Diagnostics
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SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization
Artificial intelligence (AI)-driven materials discovery offers rapid design of novel material compositions, yet synthesis and characterization lag behind. Characterization, in particular, remains bottlenecked by labor-intensive experiments using expert-operated instruments that typically rely on electromagnetic spectroscopy. We introduce SpectroGen, a generative AI model for transmodality spectral generation, designed to accelerate materials characterization. SpectroGen generates high-resolution, high-signal-to-noise…
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Interplay of Electrostatic Interaction and Steric Repulsion between Bacteria and Gold Surface Influences Raman Enhancement
Plasmonic nanostructures have wide applications in photonics including pathogen detection and diagnosis via Surface-Enhanced Raman Spectroscopy (SERS). Despite major role plasmonics play in signal enhancement, electrostatics in SERS is yet to be fully understood and harnessed. Here, we perform a systematic study of electrostatic interactions between 785 nm resonant gold nanorods designed to harbor zeta…
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Polymer-coated beads serving as Raman reporter for simultaneous target binding and identification
Methods of detection are provided, comprising (i) obtaining a. sample, wherein the sample comprises target selected from biological cells and viruses; (ii) incubating the sample with polymer-coated magnetic beads to produce bead-target complexes; (iii) analyzing the bead-target complexes by Raman spectroscopy to produce a spectrograph; and (iv) detecting the presence of the bead-target complexes by…
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Universal Spectral Transfer with Physical Prior-Informed Deep Generative Learning
Spectroscopy is a powerful analytical technique for characterizing matter across physical and biological realms1-5. However, its fundamental principle necessitates specialized instrumentation per physical phenomena probed, limiting broad adoption and use in all relevant research. In this study, we introduce SpectroGen, a novel physical prior-informed deep generative model for generating relevant spectral signatures across modalities using…
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From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis
Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics–including plasmonics, metamaterials, and metasurfaces–enhance Raman scattering…
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Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy
Tuberculosis (TB) is the world’s deadliest infectious disease, with over 1.5 million deathsand 10 million new cases reported anually. The causative organism Mycobacterium tuber-culosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen’santibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testingof Mtb are essential for effective patient treatment…
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MicrobioRaman: an open-access web repository for microbiological Raman spectroscopy data
Here we present the establishment of an open-access web-based repository for microbiological Raman spectroscopy data. The data collection, called ‘MicrobioRaman’ (https://www.ebi.ac.uk/biostudies/MicrobioRaman/studies), was inspired by the great success and usefulness of research databases such as GenBank and UniProt. This centralized repository, residing within the BioStudies database1 — which is maintained by a public institution, the European Bioinformatics…
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Sharpness-Aware Minimization (SAM) Improves Classification Accuracy of Bacterial Raman Spectral Data Enabling Portable Diagnostics
Antimicrobial resistance is expected to claim 10 million lives per year by 2050, and resource-limited regions are most affected. Raman spectroscopy is a novel pathogen diagnostic approach promising rapid and portable antibiotic resistance testing within a few hours, compared to days when using gold standard methods. However, current algorithms for Raman spectra analysis 1) are…
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More than magnetic isolation: Dynabeads as strong Raman reporters toward simultaneous capture and identification of targets
Dynabeads are superparamagnetic particles used for the immunomagneticpurification of cells and biomolecules. Post-capture, however, target identifica-tion relies on tedious culturing, fluorescence staining, and/or target amplifica-tion. Raman spectroscopy presents a rapid detection alternative, but currentimplementations target cells themselves with weak Raman signals. We presentantibody-coated Dynabeads as strong Raman reporter labels whose effect canbe considered a Raman…
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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…