Diagnostic Raman spectroscopy offers a rapid approach for pathogen detection and phenotypic antimicrobial susceptibility testing. Following isolation, two common bloodstream infection bacteria, Escherichia coli and Staphylococcus aureus, were treated with appropriate antibiotic concentrations and analyzed using On-Chip Raman spectroscopy. Using photonic data analysis, the spectral signals are translated into antibiograms.
Sample size planning (SSP) is crucial for experimental planning but is not well-established for spectroscopic and image data, especially in combination with deep learning. The existing approaches are typically quite complex for routine use in experimental planning. To make the existing approaches more accessible, we developed web-based tools for the existing approaches. Besides, we extended the approach to imaging data and deep learning by introducing transfer learning in the SSP pipeline.
ACKNOWLEDGMENT:
Financial support from the EU, the TMWWDG, the TAB, the BMBF, the DFG, the Carl-Zeiss Foundation, and the Leibniz Association is greatly acknowledged. This work is supported by the BMBF, funding program Photonics Research Germany (LPI-BT3-IPHT, FKZ: 13N15708) and is integrated into the Leibniz Center for Photonics in Infection Research (LPI). The LPI initiated by Leibniz-IPHT, Leibniz-HKI, Friedrich Schiller University Jena, and Jena University Hospital is part of the BMBF national roadmap for research infrastructures.
We developed a simple and convenient magnetic bead-based sample preparation scheme for enabling a Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) positive and negative samples. By utilizing the angiotensin-converting enzyme 2 (ACE2) receptor protein as selective recognition element, we avoid having to identify the virus species based on its specific Raman signature. Instead we only need to verify the presence of the virus, which is significantly less difficult. For quantitative evaluation of the spectra, we calculated the Pearson coefficient and the Normalized Cross Correlation coefficient.
Photonic data can be used to characterize the biochemical composition of samples and often in a non-destructive and label-free manner. To utilize these label-free measurements for applications like diagnostics or analytics, data driven modeling is utilized to translate photonic data into higher-level information. In this contribution, two scenarios of data driven modeling will be presented. We will present the translation of nonlinear multi-contrast images into diagnostic information like tissue types, disease types, and histopathological stainings. Additionally, we will demonstrate deep learning as tool for the extraction of the imaginary part of the third-order susceptibility of spectral CARS measurements.
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