Advances in deep learning-based applications for Raman spectroscopy analysis: A mini-review of the progress and challenges

Microchemical Journal, Journal Year: 2025, Volume and Issue: 209, P. 112692 - 112692

Published: Jan. 5, 2025

Language: Английский

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Advancing precision cancer immunotherapy drug development, administration, and response prediction with AI-enabled Raman spectroscopy

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 9, 2025

Molecular characterization of tumors is essential to identify predictive biomarkers that inform treatment decisions and improve precision immunotherapy development administration. However, challenges such as the heterogeneity patient responses, limited efficacy current biomarkers, predominant reliance on single-omics data, have hindered advances in accurately predicting outcomes. Standard therapy generally applies a "one size fits all" approach, which not only provides ineffective or but also an increased risk off-target toxicities acceleration resistance mechanisms adverse effects. As emerging multi- spatial-omics platforms continues evolve, effective tumor assessment platform providing utility clinical setting should i) enable high-throughput robust screening variety biological matrices, ii) provide in-depth information resolved with single subcellular precision, iii) accessibility economical point-of-care settings. In this perspective, we explore application label-free Raman spectroscopy profiling tool for immunotherapy. We examine how spectroscopy's non-invasive, approach can deepen our understanding intricate inter- intra-cellular interactions within tumor-immune microenvironment. Furthermore, discuss analytical spectroscopy, highlighting its evolution be utilized "Raman-omics" approach. Lastly, highlight translational potential integration practice safe precise patient-centric

Language: Английский

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Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(45)

Published: Oct. 29, 2024

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail unmixing signals from mixtures molecular species identify individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered practice. Here, we develop hyperspectral algorithms based on autoencoder neural networks, systematically validate them using both synthetic experimental benchmark datasets created in-house. Our results demonstrate that autoencoders provide improved accuracy, robustness, efficiency compared standard methods. We also showcase applicability biological settings by showing biochemical characterization volumetric imaging data monocytic cell.

Language: Английский

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Raman Spectroscopy for Cell Analysis: Retrospect and Prospect

Talanta, Journal Year: 2024, Volume and Issue: 285, P. 127283 - 127283

Published: Nov. 26, 2024

Language: Английский

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Integrating surface‐enhanced Raman scattering with machine learning: Pioneering a comprehensive diagnostic and therapeutic platform for cancer management

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: May 11, 2025

Abstract The integration of Surface‐Enhanced Raman Scattering (SERS) with machine learning heralds a transformative era in cancer management, offering non‐invasive, expedited, and comprehensive approach for early diagnosis, targeted therapy, continuous monitoring. As SERS penetrates the molecular intricacies cancerous tissues, its conjunction advanced algorithms enhances diagnostic accuracy, enabling discernment subtle biochemical cues critical early‐stage detection precise therapeutic targeting, holds promise establishing systematic platform from diagnosis to therapy. This review explores synergistic potential these technologies advocating their expanded application across spectra images revolutionize landscape cancer. By harnessing this integrated approach, we propose development an intelligent that promises refine thereby redefining oncological diagnostics care.

Language: Английский

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Machine Learning-Based Two-Dimensional Ultraviolet Spectroscopy for Monitoring Protein Structures and Dynamics

Processes, Journal Year: 2025, Volume and Issue: 13(2), P. 290 - 290

Published: Jan. 21, 2025

Two-dimensional ultraviolet (2DUV) spectroscopy is an emerging spectroscopic technique that offers high resolution and detailed insights into protein structures. However, traditional theoretical calculations of 2DUV spectra for proteins are computationally expensive due to their complex flexible In this study, we developed a machine learning (ML)-based approach the rapid accurate prediction spectra. The results demonstrate that, compared one-dimensional (1DUV) spectroscopy, provides higher structural characterization effectively monitors dynamic processes such as mutations, aggregation, folding. This not only cost-effective ML-based solution predicting but also serves powerful tool studying structures dynamics, with potential applications in understanding mechanisms regulating functions.

Language: Английский

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Advances of functional graphdiyne in separation and detection

Talanta, Journal Year: 2025, Volume and Issue: 287, P. 127673 - 127673

Published: Jan. 30, 2025

Language: Английский

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Photolithographic 3D SERS substrate via in-situ mineralization for optical anti-counterfeiting and ultra-trace analysis

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 160399 - 160399

Published: Feb. 1, 2025

Language: Английский

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How can surface-enhanced Raman spectroscopy improve diagnostics for bacterial infections?

Nanomedicine, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 6

Published: Feb. 17, 2025

Currently, bacterial infection is still a major global health issue. Although antibiotics have been widely used to control and treat infections, the overuse misuse of led widespread antimicrobial resistance among many pathogens. Therefore, reducing infections through rapid accurate diagnostics crucial for public health. Traditional microbiological detection methods limitations such as poor selectivity, high complexity, excessive time consumption, highlighting urgent need develop efficient sensitive diagnosis methods. Surface-enhanced Raman spectroscopy (SERS), an emerging technique in clinical settings, holds promising future identification due its rapid, nondestructive, cost-effective nature. This invited special report discusses application SERS technology using pure culture, samples, single-cell analysis. Current challenges prospects are also addressed with in-depth discussion.

Language: Английский

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The times they are AI-changing: AI-powered advances in the application of extracellular vesicles to liquid biopsy in breast cancer

Extracellular Vesicles and Circulating Nucleic Acids, Journal Year: 2025, Volume and Issue: 6(1), P. 128 - 40

Published: Feb. 28, 2025

Artificial intelligence (AI) is revolutionizing scientific research by facilitating a paradigm shift in data analysis and discovery. This transformation characterized fundamental change methods concepts due to AI’s ability process vast datasets with unprecedented speed accuracy. In breast cancer research, AI aids early detection, prognosis, personalized treatment strategies. Liquid biopsy, noninvasive tool for detecting circulating tumor traits, could ideally benefit from analytical capabilities, enhancing the detection of minimal residual disease improving monitoring. Extracellular vesicles (EVs), which are key elements cell communication progression, be analyzed identify disease-specific biomarkers. combined EV promises an enhancement diagnosis precision, aiding Studies show that can differentiate types predict drug efficacy, exemplifying its potential medicine. Overall, integration biomedical clinical practice significant changes advancements diagnostics, medicine-based approaches, our understanding complex diseases like cancer.

Language: Английский

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