arXiv subjects

Amr A. E. Saleh

Publications and source records attributed to Amr A. E. Saleh.

3 recordsLinked to original sources

arXiv2023

Combining acoustic bioprinting with AI-assisted Raman spectroscopy for high-throughput identification of bacteria in blood

Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when mixed with gold nanorods (GNRs), SERS enhancements of up to 1500x are achieved.We then train a ML model and achieve >=99% classification accuracy from cellularly-pure samples, and >=87% accuracy from cellularly-mixed samples. We also obtain >=90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.

physics.bio-ph↗

arXiv2020

Electron and Light Induced Stimulated Raman Spectroscopy for Nanoscale Molecular Mapping

We propose and theoretically analyze a new vibrational spectroscopy, termed electron- and light-induced stimulated Raman (ELISR) scattering, that combines the high spatial resolution of electron microscopy with the molecular sensitivity of surface-enhanced Raman spectroscopy. With ELISR, electron-beam excitation of plasmonic nanoparticles is utilized as a spectrally-broadband but spatially-confined Stokes beam in the presence of a diffraction-limited pump laser. To characterize this technique, we develop a numerical model and conduct full-field electromagnetic simulations to investigate two distinct nanoparticle geometries, nanorods and nanospheres, coated with a Raman-active material. Our results show the significant ($10^6$-$10^7$) stimulated Raman enhancement that is achieved with dual electron and optical excitation of these nanoparticle geometries. Importantly, the spatial resolution of this vibrational spectroscopy for electron microscopy is solely determined by the nanoparticle geometry and the plasmon mode volume. Our results highlight the promise of ELISR for simultaneous high-resolution electron microscopy with sub-diffraction-limited Raman spectroscopy, complementing advances in superresolution microscopy, correlated light and electron microscopy, and vibrational electron energy loss spectroscopy.

physics.optics↗

arXiv2019

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

Rapid identification of bacteria is essential to prevent the spread of infectious disease, help combat antimicrobial resistance, and improve patient outcomes. Raman optical spectroscopy promises to combine bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to the weak Raman signal from bacterial cells and the large number of bacterial species and phenotypes. By amassing the largest known dataset of bacterial Raman spectra, we are able to apply state-of-the-art deep learning approaches to identify 30 of the most common bacterial pathogens from noisy Raman spectra, achieving antibiotic treatment identification accuracies of 99.0$\pm$0.1%. This novel approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) as well as a pair of isogenic MRSA and MSSA that are genetically identical apart from deletion of the mecA resistance gene, indicating the potential for culture-free detection of antibiotic resistance. Results from initial clinical validation are promising: using just 10 bacterial spectra from each of 25 isolates, we achieve 99.0$\pm$1.9% species identification accuracy. Our combined Raman-deep learning system represents an important proof-of-concept for rapid, culture-free identification of bacterial isolates and antibiotic resistance and could be readily extended for diagnostics on blood, urine, and sputum.

q-bio.QM↗