SERS on analyte-enriched blood for rapid, culture-free sepsis recognition and causative pathogen identification with super operational neural networks

SERS on analyte-enriched blood for rapid, culture-free sepsis recognition and causative pathogen identification with super operational neural networks

<p dir="ltr">Sepsis remains a leading cause of morbidity and mortality, yet routine diagnostics are slow, culture-dependent, and often lack the sensitivity or specificity required for early intervention. Prior studies rarely demonstrate clinical-grade performance on blood culture sam...

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Main Author: Manal Hassan (458628) (author)
Other Authors: Md Sakib Bin Islam (22804121) (author), Sakib Mahmud (15302404) (author), Mahmoud Elgamal (10011975) (author), Md. Shaheenur Islam Sumon (22045907) (author), Ali Ait Hssain (11264358) (author), Emad Ibrahim (3567056) (author), Amit Kumar (81180) (author), Yiping Zhao (250264) (author), Saad Bin Abul Kashem (17773188) (author), Mehmet Burcin Unlu (4442374) (author), Gozde Durmus (22933465) (author), Muhammad E.H. Chowdhury (17151154) (author), Susu M. Zughaier (14151987) (author)
Published: 2025
Subjects:
Engineering
Biomedical engineering
Health sciences
Health services and systems
Rapid sepsis recognition
Culture-free pathogen detection
Surface-enhanced Raman spectroscopy (SERS)
Super generative operational neural networks (Super-ONNs)
SuperRamanNet
SERS-DLAnalyte-enriched blood samples
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