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The aim of the study was to examine the biochemical and structural changes occurring in the periodontal ligament (PDL) during orthodontic-force application using micro-Raman spectroscopy ( μ -RS). Adolescent and young patients who needed orthodontic treatment with first premolar extractions were recruited. Before extractions, orthodontic forces were applied using a closed-coil spring that was positioned between the molar and premolar. Patients were randomly divided into three groups, whose extractions were performed after 2, 7, and 14 days of force application. From the extracted premolars, PDL samples were obtained, and a fixation procedure with paraformaldehyde was adopted. Raman spectra were acquired for each PDL sample in the range of 1000–3200 cm − 1 and the more relevant vibrational modes of proteins (Amide I and Amide III bands) and CH 2 and CH 3 modes were shown. Analysis indicated that the protein structure in the PDL samples after different time points of orthodontic-force application was modified. In addition, changes were observed in the CH 2 and CH 3 high wavenumber region due to local hypoxia and mechanical force transduction. The reported results indicated that μ -RS provides a valuable tool for investigating molecular interchain interactions and conformational modifications in periodontal fibers after orthodontic tooth movement, providing quantitative insight of time occurring for PDL molecular readjustment.

Theory Theory of Coherent Raman Scattering, Eric Olaf Potma and Shaul MukamelCoherent Raman Scattering under Tightly Focused Conditions, Eric Olaf Potma, Xiaoliang Sunney Xie, Andreas Volkmer, and Ji-Xin ChengPlatformsConstruction of a Coherent Raman Microscope, Brian G. Sarr and Xiaoliang Sunney XieStimulated Raman Scattering Microscopy, Christian Freudiger and Xiaoliang Sunney XieFemtosecond versus Picosecond Pulses for Coherent Raman Microscopy, Mikhail N. Slipchenko, Delong Zhang, and Ji-Xin ChengWide-Field CARS Microscopy, Alexander Jesacher, Gregor Thalhammer, Stefan Bernet, and Monika Ritsch-MarteVibrational Spectromicroscopy by Coupling Coherent Raman Imaging with Spontaneous Raman Spectral Analysis, Mikhail N. Slipchenko and Ji-Xin ChengCoherent Control in CARS, Jonathan M. Levitt, Ori Katz, and Yaron SilberbergFourier Transform CARS Microscopy, Jennifer P. OgilvieCRS with Alternative Beam Profiles, Varun Raghunathan, Hyunmin Kim, Stephan Stranick, and Eric Olaf PotmaVibrational Phase Microscopy, Martin Jurna, Cees Otto, and Herman L. OfferhausMultiplex CARS Microscopy, James P.R. Day, Katrin F. Domke, Gianluca Rago, Erik M. Vartiainen, and Mischa BonnInterferometric Multiplex CARS, Sang-Hyun LimPhotonic Crystal Fiber-Based Broadband CARS Microscopy, Marcus T. Cicerone, Young Jong Lee, Sapun H. Parekh, and Khaled A. AamerMultiplex Stimulated Raman Scattering Microscopy, Dan Fu and Xiaoliang Sunney XieApplicationsImaging Myelin Sheath Ex Vivo and In Vivo by CARS Microscopy, Yan Fu, Yunzhou (Sophia) Shi, and Ji-Xin ChengImaging Lipid Metabolism in Caenorhabditis elegans and Other Model Organisms, Helen Fink, Christian Brackmann, and Annika EnejderLipid-Droplet Biology and Obesity-Related Health Risks, Thuc T. LeWhite Matter Injury: Cellular-Level Myelin Damage Quantification in Live Animals, Erik Bélanger, F.P. Henry, R. Vallée, M.A. Randolph, I.E. Kochevar, J.M. Winograd, Charles P. Lin, and Daniel CôtéCARS Microscopy Study of Liquid Crystals, Heung-Shik Park and Oleg D. LavrentovichLive Cell Imaging by Multiplex CARS Microspectroscopy, Hideaki Kano Coherent Raman Scattering Imaging of Drug Delivery Systems, Ling Tong and Ji-Xin ChengApplications of Stimulated Raman Scattering Microscopy, Christian Freudiger, Daniel A. Orringer, and Xiaoliang Sunney XieApplications of Coherent Anti-Stokes Raman Spectroscopy Imaging to Cardiovascular Diseases, Han-Wei Wang, Michael Sturek, and Ji-Xin ChengApplications of CARS Microscopy to Tissue Engineering, Annika Enejder and Christian BrackmannDietary Fat Absorption Visualized by CARS Microscopy, Kimberly K. BuhmanIndex.

The influence of a topically applied formulation containing components of natural moisturizing factor (NMF) on barrier-related parameters of the stratum corneum (SC) was investigated in vivo using confocal Raman microspectroscopy in a randomized, placebo-controlled double-blind study on 12 volunteers for 14 days. This method allowed for the elucidation of subtle differences between the verum and the placebo even though the components of the verum naturally occur in the SC. This differentiation is not possible non-invasively by conventional methods. In this study, we found that the applied verum and placebo formulations disrupted the equilibrium of water, NMF and lipids in the SC. The adverse effects of the formulation could be mitigated by incorporating it into a simplified supplementation of NMF molecules. As a long-term effect, the amount of strongly bound water increases at 30–40% SC depth (p < 0.05) and the amount of weakly bound water decreases at 30–40% SC depth (p < 0.05) for the verum. This supplement was also unexpectedly able to prevent intercellular lipids (ICL) disorganization in selected depths. In the long term, the verum treatment limited the lateral disorganization of the ICL to the upper 20% SC depth. Further research is required to elucidate the interplay of these factors in the SC, to better understand their contribution to the equilibrium and barrier function of the skin. This understanding of the interaction of these naturally occurring components could help in the future to develop and optimize topical treatments for diseases like psoriasis, atopic dermatitis, ichthyosis where the skin barrier is disrupted.

Introduction Ceramides are essential epidermal constituents that play a critical role in skin moisturization treatment as a raw material in cosmetics formulation. Recently, ceramides have been known to be frequently applied in various cosmetic formulations. Despite ceramide's beneficial characteristics, academic research regarding ceramides and their skin absorption remains insufficient. Therefore, our study conducted clinical research employing Raman spectroscopy to investigate the effects of ceramides on skin absorption to enhance the understanding of ceramides’ dermatological functionality and their topical application in cosmetics science. Materials and Methods Twenty healthy individuals with dry skin have participated in this clinical trial. In this double‐arm designed trial, the test group received an investigational product with ceramides (5000 ppm) and a control group received an investigational product without the ceramides while all other components remained identical. The subjects visited the clinical research center and acclimatized for 30 min in constant humidity and temperature for equilibrium, subsequently conducting a measurement. Before the trial, the research subject's target site (lower arm area) was kept clean, devoid of any cosmetic administering 24 h before the trial when investigational product was topically applied. Results Our findings with Raman spectroscopy statistically demonstrate that skin absorption amount, speed and depth for both groups improved overall (p < 0.05) after administration of the investigational product. Notably, the test group received an investigational product with ceramides (5000 ppm) indicating superior effectiveness across all parameters compared to a control group from comparison analysis of each parameter (p < 0.05). Conclusion This study concludes that ceramide‐containing cosmetics provide a beneficial effect on skin absorption via visual and statistical results of Raman spectroscopy analysis.

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning–based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications. Raman spectroscopy is increasingly being used in biological assays and studies. This protocol provides guidance for performing chemometric analysis to detect and extract information relating to the chemical differences between biological samples.

Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases. Early detection of multiple cancers through a single method could be clinically important. Here the authors report the diagnostic performance for early detection for multiple cancers using surface-enhanced Raman spectroscopy (SERS) profiles of exosomes from a single blood test and artificial intelligence in a retrospective study design.

Raman microspectroscopy is useful for the analysis of biological samples, because chemical and structural information can be obtained without using labels. This protocol brings together practical guidelines from expert research groups. Raman spectroscopy can be used to measure the chemical composition of a sample, which can in turn be used to extract biological information. Many materials have characteristic Raman spectra, which means that Raman spectroscopy has proven to be an effective analytical approach in geology, semiconductor, materials and polymer science fields. The application of Raman spectroscopy and microscopy within biology is rapidly increasing because it can provide chemical and compositional information, but it does not typically suffer from interference from water molecules. Analysis does not conventionally require extensive sample preparation; biochemical and structural information can usually be obtained without labeling. In this protocol, we aim to standardize and bring together multiple experimental approaches from key leaders in the field for obtaining Raman spectra using a microspectrometer. As examples of the range of biological samples that can be analyzed, we provide instructions for acquiring Raman spectra, maps and images for fresh plant tissue, formalin-fixed and fresh frozen mammalian tissue, fixed cells and biofluids. We explore a robust approach for sample preparation, instrumentation, acquisition parameters and data processing. By using this approach, we expect that a typical Raman experiment can be performed by a nonspecialist user to generate high-quality data for biological materials analysis.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents 1 – 5 . Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred 6 . In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures 7 . However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations 8 . Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine–reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health. Research published in Nature shows that surface-enhanced Raman spectroscopy carried out with colloids can quantify a range of molecules down to concentrations at the femtomolar level.

The purpose of this in vitro study was to investigate the efficacy of micro-Raman spectroscopy on detecting mineral content change during the demineralization and de/remineralization cycling process. The enamel samples ( n  = 55) were randomly divided into three groups and separately treated with demineralization solution ( n  = 20), de/remineralization cycling solution ( n  = 30), and distilled water ( n  = 5). Micro-Raman spectroscopy, microhardness (MHS), and the released calcium ions concentration were performed before and after treatment, respectively. A one-way analysis of variance (ANOVA) with a post hoc Tukey test was used to analyze the results. The Spearman correlation coefficients among the parameters of Raman relative intensity decrease (RRI D %), the percentage of MHS loss (PML), and the released calcium ions concentration were also analyzed. In demineralization group, RRI D %, PML, and released calcium ions concentration were highly correlated with each other ( r  = 0.979, p  < 0.001; r  = 0.984, p  < 0.001; and r  = 0.983, p  < 0.001, respectively). While for the de/remineralization cycling group, there also existed a high correlation between RRI D % and PML ( r  = 0.987, p  < 0.001). In conclusion, micro-Raman spectroscopy could effectively monitor the mineral content change, and its efficacy was validated by the measurement of released calcium ions concentration and MHS.

Background Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization. Results We introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy. Conclusions We demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward’s clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images.