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In vivo surface-enhanced Raman scattering techniques: nanoprobes, instrumentation, and applications
Surface-enhanced Raman scattering (SERS) has emerged as a powerful tool in various biomedical applications, including in vivo imaging, diagnostics, and therapy, largely due to the development of near-infrared (NIR) active SERS substrates. This review provides a comprehensive overview of SERS-based applications in vivo, focusing on key aspects such as the design considerations for SERS nanoprobes and advancements in instrumentation. Topics covered include the development of NIR SERS substrates, Raman label compounds (RLCs), protective coatings, and the conjugation of bioligands for targeted imaging and therapy. The review also discusses microscope-based configurations such as scanning, widefield imaging, and fiber-optic setups. Recent advances in using SERS nanoprobes for in vivo sensing, diagnostics, biomolecule screening, multiplex imaging, intraoperative guidance, and multifunctional cancer therapy are highlighted. The review concludes by addressing challenges in the clinical translation of SERS nanoprobes and outlines future directions, emphasizing opportunities for advancing biomedical research and clinical applications.
Light-matter coupling via quantum pathways for spontaneous symmetry breaking in van der Waals antiferromagnetic semiconductors
Light-matter interaction simultaneously alters both the original material and incident light. Light not only reveals material details but also activates coupling mechanisms. The coupling has been demonstrated mechanically, for instance, through the patterning of metallic antennas, resulting in the emergence of plasmonic quasiparticles and enabling wavefront engineering of light via the generalized Snell’s law. However, quantum-mechanical light-matter interaction, wherein photons coherently excite distinct quantum pathways, remains poorly understood. Here, we report on quantum interference between light-induced quantum pathways through the orbital quantum levels and spin continuum. The quantum interference immediately breaks the symmetry of the hexagonal antiferromagnetic semiconductor FePS3. Below the Néel temperature, we observe the emergence of birefringence and linear dichroism, namely, quantum anisotropy due to quantum interference, which is further enhanced by the thickness effect. We explain the direct relevance of the quantum anisotropy to a quantum phase transition by spontaneous symmetry breaking in Mexican hat potential. Our findings suggest material modulation via selective quantum pathways through quantum light-matter interaction.
Low-power Spiking Neural Network audio source localisation using a Hilbert Transform audio event encoding scheme
Sound source localisation is used in many consumer devices, to isolate audio from individual speakers and reject noise. Localization is frequently accomplished by “beamforming”, which combines phase-shifted audio streams to increase power from chosen source directions, under a known microphone array geometry. Dense band-pass filters are often needed to obtain narrowband signal components from wideband audio. These approaches achieve high accuracy, but narrowband beamforming is computationally demanding, and not ideal for low-power IoT devices. We introduce a method for sound source localisation on arbitrary microphone arrays, designed for efficient implementation in ultra-low-power spiking neural networks (SNNs). We use a Hilbert transform to avoid dense band-pass filters, and introduce an event-based encoding method that captures the phase of the complex analytic signal. Our approach achieves high accuracy for SNN methods, comparable with traditional non-SNN super-resolution beamforming. We deploy our method to low-power SNN inference hardware, with much lower power consumption than super-resolution methods. We demonstrate that signal processing approaches co-designed with spiking neural network implementations can achieve much improved power efficiency. Our Hilbert-transform-based method for beamforming can also improve the efficiency of traditional digital signal processing.
Nanoplasmonic SERS on fidget spinner for digital bacterial identification
Raman spectroscopy offers non-destructive and highly sensitive molecular insights into bacterial species, making it a valuable tool for detection, identification, and antibiotic susceptibility testing. However, achieving clinically relevant accuracy, quantitative data, and reproducibility remains challenging due to the dominance of bulk signals and the uncontrollable heterogeneity of analytes. In this study, we introduce an innovative diagnostic tool: a plasmonic fidget spinner (P-FS) incorporating a nitrocellulose membrane integrated with a metallic feature, referred to as a nanoplasmonic-enhanced matrix, designed for simultaneous bacterial filtration and detection. We developed a method to fabricate a plasmonic array patterned nitrocellulose membrane using photolithography, which is then integrated with a customized fidget spinner. Testing the P-FS device with various bacterial species (E. coli 25922, S. aureus 25923, E. coli MG1655, Lactobacillus brevis, and S. mutans 3065) demonstrated successful identification based on their unique Raman fingerprints. The bacterial interface with regions within the plasmonic array, where the electromagnetic field is most intensely concentrated—called nanoplasmonic hotspots—on the P-FS significantly enhances sensitivity, enabling more precise detection. SERS intensity mappings from the Raman spectrometer are transformed into digital signals using a threshold-based approach to identify and quantify bacterial distribution. Given the P-FS’s ability to enhance vibrational signatures and its scalable fabrication under routine conditions, we anticipate that nanoplasmonic-enhanced Raman spectroscopy—utilizing nanostructures made from metals (specifically gold and silver) deposited onto a nitrocellulose membrane to amplify Raman scattering signals—will become the preferred technology for reliable and ultrasensitive detection of various analytes, including those crucial to human health, with strong potential for transitioning from laboratory research to clinical applications.
GenAI synthesis of histopathological images from Raman imaging for intraoperative tongue squamous cell carcinoma assessment
The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis. This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images, which are then transformed into hematoxylin-eosin (H&E)-stained histopathological images using an AI generative model for histopathological diagnosis. The generated H&E-stained images clearly illustrate the tissue’s pathological conditions. Independently reviewed by three pathologists, the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%. Notably, it outperforms current clinical practices, especially in TSCC with positive lymph node metastasis or moderately differentiated grades. This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations, promising a versatile diagnostic tool beyond TSCC.
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