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Multivariate analysis applied in biosensing greatly improves analytical performance by extracting relevant information or bypassing confounding factors such as nonlinear responses or experimental errors and noise. Plasmonic sensors based on various light coupling mechanisms have shown impressive performance in biosensing by detecting dielectric changes with high sensitivity. In this study, gold nanodiscs are used as metasurface in a Kretschmann setup, and a variety of features from the reflectance curve are used by machine learning to improve sensing performance. The nanostructures of the metasurface generate new plasmonic features, apart from the typical resonance that occurs in the classical Kretschmann mode of a gold thin film, related to the evanescent field beyond total internal reflection. When the engineered metasurface is integrated into a microfluidic chamber, the device provides additional spectral features generated by Fresnel reflections at all dielectric interfaces. The increased number of features results in greatly improved detection. Here, multivariate analysis enhances analytical sensitivity and sensor resolution by 200% and more than 20%, respectively, and reduces prediction errors by almost 40% compared to a standard plasmonic sensor. The combination of plasmonic metasurfaces and Fresnel reflections thus offers the possibility of improving sensing capabilities even in commonly available setups. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods. [ABSTRACT FROM AUTHOR]

Temperature‐modulated colloidal phase of plasmonic nanoparticles is a convenient playground for resettable soft‐actuators or colorimetric sensors. To render reversible clustering under temperature change, bulky ligands are required, especially if anisotropic morphologies are of interest. This study showcases thermoresponsive gold nanorods by employing small surface ligands, bis (p‐sulfonatophenyl) phenyl‐phosphine dihydrate dipotassium salt (BSPP) and native cationic surfactant. Temperature‐dependent analysis in real‐time allowed to describe the structural features (interparticle distance and cluster size) as well as thermal parameters, melting and freezing temperatures. These findings suggest that neither covalent Au−S bonds nor bulky ligands are required to obtain a robust thermoresponsive system based on anisotropic gold nanoparticles, paving the way to stimuli‐responsive nanoparticles with a wide range of sizes and geometries. [ABSTRACT FROM AUTHOR]

Surveillance of physiological parameters of newborns during delivery triggers medical decision‐making, can rescue life and health, and helps avoid unnecessary cesareans. Here, the development of a photonic technology for monitoring perinatal asphyxia is presented and validated in vivo in a preclinical stage. Contrary to state of the art, the technology provides continuous data in real‐time in a non‐invasive manner. Moreover, the technology does not rely on a single parameter as pH or lactate, instead monitors changes of the entirety of physiological parameters accessible by Raman spectroscopy. By a fiber‐coupled Raman probe that is in controlled contact with the skin of the subject, near‐infrared Raman spectra are measured and analyzed by machine learning algorithms to develop classification models. As a performance benchmarking, various hybrid and non‐hybrid classifiers are tested. In an asphyxia model in newborn pigs, more than 1000 Raman spectra are acquired at three different clinical phases—basal condition, hypoxia–ischemia, and post‐hypoxia–ischemia stage. In this preclinical proof‐of‐concept study, figures of merit reach 90% levels for classifying the clinical phases and demonstrate the power of the technology as an innovative medical tool for diagnosing a perinatal adverse outcome. [ABSTRACT FROM AUTHOR]

Temperature-modulated colloidal phase of plasmonic nanoparticles is a convenient playground for resettable soft-actuators or colorimetric sensors. To render reversible clustering under temperature change, bulky ligands are required, especially if anisotropic morphologies are of interest. This study showcases thermoresponsive gold nanorods by employing small surface ligands, bis (p-sulfonatophenyl) phenyl-phosphine dihydrate dipotassium salt (BSPP) and native cationic surfactant. Temperature-dependent analysis in real-time allowed to describe the structural features (interparticle distance and cluster size) as well as thermal parameters, melting and freezing temperatures. These findings suggest that neither covalent Au-S bonds nor bulky ligands are required to obtain a robust thermoresponsive system based on anisotropic gold nanoparticles, paving the way to stimuli-responsive nanoparticles with a wide range of sizes and geometries., (© 2023 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH.)