Surface-enhanced Raman spectroscopy for characterization of filtrate portions of blood serum samples of typhoid patients

Surface-enhanced Raman spectroscopy for the characterization of filtrate portions of blood serum samples of myocardial infarction patients using 30 kDa centrifugal filter devices

Myocardial infarction (MI) is the leading cause of death and disability worldwide. It occurs when a thrombus forms after an atherosclerotic plaque bursts, obstructing blood flow to the heart. Prompt and accurate diagnosis is crucial for improving patient survival. Surface-enhanced Raman spectroscopy (SERS) offers quick response and excellent resolution for qualitative and quantitative analysis of body fluids, making it a valuable diagnostic tool. This study explores SERS for identifying proteins in blood serum samples from MI patients, focusing on cardiac Troponin I (cTnI), a key biomarker. Due to the small size and low concentration of cTnI, its SERS signal may be weak or absent. To address this, 30 kDa filtering devices are used to obtain filtrate portions of serum samples from cTnI-positive patients and healthy individuals. SERS spectral analysis of these filtrates identifies key SERS bands associated with biomolecular changes related to cTnI levels. Principal component analysis (PCA) effectively differentiates SERS spectra from healthy and MI-positive patients. Partial least squares regression (PLSR) quantifies cTnI levels based on SERS features, with a model showing R2 value of 0.79 and RMSEC of 1.37, validating its accuracy.

Surface-enhanced Raman spectroscopy (SERS) for the diagnosis of acute myocardial infarction (AMI) using blood serum samples

Acute myocardial infarction (AMI) is a serious medical condition generally known as heart attack, which is caused by the decreased or completely blocked blood flow to a part of the heart muscle. It is a significant cause of both mortality and morbidity throughout the world. Cardiac troponin-I (cTnI) is an important biomarker at different stages of AMI and is one of the most specific and widely used cardiac skeletal muscle proteins. Delays in medical treatment and inaccurate diagnosis might be the main cause of death of AMI patients. To overcome the death rate of AMI patients, early diagnosis of this disease is crucial. In the current study, surface-enhanced Raman spectroscopy (SERS) is employed for the characterization and diagnosis of this disease using blood serum samples from 49 clinically confirmed acute myocardial infarction (AMI) patients and 17 healthy persons. Silver nanoparticles (AgNPs) are used as the SERS substrate for the recognition of characteristic SERS spectral features, differentiating between healthy and AMI-positive samples. The acute myocardial infarction-positive blood serum samples reveal remarkable differences in spectral intensities at 534, 697, 744, 835, 927, 941, 988, 1221, 1303, 1403, 1481, 1541, 1588 and 1694 cm-1. For the differentiation and quantitative analysis of the SERS spectra, multivariate chemometric tools (including principal component analysis (PCA) and partial least squares regression (PLSR)) are employed. A PLSR model established on the basis of differentiating the SERS spectral features is found to be helpful in the prediction of the levels of cardiac troponin-I (cTnI) in the blood serum samples with the root mean square error of calibration (RMSEC) value of 2.98 ng mL-1 and root mean square errors of prediction (RMSEP) value of 3.98 ng mL-1 for S7.

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.

Surface-Enhanced Raman Spectroscopy for Monitoring the Biochemical Changes Due to DNA Mutations Induced by CRISPR-Cas9 Genome Editing in the Aspergillus niger Fungus

In this study, surface-enhanced Raman spectroscopy (SERS) technique, along with principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA), is used as a simple, quick, and cost-effective analysis method for identifying biochemical changes occurring due to induced mutations in the Aspergillus niger fungus strain. The goal of this study is to identify the biochemical changes in the mutated fungal cells (cell mass) as compared to the control/nonmutated cells. Furthermore, multivariate data analysis tools, including PCA and PLS-DA, are used to further confirm the differentiating SERS spectral features among fungal samples. The mutations are caused in A. niger by the clustered regularly interspaced palindromic repeat CRISPR-Cas9 genomic editing method to improve their biotechnological potential for the production of cellulase enzyme. SERS was employed to detect the changes in the cells of mutated A. niger fungal strains, including one mutant producing low levels of an enzyme and another mutant producing high levels of the enzyme as a result of mutation as compared with an unmutated fungal strain as a control sample. The distinctive features of SERS corresponding to nucleic acids and proteins appear at 546, 622, 655, 738, 802, 835, 959, 1025, 1157, 1245, 1331, 1398, and 1469 cm-1. Furthermore, PLS-DA is used to confirm the 89% accuracy, 87.7% precision, 87% sensitivity, and 88.9% specificity of this method, and the value of the area under the curve (AUROC) is 0.67. It has been shown that surface-enhanced Raman spectroscopy is an effective method for identifying and differentiating biochemical changes in genome-modified fungal samples.

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.

Review of quantitative and qualitative methods for monitoring photopolymerization reactions

Authomatic in-situ monitoring and characterization of photopolymerization.