivity against bacterial controls. This integrated approach represents a rapid, sensitive, and multiplex alternative to current diagnostics, with the potential to improve the early detection and targeted treatment of candidemia, thereby enhancing the clinical outcomes and reducing the healthcare burden.