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Cardiovascular diseases in general and atherothrombosis as the most common of its individual disease entities is the leading cause of death in the developed countries. Therefore, visualization and characterization of inner arterial plaque composition is of vital diagnostic interest, especially for the early recognition of vulnerable plaques. Established clinical techniques provide valuable morphological information but cannot deliver information about the chemical composition of individual plaques. Therefore, spectroscopic imaging techniques have recently drawn considerable attention. Based on the spectroscopic properties of the individual plaque components, as for instance different types of lipids, the composition of atherosclerotic plaques can be analyzed qualitatively as well as quantitatively. Here, we compare the feasibility of multimodal nonlinear imaging combining two-photon fluorescence (TPF), coherent anti-Stokes Raman scattering (CARS) and second-harmonic generation (SHG) microscopy to contrast composition and morphology of lipid deposits against the surrounding matrix of connective tissue with diffraction limited spatial resolution. In this contribution, the spatial distribution of major constituents of the arterial wall and atherosclerotic plaques like elastin, collagen, triglycerides and cholesterol can be simultaneously visualized by a combination of nonlinear imaging methods, providing a powerful label-free complement to standard histopathological methods with great potential for in vivo application.

Intravascular imaging techniques provide detailed specification about plaque appearance and morphology, but cannot deliver information about the biochemical composition of atherosclerotic plaques. As the biochemical composition is related to the plaque type, important aspects such as the risk of a plaque rupture and treatment are still difficult to assess. Currently, various spectroscopic techniques are tested for potential applications for the chemical analysis of plaque depositions. Here, we employ Raman spectroscopy in combination with optical coherence tomography (OCT) for the characterization of plaques on rabbits in vivo. Experiments were carried out on New Zealand white rabbits treated with a fat- and cholesterol-enriched diet, using a Raman probe setup with a 785-nm multimode laser as an excitation source. Subsequently, OCT images were acquired with a swept source at 1305 ± 55 nm at 22.6 mW. Raman spectra were recorded from normal regions and regions with early plaque formations. The probe positioning was monitored by x-ray angiography. The spectral information identified plaque depositions consisting of lipids, with triglycerides as the major component. Afterward, OCT images of the spectroscopically investigated areas were obtained. The spectral information correlates well with the observed intravascular morphology and is in good agreement with histology. Raman spectroscopy can provide detailed biochemical specification of atherosclerotic plaques. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases in general and atherothrombosis as the most common of its individual disease entities is the leading cause of death in the developed countries. Therefore, visualization and characterization of inner arterial plaque composition is of vital diagnostic interest, especially for the early recognition of vulnerable plaques. Established clinical techniques provide valuable morphological information but cannot deliver information about the chemical composition of individual plaques. Therefore, spectroscopic imaging techniques have recently drawn considerable attention. Based on the spectroscopic properties of the individual plaque components, as for instance different types of lipids, the composition of atherosclerotic plaques can be analyzed qualitatively as well as quantitatively. Here, we compare the feasibility of multimodal nonlinear imaging combining two-photon fluorescence (TPF), coherent anti-Stokes Raman scattering (CARS) and second-harmonic generation (SHG) microscopy to contrast composition and morphology of lipid deposits against the surrounding matrix of connective tissue with diffraction limited spatial resolution. In this contribution, the spatial distribution of major constituents of the arterial wall and atherosclerotic plaques like elastin, collagen, triglycerides and cholesterol can be simultaneously visualized by a combination of nonlinear imaging methods, providing a powerful label-free complement to standard histopathological methods with great potential for in vivo application. [ABSTRACT FROM AUTHOR]

Spectroscopy-based imaging techniques can provide useful biochemical information about tissue samples. Here, we employ Raman and Fourier transform infrared (IR) imaging to characterize composition and constitution of atherosclerotic plaques of rabbits, fed with a high cholesterol diet. The results were compared with conventional light microscopy after staining with hematoxylin eosin, and elastica van Gieson. The spectral unmixing algorithm vertex component analysis was applied for data analysis and image reconstruction. IR microscopy allowed for differentiation between lipids and proteins in plaques of full aortic cross sections. Raman microscopy further discriminated cholesterol esters, cholesterol and triglycerides. FTIR and Raman images were recorded at a resolution near 20 micrometer per pixel for a large field of view. High resolution Raman images at 1 micrometer per pixel revealed structural details at selected regions of interest. The intima-media and the lipid-protein ratio were determined in five specimens for quantitation. These results correlate well with histopathology. The described method is a promising tool for easy and fast molecular imaging of atherosclerosis. (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

The rapid and precise identification of microorganisms is essential in environmental science, pharmaceuticals, food safety, and medical diagnostics. Raman spectroscopy, valued for its ability to provide detailed chemical and structural information, has gained significant traction in these fields, especially with the adoption of various excitation wavelengths and tailored optical setups. The choice of wavelength and setup in Raman spectroscopy is influenced by factors such as applicability, cost, and whether bulk or single-cell analysis is performed, each impacting sensitivity and specificity in bacterial detection. In this study, we investigate the potential of different excitation wavelengths for bacterial identification, utilizing a mock culture composed of six bacterial species: three Gram-positive (S. warneri, S. cohnii, and E. malodoratus) and three Gram-negative (P. stutzeri, K. terrigena, and E. coli). To improve bacterial classification, we applied machine learning models to analyze and extract unique spectral features from Raman data. The results indicate that the choice of excitation wavelength significantly influences the bacterial spectra obtained, thereby impacting the accuracy and effectiveness of the subsequent classification results. [ABSTRACT FROM AUTHOR]

Cerumen, commonly known as earwax, is a complex mixture composed of secretions from ceruminous glands. These secretions are heterogeneous mixtures mainly composed of lipids and proteins. Despite its prevalence, the potential diagnostic value of cerumen remains largely unexplored. Here, we present an in-depth analysis of cerumen utilizing well-known vibrational approaches such as conventional Raman spectroscopy or surface-enhanced Raman spectroscopy (SERS) together with advanced vibrational spectroscopy techniques such as coherent Raman scattering (CRS), i.e. broadband coherent anti-Stokes Raman scattering (CARS) or stimulated Raman scattering (SRS), as well as optical photothermal infrared (OPTIR) spectroscopy. Through the integration of these vibrational spectroscopic methods, lipids and proteins can be identified as the main components of cerumen; however, they contribute to the final spectral information to various extents depending on the vibrational detection scheme applied. The inherently weak Raman signal could be enhanced by linear (SERS) and non-linear (CRS) processes, resulting in efficient acquisition of fingerprint information and allowing for the detection of marker modes, which cannot be addressed by conventional Raman spectroscopy. OPTIR spectroscopy provides complementary information to Raman spectroscopy, however, without the contribution of a fluorescence background. Our findings underscore the utility of these cutting-edge techniques in unveiling the intricate molecular landscape of cerumen, paving the way for novel point-of-care diagnostic methodologies and therapeutic interventions. [ABSTRACT FROM AUTHOR]

As a highly sensitive vibrational technique, Raman spectroscopy (RS) can provide valuable chemical and molecular data useful to characterise animal cell types, tissues and organs. As a label-free, rapid detection method, RS has been considered a valuable asset in forensics, biology and medicine. The technique has been applied to zebrafish for various purposes, including physiological, biochemical or bioaccumulation analyses. The available data point out its potential for the early diagnosis of detrimental effects elicited by toxicant exposure. Nevertheless, no baseline spectra are available for zebrafish embryos and larvae that could allow for suitable planning of toxicological assessments, comparison with toxicant-elicited spectra or mechanistic understanding of biochemical and physiological responses to the exposures. With this in mind, this work carried out a baseline characterisation of Raman spectra of zebrafish embryos and larvae throughout early development. Raman spectra were recorded from the iris, forebrain, melanocytes, heart, muscle and swim bladder between 24 and 168 h post-fertilisation. A chemometrics approach, based on partial least-squares discriminant analysis (PLS-DA), was used to obtain a Raman characterisation of each tissue or organ. In total, 117 Raman bands were identified, of which 24 were well represented and, thus, retained in the data analysed. Only three bands were found to be common to all organs and tissues. The PLS-DA provided a tentative Raman spectral fingerprint typical of each tissue or organ, reflecting the ongoing developmental dynamics. The bands showed frequencies previously assigned to collagen, cholesterol, various essential amino acids, carbohydrates and nucleic acids. [ABSTRACT FROM AUTHOR]

Background: Neurosurgery demands exceptional precision due to the brain's complex and delicate structures, necessitating precise targeting of pathological targets. Achieving optimal outcomes depends on the surgeon's ability to accurately differentiate between healthy and pathological tissues during operations. Raman spectroscopy (RS) has emerged as a promising innovation, offering real-time, in vivo non-invasive biochemical tissue characterization. This literature review evaluates the current research on RS applications in intraoperative neurosurgery, emphasizing its potential to enhance surgical precision and patient outcomes. Methods: Following PRISMA guidelines, a comprehensive systematic review was conducted using PubMed to extract relevant peer-reviewed articles. The inclusion criteria focused on original research discussing real-time RS applications with human tissue samples in or near the operating room, excluding retrospective studies, reviews, non-human research, and other non-relevant publications. Results: Our findings demonstrate that RS significantly improves tumor margin delineation, with handheld devices achieving high sensitivity and specificity. Stimulated Raman Histology (SRH) provides rapid, high-resolution tissue images comparable to traditional histopathology but with reduced time to diagnosis. Additionally, RS shows promise in identifying tumor types and grades, aiding precise surgical decision-making. RS techniques have been particularly beneficial in enhancing the accuracy of glioma surgeries, where distinguishing between tumor and healthy tissue is critical. By providing real-time molecular data, RS aids neurosurgeons in maximizing the extent of resection (EOR) while minimizing damage to normal brain tissue, potentially improving patient outcomes and reducing recurrence rates. Conclusions: This review underscores the transformative potential of RS in neurosurgery, advocating for continued innovation and research to fully realize its benefits. Despite its substantial potential, further research is needed to validate RS's clinical utility and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue. [ABSTRACT FROM AUTHOR]

Contemporary brain tumor research focuses on two challenges: First, tumor typing and grading by analyzing excised tissue is of utmost importance for choosing a therapy. Second, for prognostication the tumor has to be removed as completely as possible. Nowadays, histopathology of excised tissue using haematoxylin-eosine staining is the gold standard for the definitive diagnosis of surgical pathology specimens. However, it is neither applicable in vivo, nor does it allow for precise tumor typing in those cases when only nonrepresentative specimens are procured. Infrared and Raman spectroscopy allow for very precise cancer analysis due to their molecular specificity, while nonlinear microscopy is a suitable tool for rapid imaging of large tissue sections. Here, unstained samples from the brain of a domestic pig have been investigated by a multimodal nonlinear imaging approach combining coherent anti-Stokes Raman scattering, second harmonic generation, and two photon excited fluorescence microscopy. Furthermore, a brain tumor specimen was additionally analyzed by linear Raman and Fourier transform infrared imaging for a detailed assessment of the tissue types that is required for classification and to validate the multimodal imaging approach. Hence label-free vibrational microspectroscopic imaging is a promising tool for fast and precise in vivodiagnostics of brain tumors. [ABSTRACT FROM AUTHOR]

The process and mechanism of biomineralization and relevant physicochemical properties of mineral crystals are remarkably sophisticated multidisciplinary fields that include biology, chemistry, physics, and materials science. The components of the organic matter, structural construction of minerals, and related mechanical interaction, etc., could help to reveal the unique nature of the special mineralization process. Herein, the paper provides an overview of the biomineralization process from the perspective of molecular vibrational spectroscopy, including the physicochemical properties of biomineralized tissues, from physiological to applied mineralization. These physicochemical characteristics closely to the hierarchical mineralization process include biological crystal defects, chemical bonding, atomic doping, structural changes, and content changes in organic matter, along with the interface between biocrystals and organic matter as well as the specific mechanical effects for hardness and toughness. Based on those observations, the special physiological properties of mineralization for enamel and bone, as well as the possible mechanism of pathological mineralization and calcification such as atherosclerosis, tumor micro mineralization, and urolithiasis are also reviewed and discussed. Indeed, the clearly defined physicochemical properties of mineral crystals could pave the way for studies on the mechanisms and applications. [ABSTRACT FROM AUTHOR]

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions. [ABSTRACT FROM AUTHOR]

Simple Summary: Bladder cancer is the sixth most incident neoplasm, and the average five-year survival rate drops to 5% for patients with the metastatic disease. Raman spectroscopy probes molecular vibrations by inelastic scattering of laser light. In a retrospective study, control and cancer specimens were prepared from ten human bladder resectates and label-free assessed by Raman microspectroscopic imaging. A data analysis workflow without preprocessing identified the locations and signatures of normal bladder, bladder cancer, necrosis, epithelium, and lipid inclusions. Furthermore, microplastic particles, pigments or carotenoids were detected in 13 out of 20 specimens inside tissue and near tissue margins and their identity was confirmed by spectral library surveys. Hypotheses about the origin of these foreign materials are discussed. Our approach with minimal user interference offers prospects for future clinical translation such as intraoperative tumor detection and label-free material identification in complex matrices. Raman spectroscopy offers label-free assessment of bladder tissue for in vivo and ex vivo intraoperative applications. In a retrospective study, control and cancer specimens were prepared from ten human bladder resectates. Raman microspectroscopic images were collected from whole tissue samples in a closed chamber at 785 nm laser excitation using a 20× objective lens and 250 µm step size. Without further preprocessing, Raman images were decomposed by the hyperspectral unmixing algorithm vertex component analysis into endmember spectra and their abundancies. Hierarchical cluster analysis distinguished endmember Raman spectra that were assigned to normal bladder, bladder cancer, necrosis, epithelium and lipid inclusions. Interestingly, Raman spectra of microplastic particles, pigments or carotenoids were detected in 13 out of 20 specimens inside tissue and near tissue margins and their identity was confirmed by spectral library surveys. Hypotheses about the origin of these foreign materials are discussed. In conclusion, our Raman workflow and data processing protocol with minimal user interference offers advantages for future clinical translation such as intraoperative tumor detection and label-free material identification in complex matrices. [ABSTRACT FROM AUTHOR]

Simple Summary: Real-time diagnosis tools and methods are desired to aid in the intraoperative grading of glioma and tumor boundary identification to achieve safe maximal tumor removal. Raman spectroscopy is an optical method for real-time glioma detection, but few studies use fresh glioma tissue for biochemical analysis. This study is the first investigation of human glioma using a portable VRR-LRRTM Raman analyzer under quasi-clinical conditions, and reveals significant spectral differences between normal (control) and different grades of glioma. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with histopathology as the gold standard. This result validates the possibility of glioma diagnosis using fresh tissue and provides instant feedback for neurosurgeons in guiding maximal safe resection, and it may support the translation of this portable tool for in vivo and real-time use in tissue biochemical analysis. There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation. [ABSTRACT FROM AUTHOR]

The Raman phenomenon is based on the spontaneous inelastic scattering of light, which depends on the molecular characteristics of the dispersant. Therefore, Raman spectroscopy and imaging allow us to obtain direct information, in a label-free manner, from the chemical composition of the sample. Since it is well established that the development of many brain diseases is associated with biochemical alterations of the affected tissue, Raman spectroscopy and imaging have emerged as promising tools for the diagnosis of ailments. A combination of Raman spectroscopy and/or imaging with tagged molecules could also help in drug delivery and tracing for treatment of brain diseases. In this review, we first describe the basics of the Raman phenomenon and spectroscopy. Then, we delve into the Raman spectroscopy and imaging modes and the Raman-compatible tags. Finally, we center on the application of Raman in the study, diagnosis, and treatment of brain diseases, by focusing on traumatic brain injury and ischemia, neurodegenerative disorders, and brain cancer. [ABSTRACT FROM AUTHOR]

Confocal Raman microscopy is a useful tool to observe composition and constitution of label‐free samples at high spatial resolution. However, accurate characterization of microstructure of tissue and its application in diagnostic imaging are challenging due to weak Raman scattering signal and complex chemical composition of tissue. We have developed a method to improve imaging speed, diffraction efficiency, and spectral resolution of confocal Raman microscopy. In addition to the novel imaging technique, the machine learning method enables confocal Raman microscopy to visualize accurate histology of tissue sections. Here, we have demonstrated the performance of the proposed method by measuring histological classification of atherosclerotic arteries and compared the histological confocal Raman images with the conventional staining method. Our new confocal Raman microscopy enables us to comprehend the structure and biochemical composition of tissue and diagnose the buildup of atherosclerotic plaques in the arterial wall without labeling. [ABSTRACT FROM AUTHOR]

Abstract: Vibrational spectroscopic imaging techniques are new tools for visualizing chemical components in tissue without staining. The spectroscopic signature can be used as a molecular fingerprint of pathological tissues. Fourier transform infrared imaging which is more common than Raman imaging so far has already been applied to identify the primary tumor of brain metastases. The current study introduces a two level discrimination model for Raman microspectroscopic images to distinguish normal brain, necrosis and tumor tissue, and subsequently to determine the primary tumor. 22 Specimens of normal brain tissue and brain metastasis of bladder carcinoma, lung carcinoma, mamma carcinoma, colon carcinoma, prostate carcinoma and renal cell carcinoma were snap frozen, and thin tissue sections were prepared. Raman microscopic images were collected with 785nm laser excitation at 10μm step size. Cluster analysis, vertex component analysis and principal component analysis were applied for data preprocessing. Then, data of 17 specimens were used to train the discrimination model based on support vector machines with radial basis functions kernel. The training data were discriminated with accuracy better than 99%. Finally, the discrimination model correctly predicted independent specimens. The results were superior to discrimination by partial least squares discriminant analysis and support vector machines with linear basis function kernel that were applied for comparison. [Copyright &y& Elsevier]

Accurate identification of Hepatitis B virus (HBV) disease by analyzing the Raman spectroscopic images is a challenge for pathologists. To save precious human lives, an efficient technique is required with higher diagnostic accuracy at early‐stage of HBV. We proposed a novel method of HBV diagnosis using deep neural networks with the concept of transfer learning and Raman spectroscopic images. The proposed approach developed by utilizing pretrained convolutional neural networks ResNet101 by employing transfer learning on a real dataset of HBV‐infected blood plasma samples. Dataset consists of 1000 Raman images in which 600 are HBV‐infected blood plasma samples, and 400 are healthy ones. The developed model is capable to detect minute variation between infected and healthy samples and achieved enhanced performance. The proposed approach has been assessed and attained high classification accuracy, sensitivity, specificity, and AUC of 99.7%, 100%, 99.25%, and 98.7%, respectively. The proposed TL‐ResNet101 model outperformed the conventional approaches such as PCA‐SVM and PCA‐LDA and demonstrated improved accuracy more than 7%. High performance indicates that the developed TL‐ResNet101 model has potential to use for HBV diagnosis. Moreover, the developed automated approach can be extended for other disease. [ABSTRACT FROM AUTHOR]

In accordance with the recent studies, Raman spectroscopy is well experimented as a highly sensitive analytical and imaging technique in biomedical research, mainly for various disease diagnosis including cancer. In comparison with other imaging modalities, Raman spectroscopy facilitate numerous assistances owing to its low background signal, immense spatial resolution, high chemical specificity, multiplexing capability, excellent photo stability and non‐invasive detection capability. In cancer diagnosis Raman imaging intervened as a promising investigative tool to provide molecular level information to differentiate the cancerous vs non‐cancerous cells, tissues and even in body fluids. Anciently, spontaneous Raman scattering is very feeble due to its low signal intensity and long acquisition time but new advanced techniques like coherent Raman scattering (CRS) and surface enhanced Raman scattering (SERS) gradually superseded these issues. So, the present review focuses on the recent developments and applications of Raman spectroscopy‐based imaging techniques for cancer diagnosis. [ABSTRACT FROM AUTHOR]

The literature is rich in proof of concept studies demonstrating the potential of Raman spectroscopy for disease diagnosis. However, few studies are conducted in a clinical context to demonstrate its applicability in current clinical practice and workflow. Indeed, this translational research remains far from the patient's bedside for several reasons. First, samples are often cultured cell lines. Second, they are prepared on non-standard substrates for clinical routine. Third, a unique supervised classification model is usually constructed using inadequate cross-validation strategy. Finally, the implemented models maximize classification accuracy without taking into account the clinician's needs. In this paper, we address these issues through a diagnosis problem in real clinical conditions, i.e., the diagnosis of chronic lymphocytic leukemia from fresh unstained blood smears spread on glass slides. From Raman data acquired in different experimental conditions, a repeated double cross-validation strategy was combined with different cross-validation approaches, a consensus label strategy and adaptive thresholds able to adapt to the clinician's needs. Combined with validation at the patient level, classification results were improved compared to traditional strategies. [ABSTRACT FROM AUTHOR]

Raman spectroscopy, a form of vibrational spectroscopy, has the ability to provide sensitive and specific biochemical analysis of tissue. This review article provides an in-depth analysis of the suitability of different Raman spectroscopy techniques in providing intra-operative margin analysis in a range of solid tumour pathologies. Surgical excision remains the primary treatment of a number of solid organ cancers. Incomplete excision of a tumour and positive margins on histopathological analysis is associated with a worse prognosis, the need for adjuvant therapies with significant side effects and a resulting financial burden. The provision of intra-operative margin analysis of surgically excised tumour specimens would be beneficial for a number of pathologies, as there are no widely adopted and accurate methods of margin analysis, beyond histopathology. The limitations of Raman spectroscopic studies to date are discussed and future work necessary to enable translation to clinical use is identified. We conclude that, although there remain a number of challenges in translating current techniques into a clinically effective tool, studies so far demonstrate that Raman Spectroscopy has the attributes to successfully perform highly accurate intra-operative margin analysis in a clinically relevant environment. [ABSTRACT FROM AUTHOR]

In brain surgery, novel technologies are continuously developed to achieve better tumor delineation and maximize the extent of resection. Raman spectroscopy is an optical method that enables to retrieve a molecular signature of tissue biochemical composition in order to identify tumor and normal tissue. Here, the translation of Raman spectroscopy to the surgical practice for discerning a variety of different tumor entities from non-neoplastic brain parenchyma was investigated. Fresh unprocessed biopsies obtained from brain tumor surgery were analyzed over 1.5 years including all patients that gave consent. Measurements were performed with a Raman microscope by medical personnel as routine activity. The Raman and fluorescence signals of the acquired spectra were analyzed by principal component analysis, followed by supervised classification to discriminate non-tumor tissue vs. tumor and distinguish tumor entities. Histopathology of the measured biopsies was performed as reference. Classification led to the correct recognition of all non-neoplastic biopsies (7/7) and of 97% of the investigated tumor biopsies (195/202). For instance, GBM was recognized as tumor with a correct rate of 94% if primary, and of 100% if recurrent. Astrocytoma and oligodendroglioma were recognized as tumor with correct rates of 86 and 90%, respectively. All brain metastases, meningioma and schwannoma were correctly recognized as tumor and distinguished from non-neoplastic brain tissue. Furthermore, metastases were discerned from glioma with correct rate of 90%. Oligodendroglioma and astrocytoma IDH1 -mutant, which differ in the presence of 1p/19q codeletion, were discerned with a correct rate of 81%. These results demonstrate the feasibility of rapid brain tumors recognition and extraction of diagnostic information by Raman spectroscopy, using a protocol that can be easily included in the routine surgical workflow. [ABSTRACT FROM AUTHOR]

Metastatic brain tumors represent a significant proportion of tumors identified intraoperatively. A rapid diagnostic method, circumventing the need for histopathology studies, could prove clinically useful. As many spectroscopic studies have shown ability to differentitate between different tumor types, this technique was evaluated for use within metastatic brain tumors. Spectrochemical approaches [Raman and attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) spectroscopy] were applied to determine how readily they may identify the primary site for the metastatic tumor. Metastases were from primary adenocarcinomas of lung (n = 7) and colorectum (n = 7), and for comparison, metastatic melanoma (n = 7). The objective was to determine if Raman or ATR-FTIR spectroscopy could delineate the origin of the primary tumor. The results demonstrate that there are marked similarities between the two adenocarcinoma groups and whilst Raman and ATR-FTIR can distinguish the three groups with limited success, classification accuracy is greatly improved when combining the adenocarcinoma groups. The use of such techniques in the clinical setting is more likely to be found intraoperatively, determining the presence of a tumor and suggesting the tumor class; however, traditional histopathology would still be needed to identify the primary origin of the tumor. [ABSTRACT FROM AUTHOR]

Oral injuries are currently diagnosed by histopathological analysis of biopsy, which is an invasive procedure and does not give immediate results. On the other hand, the Raman spectroscopy technique is a real-time and minimally invasive analytical tool with a notable diagnostic capability. At the current stage, researchers are widely aware of the diagnostic potential of the technique and how it is considered promising for providing biochemical information in real time and without damaging the tissue. The problem originates from a lack of relevant studies and clinical trials that could show the actual use of Raman spectroscopy to help patients. Our goal here is to narrow the relationship between physicists, chemists, engineers, computer scientists, and the medical community, and in fact discuss the potential of Raman spectroscopy as a novel clinical analysis method. In the present study, we focused on the use of Raman spectroscopy as a daily clinical practice. In this context, additional studies and in vivo tests should be performed with the same approach as a real application. We want to show the scientific and industrial community what is really necessary for this, starting from a clinical point of view. Using our previous experience publishing different oral pathologies and types of samples, we also aim to discuss the current state and potential of Raman spectroscopy and what is required to implement Raman spectroscopy for oral clinical applications. [ABSTRACT FROM AUTHOR]

One of the new targets of untapped therapeutic potential is perivascular adipose tissue (pVAT). pVAT releases a plethora of pro- and anti-inflammatory agents and is involved in the inflammatory response of the vascular wall, playing a key role in various cardiovascular pathologies. Both fiber optic Raman spectroscopy with a high-spatial resolution probe and Raman microscopy were applied to study various types of adipose tissue with the emphasis on pVATs of the thoracic and abdominal aorta and the mesenteric artery, as well as epididymal and interscapular adipose tissue for comparison. Our results demonstrated that the lipid unsaturation degree was clearly distinct in various types of adipose tissue and was influenced by the age of animals. In particular, the basal unsaturation level of pVATs of the abdominal aorta and the mesenteric artery was considerably higher than that of the thoracic aorta and a significant increase of the unsaturation level of pVAT with age was observed showing that aging has a considerable impact on the pVAT's chemical composition. Overall, our results show that Raman spectroscopy is a sensitive tool to determine the perivascular adipose tissue chemical composition that appears to be vascular-bed specific. [ABSTRACT FROM AUTHOR]

Novel approaches toward understanding the evolution of disease can lead to the discovery of biomarkers that will enable better management of disease progression and improve prognostic evaluation. Raman spectroscopy is a promising investigative and diagnostic tool that can assist in uncovering the molecular basis of disease and provide objective, quantifiable molecular information for diagnosis and treatment evaluation. This technique probes molecular vibrations/rotations associated with chemical bonds in a sample to obtain information on molecular structure, composition, and intermolecular interactions. Raman scattering occurs when light interacts with a molecular vibration/rotation and a change in polarizability takes place during molecular motion. This results in light being scattered at an optical frequency shifted (up or down) from the incident light. By monitoring the intensity profile of the inelastically scattered light as a function of frequency, the unique spectroscopic fingerprint of a tissue sample is obtained. Since each sample has a unique composition, the spectroscopic profile arising from Raman-active functional groups of nucleic acids, proteins, lipids, and carbohydrates allows for the evaluation, characterization, and discrimination of tissue type. This review provides an overview of the theory of Raman spectroscopy, instrumentation used for measurement, and variation of Raman spectroscopic techniques for clinical applications in cancer, including detection of brain, ovarian, breast, prostate, and pancreatic cancers and circulating tumor cells. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the study is to use Raman spectroscopy to analyze the biochemical composition of medulloblastoma and normal tissues from the safety margin of the CNS and to find specific Raman biomarkers capable of differentiating between tumorous and normal tissues.Methods: The tissue samples consisted of medulloblastoma (grade IV) (n = 11). The tissues from the negative margins were used as normal controls. Raman images were generated by a confocal Raman microscope—WITec alpha 300 RSA.Results: Raman vibrational signatures can predict which tissue has tumorous biochemistry and can identify medulloblastoma. The Raman technique makes use of the fact that tumors contain large amounts of protein and far less lipids (fatty compounds), while healthy tissue is rich in both.Conclusion: The ability of Raman spectroscopy and imaging to detect medulloblastoma tumors fills the niche in diagnostics. These powerful analytical techniques are capable of monitoring tissue morphology and biochemistry. Our results demonstrate that RS can be used to discriminate between normal and medulloblastoma tissues. [ABSTRACT FROM AUTHOR]

Raman spectroscopy via fiber optic probes omits some of the major limitations related to ex vivo preparation of tissue samples (e.g. fixation, freezing, cutting) and enables rapid registration of spectra, therefore, this technique has the potential to become a useful diagnostic tool. In this work, we evaluated the applicability of Raman spectroscopy via fiber optic probe for rapid assessment of lipid content in the liver in the context of its potential application as a tool to verify the degree of liver steatosis. Non-alcoholic fatty liver disease (NAFLD) is a common liver disorder that is characterized by excessive lipid accumulation within hepatic tissue and is associated with insulin resistance and metabolic syndrome. Raman spectroscopy via fiber optic probe was applied to investigate the biochemical status of the liver in mild (mice fed a high-fat diet) and severe (db/db mice) models of NAFLD. A considerable increase in lipid content without substantial alterations in composition was observed in mild liver steatosis. In contrast, more severe liver steatosis caused not only a significant lipid content increase, but also hepatic cholesterol accumulation accompanied by significant loss of hepatic vitamin A content. Chemometric analysis based on average Raman spectra recorded via fiber optic probe provided discrimination of mild and severe liver steatosis and control livers with high sensitivity and specificity. In conclusion, our work demonstrates that a relatively simple Raman setup equipped with a commercial fiber optic probe combined with basic chemometric analysis enables rapid quantification of liver steatosis. [ABSTRACT FROM AUTHOR]

Raman spectroscopy has been proved to be a promising diagnostic technique for various cancers detection. A major drawback for its clinical translation is the intrinsic weakness of Raman effects. Highly sensitive equipment and optimal measurement conditions are generally applied to overcome this drawback. However, these equipment are usually bulky, expensive and may also be easily influenced by surrounding environment. In this preliminary work, a low‐resolution fiber‐optic Raman sensing system is applied to evaluate the diagnostic potential of Raman spectroscopy to identify different bladder pathologies ex vivo. A total number of 262 spectra taken from 32 bladder specimens are included in this study. These spectra are categorized into 3 groups by histopathological analysis, namely normal bladder tissues, low‐grade bladder tumors and high‐grade bladder tumors. Principal component analysis fed artificial neural network are used to train a classification model for the spectral data with 10‐fold cross‐validation and an overall prediction accuracy of 93.1% is obtained. The sensitivities and specificities for normal bladder tissues, low‐grade bladder tumors and high‐grade bladder tumors are 88.5% and 95.1%, 90.3% and 98%, and 97.5% and 96.4%, respectively. These results demonstrate the potential of using a low‐resolution fiber‐optic Raman system for in vivo bladder cancer diagnosis. [ABSTRACT FROM AUTHOR]

Raman scattering has long been used to analyze chemical compositions in biological systems. Owing to its high chemical specificity and noninvasive detection capability, Raman scattering has been widely employed in cancer screening, diagnosis, and intraoperative surgical guidance in the past ten years. In order to overcome the weak signal of spontaneous Raman scattering, coherent Raman scattering and surface-enhanced Raman scattering have been developed and recently applied in the field of cancer research. This review focuses on innovative studies of the use of Raman scattering in cancer diagnosis and their potential to transition from bench to bedside. [ABSTRACT FROM AUTHOR]

Abstract: Raman spectroscopy has gained increasing attention in biomedical diagnostics thanks to instrumental developments and chemometric models that enhance the accuracy and speed of this technique. In particular, a model transfer procedure is needed if the chemometric models are utilized to predict a new dataset measured under (secondary) conditions different to the training data (primary). The model transfer methods try to achieve satisfactory prediction on the secondary dataset with minimal or no training samples measured under secondary conditions. Model transfer methods that have been reported are mostly applied for near‐infrared spectroscopy and in regression problems. The investigation of model transfer in Raman spectroscopy and classification is rare. Our recently reported Tikhonov regularization based on partial least squares regression (TR‐PLSR) was utilized for model transfer of Raman‐based classification models for spore species. In the present work, we show that the TR‐PLSR also works for Raman spectra of vegetative bacteria, even though the Raman spectra of 3 species of bacteria were acquired on 3 different Raman spectrometers. Additionally, we report 2 newly developed model transfer methods for Raman spectra: movement of principal components scores and spectral augmentation. Both methods were validated based on the Raman spectra of bacterial spores and vegetative bacteria, where a significant improvement of the model transferability was observed. The movement of principal components scores method yielded results comparable with those of the TR‐PLSR. However, the new methods are superior to TR‐PLSR in 2 ways: No training samples in the secondary conditions are necessary, and the methods are not restricted to partial least squares regression but can also be applied to other models. Both advantages are important in real‐world applications and represent a large step for improving the model transfer of Raman spectra. [ABSTRACT FROM AUTHOR]

This article reviews spectral unmixing used as a new method for Raman imaging. Raman spectroscopy explores the vibrational information about specific chemical bonds in molecules, which can be used for label-free molecular visualization. However, chemical bonds are usually shared among different molecules, which results in closed or mixed Raman peaks of many molecules. Therefore, the acquired spectra cannot be directly used to reconstruct the Raman images, as pure component spectra are hidden under the acquired spectra. Spectral unmixing is an effective method to provide a meaningful spectrum of each component with no priori spectral information and to also reconstruct the compositional distribution images. This article summarizes some representative spectral unmixing approaches used for Raman imaging and many related researches. This review strives to introduce the combination of spectral unmixing and Raman imaging as an efficient analytic technique to characterize various constituents and make subtle understanding of those complex structures. [ABSTRACT FROM PUBLISHER]

The great capability of the label-free classification of tissue via vibrational spectroscopy, like Raman or infrared imaging, is shown in numerous publications (review: Diem et al., J. Biophotonics, 2013, 6, 855–886). Herein, we present a new approach, virtual staining, that improves the Raman spectral histopathology (SHP) images of colorectal cancer tissue by combining the integrated Raman intensity image in the C–H stretching region (2800–3050 cm−1) with the pseudo-colour Raman image. This allows the display of fine structures such as the filamentous composition of muscle tissue. The morphology of the virtually stained images is in agreement with the gold standard in medical diagnosis, the haematoxylin–eosin staining. The virtual staining image also represents the whole biochemical fingerprint, and several tissue components including carcinoma were identified automatically with high sensitivity and specificity. For fast tissue classifications, a similar approach was applied on coherent anti-Stokes Raman scattering (CARS) spectral data that is faster and therefore potentially more suitable for clinical applications. [ABSTRACT FROM AUTHOR]

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012. [ABSTRACT FROM AUTHOR]

Hepatic cirrhosis is a severe chronic disease of the liver accompanied by massive changes in the physiology of the cells constituting the hepatic tissue. Success or failing of a therapeutic effort is difficult to recognize because of its late manifestation in the tissue morphology. In this study, the complex course of hepatic cirrhosis and its regression is followed in a rodent carbon tetrachloride model. Raman spectroscopy, which senses molecular vibrations and reflects the molecular composition of a sample, was applied to gain label- and destruction-free insights into the process of cirrhosis and to conclude on the hepatic disease state on the cellular level. Hematoxylin and eosin staining and immunofluorescence labeling were used to obtain complementary information. False color images derived from maps of Raman spectra by spectral unmixing revealed individual nucleus positions giving structural information. Spectral data unraveled chemical changes associated with liver damage on the cellular level. Upon carbon tetrachloride treatment, a higher lipid content and the presence of catabolic products indicated cirrhosis in tissue samples. Furthermore, 77 % of the Raman spectra recorded from treated rats were classified as diseased, whereas 96 % of the Raman spectra recorded from untreated rats were classified as healthy. Importantly, samples from rats that experienced a recovery period revealed a chemical composition highly similar to the ones from healthy rats while morphologically clear signs of tissue damage were still obvious. Eighty-three percent of their Raman spectra were classified as healthy. The vibrational fingerprint of tissue provides characteristic information that might serve as prognostic biomarker. [ABSTRACT FROM AUTHOR]

Fluorescence lifetime imaging (FLIm) and Raman spectroscopy are two promising methods to support morphological intravascular imaging techniques with chemical contrast. Both approaches are complementary and may also be used in combination with OCT/IVUS to add chemical specificity to these morphologic intravascular imaging modalities. In this contribution, both modalities were simultaneously acquired from two human coronary specimens using a bimodal probe. A previously trained SVM model was used to interpret the fluorescence lifetime data; integrated band intensities displayed in RGB false color images were used to interpret the Raman data. Both modalities demonstrate unique strengths and weaknesses and these will be discussed in comparison to histologic analyses from the two coronary arteries imaged. [ABSTRACT FROM AUTHOR]

New approaches for visualisation of silicon nanoparticles (SiNPs) in cancer cells are realised by means of the linear and nonlinear optics in vitro. Aqueous colloidal solutions of SiNPs with sizes of about 10-40 nm obtained by ultrasound grinding of silicon nanowires were introduced into breast cancer cells (MCF-7 cell line). Further, the time-varying nanoparticles enclosed in cell structures were visualised by high-resolution structured illumination microscopy (HR-SIM) and micro-Raman spectroscopy. Additionally, the nonlinear optical methods of two-photon excited fluorescence (TPEF) and coherent anti-Stokes Raman scattering (CARS) with infrared laser excitation were applied to study the localisation of SiNPs in cells. Advantages of the nonlinear methods, such as rapid imaging, which prevents cells from overheating and larger penetration depth compared to the single-photon excited HR-SIM, are discussed. The obtained results reveal new perspectives of the multimodal visualisation and precise detection of the uptake of biodegradable non-toxic SiNPs by cancer cells and they are discussed in view of future applications for the optical diagnostics of cancer tumours. [ABSTRACT FROM AUTHOR]

Background: Due to the steadily increasing number of cancer patients worldwide the early diagnosis and treatment of cancer is a major field of research. The diagnosis of cancer is mostly performed by an experienced pathologist via the visual inspection of histo-pathological stained tissue sections. To save valuable time, low quality cryosections are frequently analyzed with diagnostic accuracies that are below those of high quality embedded tissue sections. Thus, alternative means have to be found that enable for fast and accurate diagnosis as the basis of following clinical decision making.Methods: In this contribution we will show that the combination of the three label-free non-linear imaging modalities CARS (coherent anti-Stokes Raman-scattering), TPEF (two-photon excited autofluorescence) and SHG (second harmonic generation) yields information that can be translated into computational hematoxylin and eosin (HE) images by multivariate statistics. Thereby, a computational HE stain is generated resulting in pseudo-HE overview images that allow for identification of suspicious regions. The latter are analyzed further by Raman-spectroscopy retrieving the tissue's molecular fingerprint.Results: The results suggest that the combination of non-linear multimodal imaging and Raman-spectroscopy possesses the potential as a precise and fast tool in routine histopathology.Conclusions: As the key advantage, both optical methods are non-invasive enabling for further pathological investigations of the same tissue section, e.g. a direct comparison with the current pathological gold-standard. [ABSTRACT FROM AUTHOR]