Raman spectroscopy on blood serum samples of patients with end-stage liver disease

Using a 3D Silicon Micro-Channel Device and Raman Spectroscopy for the Analysis of Whole Blood and Abnormal Blood

Blood testing is a crucial application in the field of clinical studies for disease diagnosis and screening, biomarker discovery, organ function assessment, and the personalization of medication. Therefore, it is of the utmost importance to collect precise data in a short time. In this study, we utilized Raman spectroscopy to analyze blood samples for the extraction of comprehensive biological information, including the primary components and compositions present in the blood. Short-wavelength (532 nm green light) Raman scattering spectroscopy was applied for the analysis of the blood samples, plasma, and serum for detection of the biological characteristics in each sample type. Our results indicated that the whole blood had a high hemoglobin content, which suggests that hemoglobin is a major component of blood. The characteristic Raman peaks of hemoglobin were observed at 690, 989, 1015, 1182, 1233, 1315, and 1562-1649 cm-1. Analysis of the plasma and serum samples indicated the presence of β-carotene, which exhibited characteristic peaks at 1013, 1172, and 1526 cm-1. This novel 3D silicon micro-channel device technology holds immense potential in the field of medical blood testing. It can serve as the basis for the detection of various diseases and biomarkers, providing real-time data to help medical professionals and patients better understand their health conditions. Changes in biological data collected in this manner could potentially be used for clinical diagnosis.

Vibrational spectroscopy methods for investigation of the animal models of glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most common and devastating primary brain tumor among adults. It is highly lethal disease, as only 25% of patients survive longer than 1 year and only 5% more than 5 years from the diagnosis. To search for the new, more effective methods of treatment, the understanding of mechanisms underlying the process of tumorigenesis is needed. The new light on this problem may be shed by the analysis of biochemical anomalies of tissues affected by tumor growth. Therefore, in the present work, we applied the Fourier transform infrared (FTIR) and Raman microspectroscopy to evaluate changes in the distribution and structure of biomolecules appearing in the rat brain as a result of glioblastoma development. In turn, synchrotron X-ray fluorescence microscopy was utilized to determine the elemental anomalies appearing in the nervous tissue. To achieve the assumed goals of the study animal models of GBM were used. The rats were subjected to the intracranial implantation of glioma cells with different degree of invasiveness. For spectroscopic investigation brain slices taken from the area of cancer cells administration were used. The obtained results revealed, among others, the decrease content of lipids and compounds containing carbonyl groups, compositional and structural changes of proteins as well as abnormalities in the distribution of low atomic number elements within the region of tumor.

Physicochemical understanding of biomineralization by molecular vibrational spectroscopy: From mechanism to nature

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.

Detection of primary myelofibrosis in blood serum via Raman spectroscopy assisted by machine learning approaches; correlation with clinical diagnosis

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell-derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, the molecular marker of PM, which is a mutation in the JAK2V617F gene, was observed also in other myeloproliferative neoplasms such as polycythemia vera and essential thrombocythemia. Therefore, there is a need to find methods that provide a marker unique to PM and allow for higher accuracy of PM diagnosis and consequently the treatment of the disease. Continuing, in this study, we used Raman spectroscopy, Principal Components Analysis (PCA), and Partial Least Squares (PLS) analysis as helpful diagnostic tools for PM. Consequently, we used serum collected from PM patients, which were classified using clinical parameters of PM such as the dynamic international prognostic scoring system (DIPSS) for primary myelofibrosis plus score, the JAK2V617F mutation, spleen size, bone marrow reticulin fibrosis degree and use of hydroxyurea drug features. Raman spectra showed higher amounts of C-H, C-C and C-C/C-N and amide II and lower amounts of amide I and vibrations of CH3 groups in PM patients than in healthy ones. Furthermore, shifts of amides II and I vibrations in PM patients were noticed. Machine learning methods were used to analyze Raman regions: (i) 800 cm-1 and 1800 cm-1, (ii) 1600 cm-1-1700 cm-1, and (iii) 2700 cm-1-3000 cm-1 showed 100 % accuracy, sensitivity, and specificity. Differences in the spectral dynamic showed that differences in the amide II and amide I regions were the most significant in distinguishing between PM and healthy subjects. Importantly, until now, the efficacy of Raman spectroscopy has not been established in clinical diagnostics of PM disease using the correlation between Raman spectra and PM clinical prognostic scoring. Continuing, our results showed the correlation between Raman signals and bone marrow fibrosis, as well as JAKV617F. Consequently, the results revealed that Raman spectroscopy has a high potential for use in medical laboratory diagnostics to quantify multiple biomarkers simultaneously, especially in the selected Raman regions.

Central and Peripheral Fatigue Evaluation during Physical Exercise in Athletic Horses by Means of Raman Spectroscopy

The evaluation of the performance levels in athletic horses is of major importance to prevent sports injuries. Raman spectroscopy is an innovative technique that allows for a rapid evaluation of biomolecules in biological fluids. It also permits qualitative and quantitative sample analyses, which lead to the simultaneous determination of the components of the examined biological fluids. On the basis of this, the Raman spectroscopy technique was applied on serum samples collected from five Italian Saddle horses subjected to a standardized obstacle course preceded by a warm-up to evaluate the applicability of this technique for the assessment of central and peripheral fatigue in athletic horses. Blood samples were collected via jugular venipuncture in a vacutainer tube with a clot activator before exercise, immediately after exercise, and 30 min and 1 h after the end of the obstacle course. Observing the obtained Raman spectra, the major changes due to the experimental conditions appeared in the (1300-1360) cm^-1 and (1385-1520) cm^-1 bands. In the (1300-1360) cm^-1 band, lipids and tryptophan were identified; in the (1385-1520) cm^-1 band, leucine, glycine, isoleucine, lactic acid, tripeptide, adenosine, and beta carotene were identified. A significant effect of exercise was recorded on all the sub-bands. In particular, a change immediately after exercise versus before exercise was found. Moreover, the mean lactic concentration was positively correlated with the Raman area of the sub-band assigned to lactic acid. In this context, the application of Raman spectroscopy on blood serum samples represents a useful technique for secondary-structure protein identification to investigate the metabolic changes that occur in athletic horses during physical exercise.

Carotenoids contribution in rapid diagnosis of multiple sclerosis by Raman spectroscopy

Rapid and accurate diagnosis of any illness determines the success of treatment. The same applies to multiple sclerosis (MS), chronic, inflammatory, and neurodegenerative diseases (ND) of the central nervous system (CNS). Unfortunately, the definitive diagnosis of MS is prolonged and involves mainly clinical symptoms observation and magnetic resonance imaging (MRI) of the CNS. However, as we previously reported, Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy shed new light on the minimally invasive, label-free, and rapid diagnosis of this illness through blood fraction. Herein we introduce Raman spectroscopy coupled with chemometric analysis to provide more detailed information about the biochemical changes behind MS. This pilot study demonstrates that mentioned combination may provide a new diagnostic biomarker and bring closer to rapid MS diagnosis. It has been shown that Raman spectroscopy provides lipid and carotenoid molecules as useful biomarkers which may be applied for both diagnosis and treatment monitoring.

Detecting Pre-Analytically Delayed Blood Samples for Laboratory Diagnostics Using Raman Spectroscopy

In this proof-of-principle study, we systematically studied the potential of Raman spectroscopy for detecting pre-analytical delays in blood serum samples. Spectra from 330 samples from a liver cirrhosis cohort were acquired over the course of eight days, stored one day at room temperature, and stored subsequently at 4 °C. The spectra were then used to train Convolutional Neural Networks (CNN) to predict the delay to sample examination. We achieved 90% accuracy for binary classification of the serum samples in the groups "without delay" versus "delayed". Spectra recorded on the first day could be distinguished clearly from all subsequent measurements. Distinguishing between spectra taken in the range from the second to the last day seems to be possible as well, but currently, with an accuracy of approximately 70% only. Importantly, filtering out the fluorescent background significantly reduces the precision of detection.

Raman spectroscopy-based biomarker screening by studying the fingerprint and lipid characteristics of Polycythemıa Vera cases blood serum

This study aimed to develop a novel approach for diagnosing Polycythemia Vera (PV), a stem cell-derived neoplasm of the myeloid lineage. The approach utilized Raman spectroscopy coupled with multivariate analysis to analyze blood serum samples collected from PV patients. The results showed that PV serum exhibited lower protein and lipid levels and structural changes in the functional groups that comprise proteins and lipids. The study also demonstrated differences in lipid biosynthesis and protein levels in PV serum. Using the Partial Least Square Discriminant Analysis (PLS-DA) model, Raman-based multivariate analysis achieved high accuracy rates of 96.49% and 93.04% in the training sets and 93.10% and 89.66% in the test sets for the 800-1800 cm-1 and 2700-3000 cm-1 ranges, respectively. The area under the curve (AUC) values of the test datasets were calculated as 0.92 and 0.89 in the 800-1800 cm-1 and 2700-3000 cm-1 spectral regions, respectively, demonstrating the effectiveness of the PLS-DA models for the diagnosis of PV. This study highlights the potential of Raman spectroscopy-based analysis in the early and accurate diagnosis of PV, enabling the application of effective treatment strategies.

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

This work aims at studying Raman spectroscopy in combination with chemometrics as an alternative fast noninvasive method to detect chronic heart failure (CHF) cases. Optical analysis is focused on the changes in the spectral features associated with the biochemical composition changes of skin tissues. A portable spectroscopy setup with the 785 nm excitation wavelength was used to record skin Raman features. In this in vivo study, 127 patients and 57 healthy volunteers were involved in measuring skin spectral features by Raman spectroscopy. The spectral data were analyzed with a projection on the latent structures and discriminant analysis. 202 skin spectra of patients with CHF and 90 skin spectra of healthy volunteers were classified with 0.888 ROC AUC for the 10-fold cross validated algorithm. To identify CHF cases, the performance of the proposed classifier was verified by means of a new test set that is equal to 0.917 ROC AUC.

Raman Scattering-Based Optical Sensing Of Chronic Liver Diseases

Chronic liver diseases (CLDs) are a major public health problem. Despite the progress achieved in fighting against viral hepatitis, the emergence of non-alcoholic fatty liver disease might pose a serious challenge to the public's health in the coming decades. Medical management of CLDs represents a substantial burden on the public health infrastructures. The health care cost of these diseases is an additional burden that weighs heavily on the economies of developing countries. Effective management of CLDs requires the adoption of reliable and cost-effective screening and diagnosing methods to ensure early detection and accurate clinical assessment of these diseases. Vibrational spectroscopies have emerged as universal analytical methods with promising applications in various industrial and biomedical fields. These revolutionary analytical techniques rely on analyzing the interaction between a light beam and the test sample to generate a spectral fingerprint. This latter is defined by the analyte's chemical structure and the molecular vibrations of its functional groups. Raman spectroscopy and surface-enhanced Raman spectroscopy have been used in combination with various chemometric tests to diagnose a wide range of malignant, metabolic and infectious diseases. The aim of the current review is to cast light on the use of these optical sensing methods in the diagnosis of CLDs. The vast majority of research works that investigated the potential application of these spectroscopic techniques in screening and detecting CLDs were discussed here. The advantages and limitations of these modern analytical methods, as compared with the routine and gold standard diagnostic approaches, were also reviewed in details.

Calibration of spectra in presence of non-stationary background using unsupervised physics-informed deep learning

Calibration is a key part of the development of a diagnostic. Standard approaches require the setting up of dedicated experiments under controlled conditions in order to find the calibration function that allows one to evaluate the desired information from the raw measurements. Sometimes, such controlled experiments are not possible to perform, and alternative approaches are required. Most of them aim at extracting information by looking at the theoretical expectations, requiring a lot of dedicated work and usually involving that the outputs are extremely dependent on some external factors, such as the scientist experience. This work presents a possible methodology to calibrate data or, more generally, to extract the information from the raw measurements by using a new unsupervised physics-informed deep learning methodology. The algorithm allows to automatically process the data and evaluate the searched information without the need for a supervised training by looking at the theoretical expectations. The method is examined in synthetic cases with increasing difficulties to test its potentialities, and it has been found that such an approach can also be used in very complex behaviours, where human-drive results may have huge uncertainties. Moreover, also an experimental test has been performed to validate its capabilities, but also highlight the limits of this method, which, of course, requires particular attention and a good knowledge of the analysed phenomena. The results are extremely interesting, and this methodology is believed to be applied to several cases where classic calibration and supervised approaches are not accessible.

Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning

The aim of this paper is a multivariate analysis of SERS characteristics of serum in hemodialysis patients, which includes constructing classification models (PLS-DA, CNN) by the presence/absence of end-stage chronic kidney disease (CKD) with dialysis and determining the most informative spectral bands for identifying dialysis patients by variable importance distribution. We found the spectral bands that are informative for detecting the hemodialysis patients: the 641 cm^-1, 724 cm^-1, 1094 cm^-1 and 1393 cm^-1 bands are associated with the degree of kidney function inhibition; and the 1001 cm^-1 band is able to demonstrate the distinctive features of hemodialysis patients with end-stage CKD.

Quantifying Serum Total Lipids and Tryptophan Concentrations by Raman Spectroscopy During Standardized Obstacle Course in Horses

Raman spectroscopy is an inelastic light-scattering phenomenon that provides vibrational spectrum that contains information relative to chemical bonds and symmetry of a specific molecule, allowing the quali-quantitative simultaneous determination of several components in the biological fluids. Raman spectroscopy measurement returns a spectrum over a wavenumber range constituted by several bands representing biomarkers according to investigated biological matrices. In literature, it has been reported that at the frequencies inside the (1,300-1,360) cm^-1 total lipids, and tryptophan have been identified. On the basis of that, the aim of the present study was to compare the serum concentration of total lipids and tryptophan in horses subjected to a standardized obstacle course, in comparison with the data obtained in the (1,300-1,360) cm^-1 band. At this purpose, five clinically healthy and regularly trained Italian Saddle horses aged between 8, and 10 years old performed with the same rider an obstacle course of 350 m/min with twelve 1.30 m high jumps preceded by warm-up. Blood samples were collected by means of jugular venipuncture into a vacutainer tubes with clot activator at rest, after the exercise, and 30 minutes after the end of exercise. A high correlation was observed between the area of total lipids and tryptophan in the (1,300-1,360) cm^-1 band and their serum concentrations in all experimental conditions. Our preliminary results give a hint to study the exact correspondence between the area that identify these parameters in Raman spectrum and their serum concentration in athletic horses.

Discrimination of Different Breast Cell Lines on Glass Substrate by Means of Fourier Transform Infrared Spectroscopy

Fourier transform infrared (FTIR) micro-spectroscopy has been attracting the interest of many cytologists and histopathologists for several years. This is related to the possibility of FTIR translation in the clinical diagnostic field. In fact, FTIR spectra are able to detect changes in biochemical cellular components occurring when the cells pass to a pathological state. Recently, this interest has increased because it has been shown that FTIR spectra carried out just in the high wavenumber spectral range (2500-4000 cm^-1), where information mainly relating to lipids and proteins can be obtained, are able to discriminate cell lines related to different tissues. This possibility allows to perform IR absorption measurements of cellular samples deposited onto microscopy glass slides (widely used in the medical environment) which are transparent to IR radiation only for wavenumber values larger than 2000 cm^-1. For these reasons, we show that FTIR spectra in the 2800-3000 cm^-1 spectral range can discriminate three different cell lines from breast tissue: a non-malignant cell line (MCF10A), a non-metastatic adenocarcinoma cell line (MCF7) and a metastatic adenocarcinoma cell line (MDA). All the cells were grown onto glass slides. The spectra were discriminated by means of a principal component analysis, according to the PC1 component, whose values have the opposite sign in the pairwise score plots. This result supports the wide studies that are being carried out to promote the translation of the FTIR technique in medical practice, as a complementary diagnostic tool.