Raman Spectroscopic Method Diagnoses Infection at the Point of Care

Raman Spectroscopic Method Diagnoses Infection at the Point of Care

KARINA WEBER AND JÜRGEN POPP, LEIBNIZ INSTITUTE OF PHOTONIC TECHNOLOGY, wrote an article about the Raman Spectroscopic Method Diagnoses Infection at the Point of Care.

RamanBioAssay combines biophotonic measurements with machine learning and artificial intelligence analysis. Based on a measuring chip that comes preloaded with antibiotics, the device can handle samples and provide a resistogram in less than three and a half hours.One of the quickest methods for phenotypic pathogen diagnosis is provided by this spectroscopic study.

Each year, infectious diseases claim millions of lives worldwide, and the continued spread of these illnesses seriously jeopardizes the viability of public health systems. The current pandemic caused by the SARS-CoV-2 virus has once again demonstrated how quickly health care institutions can become overwhelmed when an infection spreads quickly. Furthermore, it is frequently challenging to administer medication to patients in an effective manner due to the sharp rise in the number of pathogens that are resistant to drugs. The development of noninvasive optical technologies like Raman spectroscopy may be essential for prompt diagnosis and individualized patient care, which will help curb the spread of infectious diseases.

Benefits:

•Phenotypic resistance testing to optimize clinical relevance

•Simple, automated sample preparation

•Less than 3.5 hours for results

•Quick diagnostics for targeted and customized therapy

•Cheap, disposable chip system that doesn't require reagent preparation

 •Few manual steps for low error risk

• RFID sample identification for traceability and transparency
• Laboratory information management system (LIMS) connection

Pre-processing that is standardized, traceable, and repeatable guarantees robust and high-quality models. Building a model is simple and offers customization options. Principal component analysis (PCA) or convolutional neural networks (CNN) can be used to reduce the dimensionality. And cross-validation techniques are used to confirm the accuracy.(AH)