arXiv subjects

Elodie Ghedin

Publications and source records attributed to Elodie Ghedin.

3 recordsLinked to original sources

arXiv2025

Machine Learning Integrated Near-Infrared Surface-Enhanced Raman Spectroscopy for Accurate Strain-Level Virus Identification

Strain-level identification of viruses is critical for effective public health responses to potential outbreaks, yet current diagnostic methods often lack the necessary speed or sensitivity. Surface-enhanced Raman spectroscopy (SERS) offers great potential for fast and precise virus clarification through unique vibrational fingerprints of biological components. However, existing protocols typically operate outside of the tissue's transparent near-infrared (NIR) window, and are further limited by the intrinsic complexity of clinical viral samples, which complicates spectral analysis and recognition. Here, we report an artificial intelligence (AI)-empowered NIR-SERS platform that integrates machine learning with a rationally designed hybrid substrate: gold nanostars (AuNSt) coupled with gold-coated carbon nanotube arrays (AuCNT). This architecture generates highly localized plasmonic hot spots resonant tuned to NIR excitation, as confirmed by electron energy-loss spectroscopy (EELS), enabling effective signal amplification from viral components. Our system and protocols provide accurate classification of respiratory viruses, including influenza viruses and coronaviruses, not only at the type and subtype levels, but also the more challenging strain level. This approach overcomes the plasmonic mismatch in conventional SERS and the lack of generalizability in AI-driven diagnostics. It shows promise for enhancing rapid virus detection and identification of novel strains and outbreak response capabilities, thus potentially addressing critical challenges in global public health preparedness.

physics.chem-ph↗

arXiv2022

Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such a machine learning approach for analyzing Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and non-enveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups (for example, amide, amino acid, carboxylic acid), we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.

q-bio.QM↗

arXiv2005

Mimivirus Relatives in the Sargasso Sea

The discovery and genome analysis of Acanthamoeba polyphaga Mimivirus, the largest known DNA virus, challenged much of the accepted dogma regarding viruses. Its particle size (>400 nm), genome length (1.2 million bp) and huge gene repertoire (911 protein coding genes) all contribute to blur the established boundaries between viruses and the smallest parasitic cellular organisms. Phylogenetic analyses also suggested that the Mimivirus lineage could have emerged prior to the individualization of cellular organisms from the three established domains, triggering a debate that can only be resolved by generating and analyzing more data. The next step is then to seek some evidence that Mimivirus is not the only representative of its kind and determine where to look for new Mimiviridae. An exhaustive similarity search of all Mimivirus predicted proteins against all publicly available sequences identified many of their closest homologues among the Sargasso Sea environmental sequences. Subsequent phylogenetic analyses suggested that unknown large viruses evolutionarily closer to Mimivirus than to any presently characterized species exist in abundance in the Sargasso Sea. Their isolation and genome sequencing could prove invaluable in understanding the origin and diversity of large DNA viruses, and shed some light on the role they eventually played in the emergence of eukaryotes.

q-bio.PE↗