Correlation between near-infrared Raman spectroscopy and the histopathological analysis of atherosclerosis in human coronary arteries

Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer

BACKGROUND

Breast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors.

RESULTS

Our study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues.

CONCLUSIONS

Consequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.

FABP4 Enhances Lipidic and Fibrotic Cardiac Structural and Ca2+ Dynamic Changes

BACKGROUND

Adipocyte FABP4 (fatty acid-binding protein 4) is augmented in the epicardial stroma of patients with long-standing persistent atrial fibrillation. Because this molecule is released mainly by adipocytes, our objective was to study its role in atrial cardiomyopathy, focusing our attention on fibrosis, metabolism, and electrophysiological changes. These results might clarify the role of adiposity as a mediator of atrial cardiomyopathy.

METHODS

We used several preclinical cellular models, epicardial and subcutaneous stroma primary cell cultures from patients undergoing open heart surgery, human atrial fibroblasts, atrial cardiomyocytes derived from human induced pluripotent stem cells and isolated from adult mice, and Nav1.5 transfected Chinese hamster ovary cells. Fibrosis, glucose, mitochondrial and adipogenesis activity, gene expression, and proteomics were determined by wound healing, enzymatic, colorimetric, fluorescence assays, real-time quantitative polymerase chain reaction, and TripleTOF proteomics. Molecular changes were analyzed by Raman confocal microspectroscopy, calcium dynamics by confocal microscopy, and ion currents by patch clamp. Epicardial, subcutaneous, and atrial fibroblasts and cardiomyocytes were incubated with FABP4 at 100 ng/mL.

RESULTS

Our results showed that FABP4 induced fibrosis, glucose metabolism, and lipid accumulation on epicardial and subcutaneous stroma cells and atrial fibroblasts. Besides, it modified lipid content and calcium dynamics in atrial cardiomyocytes without effects on INa.

CONCLUSIONS

FABP4 exerts fibrotic and metabolic changes on epicardial stroma and modifies lipid content and calcium dynamic on atrial cardiomyocytes. These results suggest its possible role as an atrial cardiomyopathy mediator.

A comparative analysis of stem cell differentiation on 2D and 3D substrates using Raman microspectroscopy

Chondrogenesis is a complex cellular process that involves the transformation of mesenchymal stem cells (MSCs) into chondrocytes, the specialised cells that form cartilage. In recent years, three-dimensional (3D) culture systems have emerged as a promising approach to studying cell behaviour and development in a more physiologically relevant environment compared to traditional two-dimensional (2D) cell culture. The use of these systems provided insights into the molecular mechanisms that regulate chondrogenesis and has the potential to revolutionise the development of new therapies for cartilage repair and regeneration. This study demonstrates the successful application of Raman microspectroscopy (RMS) as a label-free, non-destructive, and sensitive method to monitor the chondrogenic differentiation of bone marrow-derived rat mesenchymal stem cells (rMSCs) in a collagen type I hydrogel, and explores the potential benefits of 3D hydrogels compared to conventional 2D cell culture environments. rMSCs were cultured on 3D substrates for 3 weeks and their differentiation was monitored by measuring the spectral signatures of their subcellular compartments. Additionally, the evolution of high-density micromass cultures was investigated to provide a comprehensive understanding of the process and complex interactions between cells and their surrounding extracellular matrix. For comparison, rMSCs were induced into chondrogenesis in identical medium conditions for 21 days in monolayer culture. Raman spectra showed that rMSCs cultured in a collagen type I hydrogel are able to undergo a distinct chondrogenic differentiation pathway at a significantly higher rate than the 2D culture cells. 3D cultures expressed stronger and more homogeneous chondrogenesis-associated peaks such as collagens, glycosaminoglycans (GAGs), and aggrecan while manifesting changes in proteins and lipidic content. These results suggest that 3D type I collagen hydrogel substrates are promising for in vitro chondrogenesis studies, and that RMS is a valuable tool for monitoring chondrogenesis in 3D environments.

Multi-Wavelength Raman Differentiation of Malignant Skin Neoplasms

Raman microspectroscopy has become an effective method for analyzing the molecular appearance of biomarkers in skin tissue. For the first time, we acquired in vitro Raman spectra of healthy and malignant skin tissues, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), at 532 and 785 nm laser excitation wavelengths in the wavenumber ranges of 900-1800 cm-1 and 2800-3100 cm-1 and analyzed them to find spectral features for differentiation between the three classes of the samples. The intensity ratios of the bands at 1268, 1336, and 1445 cm-1 appeared to be the most reliable criteria for the three-class differentiation at 532 nm excitation, whereas the bands from the higher wavenumber region (2850, 2880, and 2930 cm-1) were a robust measure of the increased protein/lipid ratio in the tumors at both excitation wavelengths. Selecting ratios of the three bands from the merged (532 + 785) dataset made it possible to increase the accuracy to 87% for the three classes and reach the specificities for BCC + SCC equal to 87% and 81% for the sensitivities of 95% and 99%, respectively. Development of multi-wavelength excitation Raman spectroscopic techniques provides a versatile non-invasive tool for research of the processes in malignant skin tumors, as well as other forms of cancer.

Label-Free Assessment of Neuronal Activity Using Raman Micro-Spectroscopy

Given the pivotal role of neuronal populations in various biological processes, assessing their collective output is crucial for understanding the nervous system's complex functions. Building on our prior development of a spiral scanning mechanism for the rapid acquisition of Raman spectra from single cells and incorporating machine learning for label-free evaluation of cell states, we investigated whether the Paint Raman Express Spectroscopy System (PRESS) can assess neuronal activities. We tested this hypothesis by examining the chemical responses of glutamatergic neurons as individual neurons and autonomic neuron ganglia as neuronal populations derived from human-induced pluripotent stem cells. The PRESS successfully acquired Raman spectra from both individual neurons and ganglia within a few seconds, achieving a signal-to-noise ratio sufficient for detailed analysis. To evaluate the ligand responsiveness of the induced neurons and ganglia, the Raman spectra were subjected to principal component and partial least squares discriminant analyses. The PRESS detected neuronal activity in response to glutamate and nicotine, which were absent in the absence of calcium. Additionally, the PRESS induced dose-dependent neuronal activity changes. These findings underscore the capability of the PRESS to assess individual neuronal activity and elucidate neuronal population dynamics and pharmacological responses, heralding new opportunities for drug discovery and regenerative medicine advancement.

Label-Free Fiber-Optic Raman Spectroscopy for Intravascular Coronary Atherosclerosis and Plaque Detection

The rupture of atherosclerotic plaques remains one of the leading causes of morbidity and mortality worldwide. The plaques have certain pathological characteristics including a fibrous cap, inflammation, and extensive lipid deposition in a lipid core. Various invasive and noninvasive imaging techniques can interrogate structural aspects of atheroma; however, the composition of the lipid core in coronary atherosclerosis and plaques cannot be accurately detected. Fiber-optic Raman spectroscopy has the capability of in vivo rapid and accurate biomarker detection as an emerging omics technology. Previous studies demonstrated that an intravascular Raman spectroscopic technique may assess and manage the therapeutic and medication strategies intraoperatively. The Raman spectral information identified plaque depositions consisting of lipids, triglycerides, and cholesterol esters as the major components by comparing normal region and early plaque formation region with histology. By focusing on the composition of plaques, we could identify the subgroups of plaques accurately and rapidly by Raman spectroscopy. Collectively, this fiber-optic Raman spectroscopy opens up new opportunities for coronary atherosclerosis and plaque detection, which would assist optimal surgical strategy and instant postoperative decision-making. In this paper, we will review the advancement of label-free fiber-optic Raman probe spectroscopy and its applications of coronary atherosclerosis and atherosclerotic plaque detection.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

The microarchitecture and chemical composition of the femur neck of senescent female rats after different physical training protocols

A sedentary lifestyle, coupled with a decrease in estrogen, impairs bone homeostasis, favoring to the development of osteopenia and osteoporosis, both recognized as risk factors for fractures. Here, we investigated the quality of the femur, particularly the femur neck region, and the ambulation performance of senescent rats subjected to three different physical training protocols during the periestropause period. Forty-eight female rats, 18 months of age, were subjected to a 120-day training period, three times a week. The rats were distributed into four groups: aerobic training (AT), strength training (ST), concurrent training (CT), or no training (NT). After the experimental period, at 21 months of age, ambulation performance and femur were analyzed using microtomography, Raman stereology, densitometry, and mechanical strength tests. The results demonstrated greater remodeling activity and improvement in resistance and bone microarchitecture in the femur neck of senescent female rats after undergoing physical training. Our verified higher intensities of bands related to collagen, phosphate, amide III, and amide I. Furthermore, the analysis of the secondary collagen structures indicated alterations in the collagen network due to the exercise, resulting in increased bone strength. Both AT and strength-based training proved beneficial, with AT showing greater adaptations in bone density and stiffness in the femur, while strength-based training greater adaptations in trabecular and cortical structure. These insights contribute to the understanding of the potential interventions for preventing osteopenia and osteoporosis, which are critical risk factors for fractures.

Human tissue-resident peritoneal macrophages reveal resistance towards oxidative cell stress induced by non-invasive physical plasma

In the context of multimodal treatments for abdominal cancer, including procedures such as cytoreductive surgery and intraperitoneal chemotherapy, recurrence rates remain high, and long-term survival benefits are uncertain due to post-operative complications. Notably, treatment-limiting side effects often arise from an uncontrolled activation of the immune system, particularly peritoneally localized macrophages, leading to massive cytokine secretion and phenotype changes. Exploring alternatives, an increasing number of studies investigated the potential of plasma-activated liquids (PAL) for adjuvant peritoneal cancer treatment, aiming to mitigate side effects, preserve healthy tissue, and reduce cytotoxicity towards non-cancer cells. To assess the non-toxicity of PAL, we isolated primary human macrophages from the peritoneum and subjected them to PAL exposure. Employing an extensive methodological spectrum, including flow cytometry, Raman microspectroscopy, and DigiWest protein analysis, we observed a pronounced resistance of macrophages towards PAL. This resistance was characterized by an upregulation of proliferation and anti-oxidative pathways, countering PAL-derived oxidative stress-induced cell death. The observed cellular effects of PAL treatment on human tissue-resident peritoneal macrophages unveil a potential avenue for PAL-derived immunomodulatory effects within the human peritoneal cavity. Our findings contribute to understanding the intricate interplay between PAL and macrophages, shedding light on the promising prospects for PAL in the adjuvant treatment of peritoneal cancer.

The role of glucose and fructose on lipid droplet metabolism in human normal bronchial and cancer lung cells by Raman spectroscopy

Fructose is one of the most important monosaccharides in the human diet that the human body needs for proper metabolism. This paper presents an approach to study biochemical changes caused by sugars in human normal bronchial cells (BEpiC) and human cancer lung cells (A549) by Raman spectroscopy and Raman imaging. Results after supplementation of human bronchial and lung cells with fructose are also discussed and compared with results obtained for pure human bronchial and lung cells. Based on Raman techniques we have proved that peaks at 750 cm-1, 1126 cm-1, 1444 cm-1, 1584 cm-1 and 2845 cm-1 can be treated as biomarkers to monitor fructose changes in cells. Results for fructose have been compared with results for glucose. Raman analysis of the bands at 750 cm-1, 1126 cm-1, 1584 cm-1 and 2845 cm-1 for pure BEpiC and A549 cells and BEpiC and A549 after supplementation with fructose and glucose are higher after supplementation with fructose in comparison to glucose. The obtained results shed light on the uninvestigated influence of glucose and fructose on lipid droplet metabolism by Raman spectroscopy methods.

Differentiation of Salivary Gland and Salivary Gland Tumor Tissue via Raman Imaging Combined with Multivariate Data Analysis

Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary.

Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks

IMPORTANCE

The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.

Raman imaging and chemometric methods in human normal bronchial and cancer lung cells: Raman biomarkers of lipid reprogramming

This paper presents an approach to study biochemical changes in human normal bronchial cells (BEpiC) and human cancer lung cells (A549) by Raman spectroscopy and Raman imaging combined with chemometric methods. Based on Raman spectra and Raman imaging combined with chemometric methods we have proved that peaks at 845 cm-1, 2845 cm-1, 2936 cm-1, 1444 cm-1, 750 cm-1, 1126 cm-1, 1584 cm-1, can be treated as Raman biomarkers probing phosphorylation, lipid reprogramming, oxidative phosphorylation and changes in cholesterol and cytochrome in normal and cancer cells. Raman analysis of the bands at 845 cm-1, 2845 cm-1, 1444 cm-1, and 1126 cm-1 in human cancer lung cells and human normal bronchial cells demonstrate enhanced phosphorylation and triglycerides de novo synthesis, reduced levels of cholesterol and cytochrome c in cancer cells. The sensitivity is equal to 100% (nucleus), 87.5% (mitochondria), 100% (endoplasmic reticulum), 87.5% (lipid droplets), 87.5% (cytoplasm), 87.5% (cell membrane) for A549 cell line and 83.3% (nucleus), 100% (mitochondria), 83.3% (endoplasmic reticulum), 100% (lipid droplets), 100% (cytoplasm), 83.3% (cell membrane) for BEpiC. The values of specificity for cross-validation equal 93.4% (nucleus), 85.5% (mitochondria), 89.5% (endoplasmic reticulum), 90.8% (lipid droplets), 61.8% (cytoplasm), 94.7% (cell membrane) for A549 cell line and 88.5% (nucleus), 85.9% (mitochondria), 85.9% (endoplasmic reticulum), 97.4% (lipid droplets), 75.6% (cytoplasm), 92.3% (cell membrane) for BEpiC. We have confirmed that Raman spectroscopy methods combined with PLS-DA are useful tools to monitor changes in human cancer lung cells and human normal bronchial cells.

Label-free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface-enhanced Raman spectroscopy validated by machine learning algorithm

The direct preventative detection of flow-induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface-enhanced Raman spectroscopy (SERS) from single-drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high-fat diet to rapidly induce disturbed flow-induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro-magnetic resonance imaging (micro-MRI) and histology in comparison to the right carotid artery. Single-drop blood samples are deposited on chips of gold-coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA-based analysis was validated against an independent test sample and compared against the PCA-PLS-DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification-based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.

Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus.

OBJECTIVES

The main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA).

METHODOLOGY

Serum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS.

RESULTS

HBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy.

CONCLUSIONS

Surface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals' serum samples.

Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.

Decorin improves human pancreatic β-cell function and regulates ECM expression in vitro

Transplantation of islets of Langerhans is a promising alternative treatment strategy in severe cases of type 1 diabetes mellitus; however, the success rate is limited by the survival rate of the cells post-transplantation. Restoration of the native pancreatic niche during transplantation potentially can help to improve cell viability and function. Here, we assessed for the first time the regulatory role of the small leucine-rich proteoglycan decorin (DCN) in insulin secretion in human β-cells, and its impact on pancreatic extracellular matrix (ECM) protein expression in vitro. In depth analyses utilizing next-generation sequencing as well as Raman microspectroscopy and Raman imaging identified pathways related to glucose metabolism to be upregulated in DCN-treated cells, including oxidative phosphorylation within the mitochondria as well as proteins and lipids of the endoplasmic reticulum. We further showed the effectiveness of DCN in a transplantation setting by treating collagen type 1-encapsulated β-cell-containing pseudo-islets with DCN. Taken together, in this study, we demonstrate the potential of DCN to improve the function of insulin-secreting β-cells while reducing the expression of ECM proteins affiliated with fibrotic capsule formation, making DCN a highly promising therapeutic agent for islet transplantation.

Discrimination of leukemias and non-leukemic cancers in blood serum samples of children and adolescents using a Raman spectral model

This study aimed to identify the differences presented in the Raman spectrum of blood serum from normal subjects compared to leukemic and non-leukemic subjects and the differences between the leukemics and non-leukemics, correlating the spectral differences with the biomolecules. Serum samples from children and adolescents were subjected to Raman spectroscopy (830 nm, laser power 350 mW; n = 566 spectra, being 72 controls, 269 leukemics, and 225 non-leukemics). Exploratory analysis based on principal component analysis (PCA) of the serum sample's spectra was performed. Classification models based on partial least squares discriminant analysis (PLS-DA) were developed to classify the spectra into normal, leukemic, and non-leukemic, as well as to discriminate spectra of leukemic from non-leukemic. The exploratory analysis showed principal components with peaks related to amino acids, proteins, lipids, and carotenoids. The spectral differences between normal, leukemic, and non-leukemic showed features assigned to proteins (serum features), amino acids, and carotenoids. The PLS-DA model classified the spectra of the normal group versus leukemic and non-leukemic groups with accuracy of 66%, sensitivity of 99%, and specificity of 57%. The PLS-DA discriminated the spectra of the leukemic and non-leukemic groups with accuracy of 67%, sensitivity of 72%, and specificity of 60%. The study showed that Raman spectroscopy is a technique that may be used for the biochemical differentiation of leukemias and other types of cancer in serum samples of children and adolescents. Nevertheless, building an extensive data library of Raman spectra from serum samples of controls, leukemics, and non-leukemics of different age groups is necessary to understand the findings better.

Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

Size-dependent theoretical and experimental photothermal conversion efficiency of spherical gold nanoparticles

INTRODUCTION

Due to their biocompatible and plasmonic properties, gold nanoparticles (Au NPs) are good candidates to be photosensitizers in photothermal cancer therapy (PTT).

MATERIALS AND METHODS

In this paper, the dependence of the NIR-light-to-heat energy on Au NPs size was investigated. Moreover, to determine the photosensitizing properties of gold nanoparticles, PTT was conducted on two colon cell lines: SW480 and SW620 by irradiating them with two lasers having different wavelengths.

RESULTS

Transmission electron microscopy showed that the respective sizes of Au NPs were 10 nm, 12 nm and 16 nm. Moreover, local as well as global structural measurements showed that all synthesized Au NPs were crystalline and UV-Vis spectroscopy revealed that with increasing nanoparticles size the position of the surface Plasmon resonance (SPR) peaks is shifted to higher wavelengths. Decrease of cells viability was observed, when they were cultured with Au NPs and irradiated by 650 nm and 808 nm lasers. Moreover, FTIR and Raman spectra of cells, showed structural changes in DNA, phospholipids, proteins and cholesterol caused by the addition of nanoparticles and laser irradiation. The chemical changes were more pronounced in the cells cultured with Au NPs and irradiated by 650 nm lasers and these changes were dependent on the nanoparticle size. Moreover, the viability of cells investigated by the MTS assay showed, that the percentage of dead cells (∼40%) is the highest for cells cultured with 8 nm Au NPs and irradiated by the 650 nm laser. The photothermal conversion efficiency calculated from the experimental results showed a decrease of this parameter from 70% to 55% and from 61% to 48% with increasing particle size, for 650 nm and 808 nm lasers, respectively.

CONCLUSIONS

The obtained results showed that the photothermal conversion efficiency of Au NPs is size-tunable, and can be correlated with the absorption/extinction ratios calculated by the Mie theory.

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy

Raman spectroscopy is a non-destructive analysis technique that provides detailed information about the chemical structure of tumors. Raman spectra of 52 giant cell tumors of bone (GCTB) and 21 adjacent normal tissues of formalin-fixed paraffin embedded (FFPE) and frozen specimens were obtained using a confocal Raman spectrometer and analyzed with machine learning and deep learning algorithms. We discovered characteristic Raman shifts in the GCTB specimens. They were assigned to phenylalanine and tyrosine. Based on the spectroscopic data, classification algorithms including support vector machine, k-nearest neighbors and long short-term memory (LSTM) were successfully applied to discriminate GCTB from adjacent normal tissues of both the FFPE and frozen specimens, with the accuracy ranging from 82.8% to 94.5%. Importantly, our LSTM algorithm showed the best performance in the discrimination of the frozen specimens, with a sensitivity and specificity of 93.9% and 95.1% respectively, and the AUC was 0.97. The results of our study suggest that confocal Raman spectroscopy accomplished by the LSTM network could non-destructively evaluate a tumor margin by its inherent biochemical specificity which may allow intraoperative assessment of the adequacy of tumor clearance.

A novel diagnostic method: FT-IR, Raman and derivative spectroscopy fusion technology for the rapid diagnosis of renal cell carcinoma serum

This research innovatively combines FT-IR, Raman spectroscopy and their first-derivative spectroscopy to develop a rapid diagnosis method for renal cell carcinoma (RCC). After measuring the Raman spectra and FT-IR spectra of 45 cases of control subjects and 28 cases of RCC, the first derivative of the infrared spectra and the Raman spectra were calculated respectively. Principal component analysis (PCA) was used to extract the features of the infrared spectra, first-derivative infrared spectra, Raman spectra and first-derivative Raman spectra. Then the four feature matrices were merged as fused spectral feature matrices. The fused matrices were used as the input of AlexNet and MCNN. The fused spectral feature matrices were used as the input of AlexNet and MCNN. The adjusted AlexNet model performed better, and the classification accuracy of the fused spectral data is 93%. Compared with the classification results of infrared spectra (74%), Raman spectra (75%) and the fusion of infrared and Raman spectra (79%) combined with the adjusted AlexNet model, the classification result of the fusion of infrared spectra, Raman spectra and their first-derivative was significantly improved. The experimental results show that infrared spectroscopy, Raman spectroscopy and their first-derivative fusion technology combined with deep learning algorithms has great potential in the diagnosis of RCC.

Disease-Associated Multimolecular Signature in the Urine of Patients with Lyme Disease Detected Using Raman Spectroscopy and Chemometrics

A urine-based screening technique for Lyme disease (LD) was developed in this research. The screen is based on Raman spectroscopy, iterative smoothing-splines with root error adjustment (ISREA) spectral baselining, and chemometric analysis using Rametrix software. Raman spectra of urine from 30 patients with positive serologic tests (including the US Centers for Disease Control [CDC] two-tier standard) for LD were compared against subsets of our database of urine spectra from 235 healthy human volunteers, 362 end-stage kidney disease (ESKD) patients, and 17 patients with active or remissive bladder cancer (BCA). We found statistical differences (p < 0.001) between urine scans of healthy volunteers and LD-positive patients. We also found a unique LD molecular signature in urine involving 112 Raman shifts (31 major Raman shifts) with significant differences from urine of healthy individuals. We were able to distinguish the LD molecular signature as statistically different (p < 0.001) from the molecular signatures of ESKD and BCA. When comparing LD-positive patients against healthy volunteers, the Rametrix-based urine screen performed with 86.7% for overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively. When considering patients with ESKD and BCA in the LD-negative group, these values were 88.7% (accuracy), 83.3% (sensitivity), 91.0% (specificity), 80.7% (PPV), and 92.4% (NPV). Additional advantages to the Raman-based urine screen include that it is rapid (minutes per analysis), is minimally invasive, requires no chemical labeling, uses a low-profile, off-the-shelf spectrometer, and is inexpensive relative to other available LD tests.

Diagnosis of Lung Cancer by FTIR Spectroscopy Combined With Raman Spectroscopy Based on Data Fusion and Wavelet Transform

Lung cancer is a fatal tumor threatening human health. It is of great significance to explore a diagnostic method with wide application range, high specificity, and high sensitivity for the detection of lung cancer. In this study, data fusion and wavelet transform were used in combination with Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy to study the serum samples of patients with lung cancer and healthy people. The Raman spectra of serum samples can provide more biological information than the FTIR spectra of serum samples. After selecting the optimal wavelet parameters for wavelet threshold denoising (WTD) of spectral data, the partial least squares–discriminant analysis (PLS-DA) model showed 93.41% accuracy, 96.08% specificity, and 90% sensitivity for the fusion data processed by WTD in the prediction set. The results showed that the combination of FTIR spectroscopy and Raman spectroscopy based on data fusion and wavelet transform can effectively diagnose patients with lung cancer, and it is expected to be applied to clinical screening and diagnosis in the future.

Raman tweezers as an alternative diagnostic tool for paroxysmal nocturnal hemoglobinuria

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare disease characterized by hemolysis of red blood cells (RBC) and venous thrombosis. The gold standard method for the diagnosis of this disease is flow cytometry. Here, we propose a combined optical tweezers and Raman spectral (Raman tweezers) approach to analyze blood samples from volunteers with or without PNH conditions. Raman spectroscopy is a well-known method for investigating a material's chemical structure and is also used in molecular analysis of biological compounds. In this study, we trap individual RBCs found in whole blood samples drawn from PNH patients and the control group. Evaluation of the Raman spectra of these cells by band component analysis and machine learning shows a significant difference between the two groups. The specificity and the sensitivity of the training performed by support vector machine (SVM) analysis were found to be 81.8% and 78.3%, respectively. This study shows that an immediate and high accuracy test result is possible for PNH disease by employing Raman tweezers and machine learning.

Studying the pathological and biochemical features in breast cancer progression by confocal Raman microspectral imaging of excised tissue samples

Confocal Raman microspectral imaging (CRMI) has been used to detect the spectra-pathological features of ductal carcinoma in situ (DCIS) and lobular hyperplasia (LH) compared with the heathy (H) breast tissue. A total of 15-20 spectra were measured from healthy tissue, LH tissue, and DCIS tissue. One-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple tests were used to evaluate the peak intensity variations in all three tissue types. Besides that, linear discrimination analysis (LDA) algorithm was adopted in combination with principal component analysis (PCA) to classify the spectral features from tissues at different stages along the continuum to breast cancer. Moreover, by using the point-by-point scanning methodology, spectral datasets were obtained and reconstructed for further pathologic visualization by multivariate imaging methods, including K-mean clustering analysis (KCA) and PCA. Univariate imaging of individual Raman bands was also used to describe the differences in the distribution of specific molecular components in the scanning area. After a detailed spectral feature analysis from 800 to 1800 cm^-1 and 2800 to 3000 cm^-1 for all the three tissue types, the histopathological features were visualized based on the content and structural variations of lipids, proteins, phenylalanine, carotenoids and collagen, as well as the calcification phenomena. The results obtained not only allowed a detailed Raman spectroscopy-based understanding of the malignant transformation process of breast cancer, but also provided a solid spectral data support for developing Raman based breast cancer clinical diagnostic techniques.

Surface-enhanced Raman spectroscopy for comparison of serum samples of typhoid and tuberculosis patients of different stages

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is a reliable tool for the identification and differentiation of two different human pathological conditions sharing the same symptomology, typhoid and tuberculosis (TB).

OBJECTIVES

To explore the potential of surface-enhanced Raman spectroscopy for differentiation of two different diseases showing the same symptoms and analysis by principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA).

METHODS

Serum samples of clinically diagnosed typhoid and tuberculosis infected individuals were analyzed and differentiated by SERS using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, the collected serum samples were analyzed under the SERS instrument and unique SERS spectra of typhoid and tuberculosis were compared showing notable spectral differences in protein, lipid and carbohydrates features. Different stages of the diseased class of typhoid (Early acute and late acute stage) and tuberculosis (Pulmonary and extra-pulmonary stage) were compared with each other and with healthy human serum samples, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe.

RESULTS

SERS Spectral data of typhoid and tuberculosis showed clear differences and were significantly separated using PCA. SERS spectral data of both stages of typhoid and tuberculosis were separated according to 1st principle component. Moreover, by analyzing data using partial least square discriminate analysis, differentiation of two disease classes were considered more valid with a 100% value of sensitivity, specificity and accuracy.

CONCLUSION

SERS can be employed for identification and comparison of two different human pathological conditions sharing same symptomology.

Confocal Raman microspectral analysis and imaging of the drug response of osteosarcoma to cisplatin

Confocal Raman microspectral analysis and imaging were used to elucidate the drug response of osteosarcoma (OS) to cisplatin. Raman spectral data were obtained from OS cells that were untreated (UT group) and treated with 20 µM (20T group) and 40 µM (40T group) cisplatin for 24 hours. Statistical analysis of the changes in specific Raman signals was performed using a one-way ANOVA and multiple Tukey's honest significant difference (HSD) post hoc tests. Principal component analysis-linear discriminant analysis (PCA-LDA) was used to highlight the featured cellular drug responses based on the obtained spectral information. For spectral imaging analysis, k-means cluster analysis (KCA) was adopted to clarify the effect of cisplatin dose changes on the subcellular structure and its biochemical composition. The results suggest that the major biochemical changes induced by cisplatin in OS cells undergoing apoptosis are reduced protein and nucleic acid content. Through univariate analysis, the changes in the distribution of nucleic acids in OS cells induced by different doses of cisplatin were obtained. The combination of Raman spectroscopy and multivariate analysis shows that cisplatin mainly acts on the nucleus and causes changes in the secondary structure of proteins. These results indicate that Raman imaging technology has the potential to offer the basis of dose optimization for personalized cancer treatment by helping to understand in vitro cellular drug interactions.

Characterization and prediction of viral loads of Hepatitis B serum samples by using surface-enhanced Raman spectroscopy (SERS)

BACKGROUND

Raman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis.

OBJECTIVES

To develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quantitative analysis of hepatitis B viral (HBV) infection from blood serum samples.

METHODS

Clinically diagnosed hepatitis B virus (HBV) infected serum samples of patients of different levels of viral loads have been subjected for SERS analysis in comparison with the healthy ones by using silver nanoparticles (Ag NPs) based SERS substrates. The SERS measurements were performed on blood serum samples of 11 healthy and 32 clinically diagnosed HBV patients of different viral load levels of different exponentials including (10¹, 10² called as low level), (10³, 10⁴ called as medium level) and (10⁵, 10⁸ called as high level). Furthermore, multivariate data analysis techniques, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data.

RESULTS

The SERS spectral features due to biochemical changes in HBV positive serum samples associated with the increasing viral loads were established which could be employed for HBV diagnostic purpose. PCA was found helpful for the differentiation between SERS spectral data of serum samples of different levels of HBV infection and healthy individuals. PLSR model developed with standard samples of known viral loads for predicting the viral loads of blind/unknown samples with 99% predicted accuracy.

CONCLUSION

SERS can be employed for qualitative and quantitative analysis of HBV infection from blood serum samples.

Surface-enhanced Raman spectroscopy analysis of serum samples of typhoid patients of different stages

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) of body fluids is considered a quick, simple and easy to use method for the diagnosis of disease.

OBJECTIVES

To evaluate rapid, reliable, and non-destructive SERS-based diagnostic tool with multivariate data analysis including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) for classification of different stages of typhoid on the basis of characteristic SERS spectral features.

METHODS

SERS has been used for analysis of serum samples of different stages of typhoid including early acute stage and late acute stage in comparison with healthy samples, in order to investigate capability of this technique for diagnosis of typhoid. SERS spectral features associated with the biochemical changes taking place during the development of the typhoid fever were analyzed and identified.

RESULTS

The value of area under the receiver operating characteristics (AUROC) for early acute stage versus healthy is 0.87 and that for healthy versus late acute stage is 0.52. PLS-DA classifier model gives values of 100 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus early acute stage. Moreover, this classifier model gives values of 91 %, 89 % and 97 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus late acute stage.

CONCLUSIONS

Based on preliminary work it is concluded that SERS has potential to diagnose various stages of typhoid fever including early acute and late acute stage in comparison with healthy samples.

Non-invasive cell classification using the Paint Raman Express Spectroscopy System (PRESS)

Raman scattering represents the distribution and abundance of intracellular molecules, including proteins and lipids, facilitating distinction between cellular states non-invasively and without staining. However, the scattered light obtained from cells is faint and cells have complex structures, making it difficult to obtain a Raman spectrum covering the entire cell in a short time using conventional methods. This also prevents efficient label-free cell classification. In the present study, we developed the Paint Raman Express Spectroscopy System, which uses two fast-rotating galvano mirrors to obtain spectra from a wide area of a cell. By using this system and applying machine learning, we were able to acquire broad spectra of a variety of human and mouse cell types, including pluripotent stem cells and confirmed that each cell type can be classified with high accuracy. Moreover, we classified different activation states of human T cells, despite their similar morphology. This system could be used for rapid and low-cost drug evaluation and quality management for drug screening in cell-based assays.

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer

This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy (LRS), can distinguish healthy and tumor tissue. We now report that combining LRS with two machine learning algorithms, unsupervised k-means and stochastic nonlinear neural networks (NN), provides rapid, quantitative, probabilistic tumor assessment with real-time error analysis. NNs were first trained on Raman spectra using human expert histopathology diagnostics as gold standard (74 spectra, 5 patients). K-means predictions using spectral data when compared to histopathology produced clustering models with 93.2-94.6% accuracy, 89.8-91.8% sensitivity, and 100% specificity. NNs trained on k-means predictions generated probabilities of correctness for the autonomous classification. Finally, the autonomous system characterized an extended dataset (203 spectra, 8 patients). Our results show that an increase in DNA|RNA signal intensity in the fingerprint region (600-1800 cm-1) and global loss of high wavenumber signal (2800-3200 cm-1) are particularly sensitive LRS warning signs of tumor. The stochastic nature of NNs made it possible to rapidly generate multiple models of target tissue classification and calculate the inherent error in the probabilistic estimates for each target.

Applicability of Raman spectroscopy on porcine parvovirus and porcine circovirus type 2 detection

Porcine parvovirus (PPV) is one of the major infectious causes of reproductive failure of swine. This disease is characterized by embryonic and fetal infection and death, responsible for important economic losses. PPV is also implicated as a trigger in the development of post-weaning multisystemic wasting syndrome (PMWS) caused by Porcine circovirus type 2 (PCV2). Their detection is PCR-based, which is quite sensitive and specific, but laborious, costly and time-demanding. Therefore, this study aimed to assess Raman spectroscopy (RS) as a diagnostic tool for PPV and PCV2 due to its label-free properties and unique ability to search and identify molecular fingerprints. Briefly, swine testis (ST) cells were inoculated with PPV or PCV2 and in vitro cultured (37 °C, 5% CO₂) for four days. Fixed cells were then submitted to RS investigation using a 633 nm laser. A total of 225 spectra centered at 1300 cm^-1 was obtained for each sample (5 spectra/cell; 15 cells/replicate; 3 replicates) of PPV-, PCV2-infected and uninfected (control) ST cells. Clear statistical discrimination between samples from both virus-infected cells was achieved with a Principal Component - Linear Discriminant Analysis (PCA-LDA) model, reaching sensitivity rates from 95.55% to 97.77%, respectively to PCV2- and PPV-infected cells. These results were then submitted to a Leave-One-Out (LOO) validation algorithm resulting in 99.97% of accuracy. Extensive band assignment was analyzed and compiled for better understanding of PPV and PCV2 virus-cell interaction, demonstrating that specific protein, lipids and DNA/RNA bands are the most important assignments related to discrimination of virus-infected from uninfected cells. In conclusion, these results represent promising bases for RS application on PCV2 and PPV detection for future diagnostic applications.

Analytical performance of Raman spectroscopy in assaying biochemical components in human serum

Chronic non-infectious diseases are important to research as they are the main causes of death in Brazil and worldwide. One very important chronic non-infectious disease is cardiovascular disease, whose risk factors (diabetes, dyslipidemia, and renal failure) can be detected through assessments of serum biochemical components. The objective of this study was to evaluate the analytical performance of Raman spectroscopy for analysis of lipid profile (total cholesterol, triglycerides, and HDL cholesterol), non-protein nitrogenous compounds (urea and creatinine), and glucose in 242 human serum samples. Models to discriminate and quantify the samples were developed using the predicted concentration by quantitative regression model based on partial least squares (PLS). The analytical error for the "leave-one-out" cross-validation based on the predicted PLS concentration was 10.5 mg/dL for total cholesterol, 21.4 mg/dL for triglyceride, 13.0 mg/dL for HDL cholesterol, 4.9 mg/dL for urea, 0.21 mg/dL for creatinine, and 15.4 mg/dL for glucose. The Kappa coefficient indicate very good agreement for cholesterol (0.83), good for triglyceride (0.77), urea (0.70) and creatinine (0.66), and fair for HDL cholesterol (0.38) and glucose (0.30). The results of the analytical performance demonstrated that Raman spectroscopy can be considered an important methodology to screen the population, especially for serum triglycerides and cholesterol.

Temporal In Vitro Raman Spectroscopy for Monitoring Replication Kinetics of Epstein-Barr Virus Infection in Glial Cells

Raman spectroscopy can be used as a tool to study virus entry and pathogen-driven manipulation of the host efficiently. To date, Epstein-Barr virus (EBV) entry and altered biochemistry of the glial cell upon infection are elusive. In this study, we detected biomolecular changes in human glial cells, namely, HMC-3 (microglia) and U-87 MG (astrocytes), at two variable cellular locations (nucleus and periphery) by Raman spectroscopy post-EBV infection at different time points. Two possible phenomena, one attributed to the response of the cell to viral attachment and invasion and the other involved in duplication of the virus followed by egress from the host cell, are investigated. These changes corresponded to unique Raman spectra associated with specific biomolecules in the infected and the uninfected cells. The Raman signals from the nucleus and periphery of the cell also varied, indicating differential biochemistry and signaling processes involved in infection progression at these locations. Molecules such as cholesterol, glucose, hyaluronan, phenylalanine, phosphoinositide, etc. are associated with the alterations in the cellular biochemical homeostasis. These molecules are mainly responsible for cellular processes such as lipid transport, cell proliferation, differentiation, and apoptosis in the cells. Raman signatures of these molecules at distinct time points of infection indicated their periodic involvement, depending on the stage of virus infection. Therefore, it is possible to discern the details of variability in EBV infection progression in glial cells at the biomolecular level using time-dependent in vitro Raman scattering.

Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis

A machine learning approach is applied to Raman spectra of cells from the MIA PaCa-2 human pancreatic cancer cell line to distinguish between tumor repopulating cells (TRCs) and parental control cells, and to aid in the identification of molecular signatures. Fifty-one Raman spectra from the two types of cells are analyzed to determine the best combination of data type, dimension size, and classification technique to differentiate the cell types. An accuracy of 0.98 is obtained from support vector machine (SVM) and k-nearest neighbor (kNN) classifiers with various dimension reduction and feature selection tools. We also identify some possible biomolecules that cause the spectral peaks that led to the best results.

Similarities in the General Chemical Composition of Colon Cancer Cells and Their Microvesicles Investigated by Spectroscopic Methods-Potential Clinical Relevance

Colon cancer constitutes 33% of all cancer cases in humans and the majority of patients with metastatic colon cancer still have poor prognosis. An important role in cancer development is the communication between cancer and normal cells. This may occur, among others, through extracellular vesicles (including microvesicles) (MVs), which are being released by both types of cells. MVs may regulate a diverse range of biological processes and are considered as useful cancer biomarkers. Herein, we show that similarity in the general chemical composition between colon cancer cells and their corresponding tumor-derived microvesicles (TMVs) does exist. These results have been confirmed by spectroscopic methods for four colon cancer cell lines: HCT116, LoVo, SW480, and SW620 differing in their aggressiveness/metastatic potential. Our results show that Raman and Fourier Transform InfraRed (FTIR) analysis of the cell lines and their corresponding TMVs did not differ significantly in the characterization of their chemical composition. However, hierarchical cluster analysis of the data obtained by both of the methods revealed that only Raman spectroscopy provides results that are in line with the molecular classification of colon cancer, thus having potential clinical relevance.

Liver changes induced by cadmium poisoning distinguished by confocal Raman imaging

Heavy metal pollution has become an important issue threatening human health and the liver is a very important metabolic organ. Here, we use label-free Raman confocal imaging to study the alterations of the liver tissue after cadmium pollution. Raman imaging has been performed on 100μmx100μm liver tissues to study the distribution of important macromolecules and the average Raman spectrum of the entire region has been used to characterize and quantize the change of biochemical compositions in liver tissue. The poisoned livers displayed a significant decrease in the intensity of 748 cm^-1, 1128 cm^-1 and 1585 cm^-1 bands of cytochrome C, in comparison to the control. The collagen peak at 1082 cm^-1 is significantly higher than that of control, suggesting the increasing fibrosis of Cd liver tissues. To confirm the results, we selected a 30μmx15μm liver cell area for high-resolution Raman imaging. We observed a substantial increase of lipids and proteins at specific points of hepatocytes. The confocal Raman imaging of liver tissues provided a unique tool to better understand disease-induced changes in the biochemical phenotype of primary liver tissues. Our study provides valuable references as in vitro models for studying Cd accumulation and toxicity in human liver.

In vitro study of effects of ELF-EMF on testicular tissues of roe deer (Capreolus capreolus) - FTIR and FT-Raman spectroscopic investigation

Electromagnetic fields (EMF) are classified as an environmental factor affecting living organisms. The aim of the study was to evaluate the effect of EMF at different frequencies (50 and 120 Hz), durations of treatment (2 and 4 h) and with the magnetic induction of 8 m T on testicular tissues of roe deer (Capreolus capreolus) in vitro by comparison with the control samples. Fourier Transform Raman Spectroscopy (FT-Raman) and Fourier Transform Infrared Spectroscopy (FTIR) were utilized in this study to identify the chemical changes in the testicular tissues. The FTIR and FT-Raman spectroscopy methods were used to evaluate differences in spectra of the treated tissues compared to the control group. The results from the analysis of the spectra indicated there were characteristic differences in the testicular tissue compared with the control samples. There was identification of peaks attributed to different biochemical components. Comparing the spectra for different frequencies and treatment times, there was a greater intensity of peaks originating from most of the functional groups in the tissues evaluated. With the FTIR spectra, there were five of 15 peaks, while with the FT-Raman spectra, there were six of ten peaks that were shifted. For FTIR and FT-Raman analyzed spectral ranges, results from the PCA analysis indicate there was no similarity between control groups (2 and 4 h) and samples treated with EMF at a frequency of 120 Hz for 2 and 4 h. In conclusion, therefore, EMF is an environmental factor affecting the testis of roe deer.

Platinum-gold nanoraspberries as effective photosensitizer in anticancer photothermal therapy

BACKGROUND

New nanophotosensitizers for photothermal cancer therapy (PTT) are still sought. In this paper we propose fancy shaped, non agglomerated core/shell PtAu NRs nanoraspberries (PtAu NRs) as potential nanophotosensitizers in PTT.

RESULTS

Light microscopy images of two colon cancer cell lines (SW480, SW620) showed, that the laser irradiation combined with PtAu NRs caused visible changes in the cell morphology. Fourier Transform InfraRed (FTIR) and Raman spectroscopies showed chemical changes in the DNA, phospholipids, lipids and protein structures caused by laser irradiation in the presence of PtAu NRs. The MTS assay showed ~ 25% mortality of cancer cells due to the addition of PtAu NRs to the cell culture, while for laser irradiation combined with nanoparticles, the mortality of cancer cells increased to 65% for the 650 nm laser and to 60% for the 808 nm laser. The calculated photothermal conversion efficiency reached 62% and 51% for the 650 nm and 808 nm lasers, respectively.

CONCLUSIONS

PtAu NRs could be applied as effective light-absorbers in the PTT anticancer therapy.

Laser stimulating ST36 with optical fiber induce blood component changes in mice: a Raman spectroscopy study

ST36 is a commonly-used acupoint in traditional Chinese medicine (TCM) for treatment of inflammations, pains and gastrointestinal disturbs. For decades, the low power laser acupuncture has been widely applied as an alternative therapy to traditional metal needle acupuncture and achieved relatively fine therapeutic effect for ST36-related symptoms with reduction of uncomfortableness and infection risks. However its disadvantages of low penetrativity and lack of manipulation skills limit its potential performance. An optical fiber laser acupuncture introduced by the previous study combines traditional needling acupuncture and the laser stimulation together, making a stronger therapeutic effect and showing a potential value in clinical application. To evaluate its acupunctural effect on blood, mice are taken as experimental model and Raman spectroscopic technique is used to analysis the changes of blood components after stimulating on ST36. The results show that both the traditional needling acupuncture and optical fiber acupuncture could lead to some spectral changes of blood in mice. This study explores the optical fiber acupuncture's effect on blood in mice using Raman spectroscopy technique for mechanism of acupuncture therapy.

Raman endoscopy for monitoring the anticancer drug treatment of colorectal tumors in live mice

A miniaturized Raman endoscope (mRE) system was employed to study the effects of anticancer treatment on colorectal tumors in a live murine model.