Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation

Aggregation of Apo/Glycated Human Serum Albumins and Aptamer-Saturated Graphene Quantum Dot: A Simulation Study

Human serum albumin (HSA) is a protein carrier that transports a wide range of drugs and nutrients. The amount of glycated HSA (GHSA) is used as a diabetes biomarker. To quantify the GHSA amount, the fluorescent graphene-based aptasensor has been a successful method. In aptasensors, the key mechanism is the adsorption/desorption of albumin from the aptamer-graphene complex. Recently, the graphene quantum dot (GQD) has been reported to be an aptamer sorbent. Due to its comparable size to aptamers, it is attractive enough to explore the possibility of GQD as a part of an albumin aptasensor. Therefore, molecular dynamics (MD) simulations were performed here to reveal the binding mechanism of albumin to an aptamer-GQD complex in molecular detail. GQD saturated by albumin-selective aptamers (GQDA) is studied, and GHSA and HSA are studied in comparison to understand the effect of glycation. Fast and spontaneous albumin-GQDA binding was observed. While no specific GQDA-binding site on both albumins was found, the residues used for binding were confined to domains I and III for HSA and domains II and III for GHSA. Albumins were found to bind preferably to aptamers rather than to GQD. Lysines and arginines were the main contributors to binding. We also found the dissociation of GLC from all GHSA trajectories, which highlights the role of GQDA in interfering with the ligand binding affinity in Sudlow site I. The binding of GQDA appears to impair albumin structure and function. The insights obtained here will be useful for the future design of diabetes aptasensors.

Label-free plasmonic spectral profiling of serum DNA

Genetic and epigenetic modifications are linked to the activation of oncogenes and inactivation of tumor suppressor genes. Likewise, the associated molecular alternations can best inform precision medicine for personalized tumor treatment. Therefore, performing characterization of genetic and epigenetic alternations at the molecular level represents a crucial step in early diagnosis and/or therapeutics of cancer. However, the prevailing methods for DNA analysis involve a series of tedious and complicated steps, in which important genetic and epigenetic information could be lost or altered. To provide a potential approach for non-invasive, direct, and efficient DNA analysis, herein, we present a promising strategy for label-free molecular profiling of serum DNA in its pristine form by fusing surface-enhanced Raman spectroscopy with machine learning on a superior plasmonic nanostructured platform. Using DNA methylation and single-point mutation as two case studies, the presented strategy allows a well-balanced sensitive and specific detection of epigenetic and genetic changes at the single-nucleotide level in serum. We envision the presented label-free strategy could serve as a versatile tool for direct molecular profiling in pristine forms of a wide range of biological markers and aid biomedical diagnostics as well as therapeutics.

Analytical applications of droplet deposition Raman spectroscopy

The droplet deposition methods in Raman spectroscopy have received considerable attention in the field of analytical sensing focusing on effective pre-concentration of the studied analyte (coffee-ring effect or small spots). This review covers different analytical applications of drop-coating deposition Raman scattering (DCDRS) and droplet deposition surface-enhanced Raman scattering (SERS) spectroscopy. Two main advantages of droplet deposition Raman techniques are considered: the drying-induced segregation of the components from the mixtures (such as body fluids) and the sensitivity of detection of various analytically important molecules. Some recent advanced applications, including clinical cancer diagnosis, are discussed and summarized. Finally, the potential and further perspectives of the droplet deposition Raman methods for analytical studies are introduced.

Engineering vascularized skin-mimetic phantom for non-invasive Raman spectroscopy

Recent advances in Raman spectroscopy have shown great potential for non-invasive analyte sensing, but the lack of a standardized optical phantom for these measurements has hindered further progress. While many research groups have developed optical phantoms that mimic bulk optical absorption and scattering, these materials typically have strong Raman scattering, making it difficult to distinguish metabolite signals. As a result, solid tissue phantoms for spectroscopy have been limited to highly scattering tissues such as bones and calcifications, and metabolite sensing has been primarily performed using liquid tissue phantoms. To address this issue, we have developed a layered skin-mimetic phantom that can support metabolite sensing through Raman spectroscopy. Our approach incorporates millifluidic vasculature that mimics blood vessels to allow for diffusion akin to metabolite diffusion in the skin. Furthermore, our skin phantoms are mechanically mimetic, providing an ideal model for development of minimally invasive optical techniques. By providing a standardized platform for measuring metabolites, our approach has the potential to facilitate critical developments in spectroscopic techniques and improve our understanding of metabolite dynamics in vivo.

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy

Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

A selective LSPR biosensor for molecular-level glycated albumin detection

A biosensor specifically engineered to detect glycated albumin (GA), a critical biomarker for diabetes monitoring, is presented. Unlike conventional GA monitoring methods, the biosensor herein uniquely employs localised surface plasmon resonance (LSPR) for signal transduction, leveraging a novel fabrication process where gold nanoparticles are deposited on a quartz substrate using flame spray pyrolysis. This enables the biosensor to provide mean glucose levels over a three-week period, correlating with the glycation status of diabetes patients. The sensor's DNA aptamer conjugation selectively binds GA, inducing a plasmonic wavelength shift; resulting in a detection limit of 0.1 μM, well within the human GA range of 20-240 μM. Selectivity experiments with diverse molecules and an exploration of sensor reusability were carried out with positive results. The novelty of the biosensor presented includes specificity, sensitivity and practical applicability; which is promising for enhanced diabetes diagnosis using a rapid and inexpensive process.

Developing New Diagnostic Tools Based on SERS Analysis of Filtered Salivary Samples for Oral Cancer Detection

Cancer still represents one of the biggest challenges in current medical practice. Among different types of cancer, oral cancer has a huge impact on patients due to its great visibility, which is more likely to create social stigma and increased anxiety. New early diagnose methods are still needed to improve treatment efficiency and patients' life quality. Raman/SERS (Surface Enhanced Raman Spectroscopy) spectroscopy has a unique and powerful potential for detecting specific molecules that can become priceless biomarkers in different pathologies, such as oral cancer. In this study, a batch of saliva samples obtained from a group of 17 patients with oro-maxillofacial pathologies compared with saliva samples from 18 healthy donors using the aforementioned methods were evaluated. At the same time, opiorphin, potassium thiocyanate and uric acid were evaluated as potential specific biomarkers for oro-maxillofacial pathologies using multivariate analysis. A careful examination of SERS spectra collected on saliva samples showed that the spectra are dominated by the vibrational bands of opiorphin, potassium thiocyanate and uric acid. Given the fact that all these small molecules are found in very small amounts, we filtrated all the samples to get rid of large molecules and to improve our analysis. By using solid plasmonic substrates, we were able to gain information about molecular concentration and geometry of interaction. On the other hand, the multivariate analysis of the salivary spectra contributed to developing a new detection method for oral cancer.

Binding of Apo and Glycated Human Serum Albumins to an Albumin-Selective Aptamer-Bound Graphene Quantum Dot Complex

Diabetes mellitus is a chronic metabolic disease involving continued elevated blood glucose levels. It is a leading cause of mortality and reduced life expectancy. Glycated human serum albumin (GHSA) has been reported to be a potential diabetes biomarker. A nanomaterial-based aptasensor is one of the effective techniques to detect GHSA. Graphene quantum dots (GQDs) have been widely used in aptasensors as an aptamer fluorescence quencher due to their high biocompatibility and sensitivity. GHSA-selective fluorescent aptamers are first quenched upon binding to GQDs. The presence of albumin targets results in the release of aptamers to albumin and consequently fluorescence recovery. To date, the molecular details on how GQDs interact with GHSA-selective aptamers and albumin remain limited, especially the interactions of an aptamer-bound GQD (GQDA) with an albumin. Thus, in this work, molecular dynamics simulations were used to reveal the binding mechanism of human serum albumin (HSA) and GHSA to GQDA. The results show the rapid and spontaneous assembly of albumin and GQDA. Multiple sites of albumins can accommodate both aptamers and GQDs. This suggests that the saturation of aptamers on GQDs is required for accurate albumin detection. Guanine and thymine are keys for albumin-aptamer clustering. GHSA gets denatured more than HSA. The presence of bound GQDA on GHSA widens the entrance of drug site I, resulting in the release of open-chain glucose. The insight obtained here will serve as a base for accurate GQD-based aptasensor design and development.

Surface-enhanced Raman spectroscopy of centrifuged blood serum samples of diabetic type II patients by using 50KDa filter devices

Blood serum contains essential biochemical information which are used for early disease diagnosis. Blood serum consisted of higher molecular weight fractions (HMWF) and lower molecular weight fractions (LMWF). The disease biomarkers are lower molecular weight fraction proteins, and their contribution to disease diagnosis is suppressed due to higher molecular weight fraction proteins. To diagnose diabetes in early stages are difficult because of the presence of huge amount of these HMWF. In the current study, surface-enhanced Raman spectroscopy (SERS) are employed to diagnose diabetes after centrifugation of serum samples using Amicon ultra filter devices of 50 kDa which produced two fractions of whole blood serum of filtrate, low molecular weight fraction, and residue, high molecular weight fraction. Furthermore SERS is employed to study the LMW fractions of healthy and diseased samples. Some prominent SERS bands are observed at 725 cm^-1, 842 cm^-1, 1025 cm^-1, 959 cm^-1, and 1447 cm^-1 due to small molecular weight proteins, and these biomarkers helped to diagnose the disease early stage. Moreover, chemometric techniques such as principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are employed to check the potential of surface-enhanced Raman spectroscopy for the differentiation and classifications of the blood serum samples. SERS can be employed for the early diagnosis and screening of biochemical changes during type II diabetes.

Application of SERS-based nanobiosensors to metabolite biomarkers of CKD

A clinical diagnosis of chronic kidney disease (CKD) is commonly achieved by estimating the serum levels of urea and creatinine (CR). Given the limitations of the conventional diagnostic assays, it is imperative to seek alternative, economical strategies for the detection of CKD-specific biomarkers with high specificity and selectivity. In this respect, surface-enhanced Raman spectroscopy (SERS) can be regarded as an ideal choice. SERS signals can be greatly amplified by noble metal nanoparticles (e.g., gold nanoparticles (GNPs)) of numerous sizes, shapes, and configurations to help achieve ultra-sensitive single molecule-level detection at 10^-15 M (up to 10 orders of magnitude more sensitive than fluorescence-based detection). The irregular geometry of GNPs with spike-like tips, dimers, and aggregates with small nanogaps (i.e., due to plasmon coupling such as Raman hot spots) play a pivotal role in enhancing the specificity and sensitivity of SERS. This review critically outlines the performance of SERS-based biosensors in the ultrasensitive detection of CKD biomarkers in various body fluids in terms of basic quality assurance parameters (e.g., limit of detection, figure of merit, enhancement factor, and stability of the biosensor). Moreover, the challenges and perspectives are described with respect to the expansion of such sensing techniques in practical clinical settings.

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods

Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may progress to end-stage renal disease with a high mortality rate, such as renal failure and uremia. At present, the diagnosis of membranous nephropathy mainly relies on the clinical manifestations of patients and pathological examination of kidney biopsy, which are expensive, time-consuming, and have certain chance and other disadvantages. Therefore, there is an urgent need to find a rapid, accurate and non-invasive diagnostic technique for the diagnosis of membranous nephropathy. In this study, Raman spectra of serum and urine were combined with deep learning methods to diagnose membranous nephropathy. After baseline correction and smoothing of the data, Gaussian white noise of different decibels was added to the training set for data amplification, and the amplified data were imported into ResNet, AlexNet and GoogleNet models to obtain the evaluation results of the models for membranous nephropathy. The experimental results showed that the three deep learning models achieved an accuracy of 1 for the classification of serum data of patients with membranous nephropathy and control group, and the discrimination of urine data was above 0.85, among which AlexNet was the best classification model for both samples. The above experimental results illustrate the great potential of serum- and urine-based Raman spectroscopy combined with deep learning methods for rapid and accurate identification of patients with membranous nephropathy.

A SERS based clinical study on HIV-1 viral load quantification and determination of disease prognosis

In resource limited settings, a cost-effective point-of-care diagnostic testing possessing the characteristics of detecting the minimum viral load of a malady like human immunodeficiency virus (HIV) acquired immune deficiency syndrome (AIDS) is a pressing priority. The present work describes a novel, rapid and field-deployable method using surface enhanced Raman spectroscopy (SERS) for detection and prognosis of HIV positive clinical samples, in seven different viral load ranges varying between 200 and 1 million copies/ml. A relationship between the increasing and decreasing intensity peaks of HIV-1 was also established for quantitation efficacy of the handheld tool. Three different types of SERS substrates: single arm Ag nanorods, double arm Ag nanorods and Au sputtered single arm Ag nanorods were used and the obtained data was compared for the three substrates. It was demonstrated that maximum enhancement was obtained for Au sputtered Ag nanorods. Rigorous coupled wave analysis (RCWA) simulations were performed to study the 'hotspots' in three different SERS substrates. Further, to explore the utility of our platform and to differentiate between the clade specific X4 and R5 tropism, their corresponding SERS spectra were studied using HIV-1 strains belonging to four different HIV-1 subtypes (A, B, C and D) which showed a clear distinction, implying the usefulness of the platform in understanding the disease prognosis. Statistical analysis of the obtained SERS spectra using principal component analysis (PCA) showed good agreement with the experimental results, confirming the ability of SERS platform to quantitate HIV-1 viral load and distinguish HIV-1 strains on the basis of their SERS spectra.

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.

Polydopamine Nanoparticles Functionalized Electrochemical DNA Aptasensor for Serum Glycated Albumin Detection

Polydopamine (PDA) has now been widely applied to electrochemical biosensing because of its excellent biocompatibility, abundant functional groups, and facile preparation. In this study, polydopamine nanoparticles (PDA-NPs)-functionalized electrochemical aptasensor was developed for the rapid, sensitive, and cost-effective detection of glycated albumin (GA), a promising biomarker for glycemic control in diabetic patients. PDA-NPs were synthesized at various pH conditions in Tris buffer. Cyclic voltammetry (CV) of PDA-NPs-coated screen-printed carbon electrodes (SPCEs) revealed that the materials were more conductive when PDA-NPs were synthesized at pH 9.5 and 10.5 than that at pH 8.5. At pH 10.5, the prepared PDA and PDA-aptamer NPs were monodispersed spherical morphology with an average size of 118.0 ± 1.9 and 127.8 ± 2.0 nm, respectively. When CV and electrochemical impedance spectrometry (EIS) were used for the characterization and detection of the electrochemical aptasensor under optimal conditions, the proposed aptasensor exhibited a broad linearity for detection of GA at a clinically relevant range of (1-10,000 µg mL^-1), provided a low detection limit of 0.40 µg mL^-1, appreciable reproducibility (less than 10%), and practicality (recoveries 90-104%). In addition, our developed aptasensor presented a great selectivity towards GA, compared to interfering substances commonly present in human serum, such as human serum albumin, urea, glucose, and bilirubin. Furthermore, the evaluation of the aptasensor performance against GA-spiked serum samples showed its probable applicability for clinical use. The developed PDA aptasensor demonstrated excellent sensitivity and selectivity towards GA detection with a simple and facile fabrication process. This proposed technique shows its potential application in GA measurement for improving the screening and management of diabetic patients in the future.

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study

Simple Summary

In recent years, research on biofluids using Raman and SERS has expanded dramatically, indicating the enormous promise of this technology as a high-throughput tool for identifying cancer and other disorders. In the investigations thus far, researchers have concentrated on a specific illness or condition, but the techniques employed to acquire experimental spectra prevent direct comparison of the data. This necessitates comparative research of a variety of diseases and an increase in scientific cooperation to standardize experimental conditions. In our study, positive results were reached by applying a combined SERS multivariate analysis (MVA) to the urgent problem of prostate cancer diagnosis that was directly linked to real-world settings in healthcare. Moreover, in comparison to the prostate-specific antigen (PSA) test, which has a high sensitivity but limited specificity, our combined SERS-MVA method has greater specificity, which may assist in preventing the overtreatment of patients.

Abstract

It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis—support vector machine (PCA-SVM).

Diagnosing COVID-19 in human serum using Raman spectroscopy

This study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC2, PC4, PC5, and PC6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free, and costless technique for diagnosing COVID-19 infection.

Spectropathologic endorsement of ocular carotenoids for early detection of diabetic retinopathy

Diabetic retinopathy (DR) is a common health concern. Unfortunately, the metabolic pathway causing DR is yet to be understood. The carotenoid level in the human body is known to protect the health of the eyes. In this work, resonance Raman spectroscopy and multivariate analysis of the spectral data of human serum are reported as next-generation spectropathologic tools to detect retinal degeneration efficiently. The proposed technique shows promise by endorsing ocular carotenoids as a critical biomarker for such pathosis. Furthermore, the multivariate analysis of the spectral data distinguishes between two different stages of the disease. The machine learning algorithm is used to estimate a significant accuracy of 94% of the proposed model for the classification. As the carotenoid level can be controlled by dietary intake, we believe that the reported results also indicate a therapeutic role of the same in DR.

Diagnosing COVID-19 in human serum using Raman spectroscopy

This study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC2, PC4, PC5, and PC6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free, and costless technique for diagnosing COVID-19 infection.

Diagnosing COVID-19 in human sera with detected immunoglobulins IgM and IgG by means of Raman spectroscopy

The severe COVID-19 pandemic requires the development of novel, rapid, accurate, and label-free techniques that facilitate the detection and discrimination of SARS-CoV-2 infected subjects. Raman spectroscopy has been used to diagnose COVID-19 in serum samples of suspected patients without clinical symptoms of COVID-19 but presented positive immunoglobulins M and G (IgM and IgG) assays versus Control (negative IgM and IgG). A dispersive Raman spectrometer (830 nm, 350 mW) was employed, and triplicate spectra were obtained. A total of 278 spectra were used from 94 serum samples (54 Control and 40 COVID-19). The main spectral differences between the positive IgM and IgG versus Control, evaluated by principal component analysis (PCA), were features assigned to proteins including albumin (lower in the group COVID-19 and in the group IgM/IgG and IgG positive) and features assigned to lipids, phospholipids, and carotenoids (higher the group COVID-19 and in the group IgM/IgG positive). Features referred to nucleic acids, tryptophan, and immunoglobulins were also seen (higher the group COVID-19). A discriminant model based on partial least squares regression (PLS-DA) found sensitivity of 84.0%, specificity of 95.0%, and accuracy of 90.3% for discriminating positive Ig groups versus Control. When considering individual Ig group versus Control, it was found sensitivity of 77.3%, specificity of 97.5%, and accuracy of 88.8%. The higher classification error was found for the IgM group (no success classification). Raman spectroscopy may become a technique of choice for rapid serological evaluation aiming COVID-19 diagnosis, mainly detecting the presence of IgM/IgG and IgG after COVID-19 infection.

Nanostructured Plasma Polymerized Fluorocarbon Films for Drop Coating Deposition Raman Spectroscopy (DCDRS) of Liposomes

Raman spectroscopy is one of the most used biodetection techniques. However, its usability is hampered in the case of low concentrated substances because of the weak intensity of the Raman signal. To overcome this limitation, the use of drop coating deposition Raman spectroscopy (DCDRS), in which the liquid samples are allowed to dry into well-defined patterns where the non-volatile solutes are highly concentrated, is appropriate. This significantly improves the Raman sensitivity when compared to the conventional Raman signal from solution/suspension. As DCDRS performance strongly depends on the wetting properties of substrates, we demonstrate here that the smooth hydrophobic plasma polymerized fluorocarbon films prepared by magnetron sputtering (contact angle 108°) are well-suited for the DCDRS detection of liposomes. Furthermore, it was proved that even better improvement of the Raman signal might be achieved if the plasma polymer surfaces are roughened. In this case, 100% higher intensities of Raman signal are observed in comparison with smooth fluorocarbon films. As it is shown, this effect, which has no influence on the profile of Raman spectra, is connected with the increased hydrophobicity of nanostructured fluorocarbon films. This results in the formation of dried liposomal deposits with smaller diameters and higher preconcentration of liposomes.

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis

In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis.

BSA-drug-ZnO-PEI conjugates interaction with glycans of gp60 endothelial cell receptor protein for targeted drug delivery: a comprehensive spectroscopic study

The zinc oxide (ZnO) nanoparticles (NPs) have several biomedical applications such as drug delivery, bio-imaging, and biomedical research. ZnO NPs were remedied with polyethyleneimine (PEI) and modified with bovine serum albumin (BSA). Two anticancer drugs - Cisplatin (CIS) and Gemcitabine (GEM) were used in conjugation with BSA. BSA-ZnO-PEI (conjugate 1), BSA-CIS-ZnO-PEI (conjugate 2), and BSA-GEM-ZnO-PEI (conjugate 3) can be used for targeted drug delivery via glycans - N-acetylneuraminic acid (NANA), L-fucose (FUC), N-acetyl glucosamine (NAG), D-mannose (MAN), and D-galactose (GAL), of albumin binding membrane receptor protein (gp60). Considerable interaction and the strong binding of conjugate 2 and conjugate 3 with NANA were observed by UV-visible absorption and fluorescence spectra. The electrostatic stability of conjugate 2 and conjugate 3 with NANA was considerably increased in comparison to conjugate 1 as evident with zeta potential values. The fluorescence quenching data (Ksv and kq) and binding parameters (K and n) of BSA-CIS, BSA-GEM, conjugate 2, and conjugate 3 with NANA and FUC attributes to the strong binding. Amide I and amide III bands of the Raman signal suggested insignificant loss in alpha-helical and beta-sheet content of conjugate 2 and conjugate 3 with NANA and FUC. Therefore, the present study is going to assist in the comprehensive development of conjugates for targeted drug delivery based on the differential glycation pattern of gp60 protein.Communicated by Ramaswamy H. Sarma.

In depth investigation of collagen non-enzymatic glycation by Raman spectroscopy

Non-enzymatic glycation is a post-translational modification of long-lived matrix proteins such as type I collagen. It occurs during aging and leads to the formation of advanced glycation end-products (AGEs). AGE accumulation is associated with severe complications in chronic and age-related diseases. The assessment of modifications induced by this (patho)physiological process represents an interest in biology and medicine for a better patient care. The objective of our work was to position the interest of Raman spectroscopy in the quantification of collagen glycation. Two types of in vitro glycation were used by incubating collagen samples, at different durations, with ribose or glyoxylic acid; these reducing agents acting on the chemical specificity of the glycation reaction. Glycation efficiency was evaluated by the liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) quantification of carboxymethyllysine (CML) and pentosidine, which are among the most studied AGEs. Raman data were processed by PCA coupled to validity indices and Lasso regression as multivariate analysis tools. Regression models were constructed by considering the LC-MS/MS results as reference values. A marked variability was observed within the Raman datasets making difficult the identification of spectral differences between control and ribose-treated collagen samples. By taking advantage of the chemical specificity of the glyoxylic acid treatment leading to CML formation, on one hand, and the feature selection included in the Lasso algorithm, on the other hand, Raman markers associated with glycation were identified. The assigned vibrations corresponded to modifications of side chains of collagen. In addition, a threshold of CML concentration was determined as quantitative indicator of the applicability of Raman spectroscopy for potential patient follow-up purposes. Although lacking in sensitivity to directly detect AGEs in collagen, Raman spectroscopy allows to highlight the molecular modifications of collagen induced by glycation.

Combination of pathological and spectroscopic characterization to promote diagnosis of retinal pigment epithelium-Bruch's membrane complex in a diabetic rat model

Diabetic retinopathy (DR) is a common condition of diabetes, and approaches to detecting early DR using the unique characteristics of the retinal pigment epithelium-Bruch's membrane complex (RBC) have increasingly attracted attention. A diabetic model was established in Sprague-Dawley rats via streptozocin (STZ) injection for 1 (DM1) and 6 months (DM6), confirmed by weekly blood glucose measurement. Serum and retinal tissue-based advanced glycation endproducts (AGE) levels significantly elevated in diabetic rats, and RBC was evaluated by transmission electron microscopy and Raman spectroscopy. The results showed that whole Raman spectra and all marked band intensities could respectively achieve almost equal and accurate discrimination of all animal groups, along with the determination of important molecules from the band data. Further quantitative analyses indicated series of metabolic disturbance due to hyperglycemia were involved while the body self-regulation mechanism still played a role with different effects during the disease progression. Given this, Raman spectroscopy can reliably distinguish the early characterization of DR in addition to providing intrinsic key molecules that is sensitive to identify the early disease progression.

Immobilization-Free Electrochemical Sensor Coupled with a Graphene-Oxide-Based Aptasensor for Glycated Albumin Detection

An immobilization-free electrochemical sensor coupled with a graphene oxide (GO)-based aptasensor was developed for glycated human serum albumin (GHSA) detection. The concentration of GHSA was monitored by measuring the electrochemical response of free GO and aptamer-bound GO in the presence of glycated albumin; their currents served as the analytical signals. The electrochemical aptasensor exhibited good performance with a base-10 logarithmic scale. The calibration curve was achieved in the range of 0.01-50 µg/mL. The limit of detection (LOD) was 8.70 ng/mL. The developed method was considered a one-drop measurement process because a fabrication step and the probe-immobilization process were not required. This simple sensor offers a cost-effective, rapid, and sensitive detection method, and could be an alternative approach for determination of GHSA levels.

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.

Silver-Coated Disordered Silicon Nanowires Provide Highly Sensitive Label-Free Glycated Albumin Detection through Molecular Trapping and Plasmonic Hotspot Formation

Glycated albumin (GA) is rapidly emerging as a robust biomarker for screening and monitoring of diabetes. To facilitate its rapid, point-of-care measurements, a label-free surface-enhanced Raman spectroscopy (SERS) sensing platform is reported that leverages the specificity of molecular vibrations and signal amplification on silver-coated silicon nanowires (Ag/SiNWs) for highly sensitive and reproducible quantification of GA. The simulations and experimental measurements demonstrate that the disordered orientation of the nanowires coupled with the wicking of the analyte molecules during the process of solvent evaporation facilitates molecular trapping at the generated plasmonic hotspots. Highly sensitive detection of glycated albumin is shown with the ability to visually detect spectral features at as low as 500 × 10^-9 m, significantly below the physiological range of GA in body fluids. Combined with chemometric regression models, the spectral data recorded on the Ag/SiNWs also allow accurate prediction of glycated concentration in mixtures of glycated and non-glycated albumin in proportions that reflect those in the bloodstream.

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions. One aim of this study is to use light sources with multiple wavelengths to enhance the sensitivity and selectivity of glucose detection in an aqueous solution. Multiple wavelength measurements have the potential to compensate for errors associated with inter- and intra-individual differences in blood and tissue components. In this study, the transmission measurements of a custom built optical sensor are examined using 18 different wavelengths between 410 and 940 nm. Results show a high correlation value (0.98) between glucose concentration and transmission intensity for four wavelengths (485, 645, 860 and 940 nm). Five machine learning methods are investigated for glucose predictions. When regression methods are used, 9% of glucose predictions fall outside the correct range (normal, hypoglycemic or hyperglycemic). The prediction accuracy is improved by applying classification methods on sets of data arranged into 21 classes. Data within each class corresponds to a discrete 10 mg/dL glucose range. Classification based models outperform regression, and among them, the support vector machine is the most successful with F1-score of 99%. Additionally, Clarke error grid shows that 99.75% of glucose readings fall within the clinically acceptable zones. This is an important step towards critical diagnosis during an emergency patient situation.

Development of a colorimetric paper fluidic dipstick assay for measurement of glycated albumin to monitor gestational diabetes at the point-of-care

Gestational diabetes mellitus (GDM) affects between 2 and 14% of pregnant women in the United States every year. Currently, glucose and hemoglobin A1c (HbA1c) are the standard biomarkers used to monitor GDM but HbA1c is representative of 2-3 months of glycemic data and is too infrequent for managing clinical impact of GDM while glucose provides multiple daily readings which arguably are not entirely necessary for mild to moderate GDM and often result in non-compliance from the patient's side. Thus, there is a need for an intermediate biomarker which can be used effectively to monitor the glycemic status of GDM patients. Serum albumin, the most abundant protein in blood, undergoes non-enzymatic glycation in the bloodstream. Owing to its half-life of ~21 days, it can effectively be used as an intermediate biomarker. Normal level of glycation of albumin is between 10 and 16% whereas in diabetic patients it is much higher, between 16 and 40%. Thus, a point-of-care (POC) monitoring system to detect glycated albumin (GA) as a % of total serum albumin has been developed here. Specifically, a dipstick paper fluidic test to measure % glycated albumin has been developed that used an aptamer assay with gold nanoparticles to produce colorimetric measurements. Both the glycated and unglycated versions of albumin were measured in their relevant physiological concentration ranges - 50 μM-300 μM with a limit of detection (LoD) of 6.5 μM for glycated albumin and 500 μM-750 μM with a LoD of 21 μM for unglycated serum albumin. The use of aptamers as recognition elements, instead of commonly used antibodies, not only provided the required sensitivity, specificity, and dynamic range but they also have the added advantage of being stable at room temperature for an extended period of time providing the potential for these dipstick tests to be used for GDM monitoring at the point-of-care (POC).

Surface-enhanced Raman scattering analysis of serum albumin via adsorption-exfoliation on hydroxyapatite nanoparticles for noninvasive cancers screening

Combining serum albumin via adsorption-exfoliation on hydroxyapatite particles (HAp) with surface-enhanced Raman scattering (SERS), we developed a novel quantitative analysis of albumin method from blood serum for cancers screening applications. The quantitatively analysis obtained by our HAp method had a good linear relationship from 1 to 10 g/dL, and the lower limit of detection was less than the albumin prognostic factor for disease (3.5 g/dL). Serum albumin was adsorbed and exfoliated by HAp from serum samples of liver cancer patients, breast cancer patients and healthy volunteers and mixed with silver colloids to perform SERS spectral analysis. Based on the PLS-SVM algorithm, the diagnostic accuracies of liver cancer patients and breast cancer patients were 100% and 96.68%, respectively. Moreover, this algorithm successfully predicted the unidentified subjects with a diagnostic accuracy of 93.75%. This exploratory work demonstrated that HAp-adsorbed-exfoliated serum proteins combined with SERS spectroscopy has great potential for cancer screening.

Tailoring Chemometric Models on Blood-Derived Cultures Secretome to Assess Personalized Cancer Risk Score

The molecular protonation profiles obtained by means of an organic electrochemical transistor, which is used for analysis of molecular products released by blood-derived cultures, contain a large amount of information The transistor is based on the conductive polymer PEDOT:PSS comprising super hydrophobic SU8 pillars positioned on the substrate to form a non-periodic square lattice to measure the state of protonation on secretomes derived from liquid biopsies. In the extracellular space of cultured cells, the number of glycation products increase, driven both by a glycolysis metabolism and by a compromised function of the glutathione redox system. Glycation products are a consequence of the interaction of the reactive aldehydes and side glycolytic products with other molecules. As a result, the amount of the glycation products reflects the anti-oxidative cellular reserves, counteracting the reactive aldehyde production of which both the secretome protonation profile and cancer risk are related. The protonation profiles can be profitably exploited through the use of mathematical techniques and multivariate statistics. This study provides a novel chemometric approach for molecular analysis of protonation and discusses the possibility of constructing a predictive cancer risk model based on the exploration of data collected by conventional analysis techniques and novel nanotechnological devices.

Novel Assessment of Urinary Albumin Excretion in Type 2 Diabetes Patients by Raman Spectroscopy

Urinary albumin excretion remains the key biomarker to detect renal complications in type 2 diabetes. As diabetes epidemy increases, particularly in low-income countries, efficient and low-cost methods to measure urinary albumin are needed. In this pilot study, we evaluated the performance of Raman spectroscopy in the assessment of urinary albumin in patients with type 2 diabetes. The spectral Raman analysis of albumin was performed using artificial urine, at five concentrations of albumin and 24 h collection urine samples from ten patients with Type 2 Diabetes. The spectra were obtained after removing the background fluorescence and fitting Gaussian curves to spectral regions containing features of such metabolites. In the samples from patients with type 2 diabetes, we identified the presence of albumin in the peaks of the spectrum located at 663.07, 993.43, 1021.43, 1235.28, 1429.91 and 1633.91 cm⁻¹. In artificial urine, there was an increase in the intensity of the Raman signal at 1450 cm⁻¹, which corresponds to the increment of the concentrations of albumin. The highest concentration of albumin was located at 1630 cm⁻¹. The capability of Raman spectroscopy for detection of small concentrations of urinary albumin suggests the feasibility of this method for the screening of type 2 diabetes renal complications.

Quantification of protein glycation using vibrational spectroscopy

FTIR-ATR and Raman spectroscopy can distinguish between glycated and non-glycated proteins.

Diagnosing sickle cell disease and iron deficiency anemia in human blood by Raman spectroscopy

This work proposed the diagnosis of iron deficiency anemia (IDA) and sickle cell disease (SCD) in human blood caused by iron deficiency and hemoglobin S (HbS), which are among the most common anemias, by means of Raman spectroscopy. Whole blood samples from patients diagnosed with IDA and HbS, as well as from normal subjects (HbA), were obtained and submitted to Raman spectroscopy (830 nm, 150 mW, 400-1800 cm^-1 spectral range, 4 cm^-1 resolution). Difference spectra of IDA-HbA showed spectral features of hemoglobin with less intensity in the IDA, whereas the difference spectra of SCD-HbA showed spectral features of deoxyhemoglobin increased and of oxyhemoglobin decreased in SCD. An exploratory analysis by principal components analysis (PCA) showed that the peaks referred to oxy- and deoxyhemoglobin markedly differentiated SCD and HbA, as well as the increased amount of hemoglobin features in the SCD group, suggesting increased erythropoiesis. The IDA group showed hemoglobin features with lower intensities as well as peaks referred to the iron bonding to the porphyrin ring with reduced intensities when compared to the HbA. Discriminant analysis based on partial least squares (PLS-DA) and PCA (PCA-DA) showed that the IDA and SCD anemias could be discriminated from the HbA spectra with 95.0% and 93.8% of accuracy, for the PLS and PCA respectively, with sensitivity/specificity of 93.8%/95.7% for the PLS-DA model. The iron depletion and the sickling of erythrocytes could be identified by Raman spectroscopy and a spectral model based on PLS accurately discriminated these IDA and SCD samples from the normal HbA.

Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools: comment

This paper comments on the article "Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools" by E. Guevara et al. The authors propose an optical method for noninvasive automated screening of type 2 diabetes mellitus. Despite the high performance of the proposed method, results shown by the authors may be ambiguous due to the overestimation of classification models for Raman spectral data analysis.

Optical screening of hepatitis-B infected blood sera using optical technique and neural network classifier

In this study we demonstrate the analysis of biochemical changes in the human blood sera infected with Hepatitis B virus (HBV) using Raman spectroscopy. In total, 120 diseased blood samples and 170 healthy blood samples, collected from Pakistan Atomic Energy Commission (PAEC) general hospital, were analyzed. Spectra from each sample of both groups were collected in the spectral range 400-1700 cm^-1. Careful spectral analyses demonstrated significant spectral variations (p < 0.0001) in the HBV infected individuals as compared to the normal ones. The spectral variations presumably occur because of the variations in the concentration of important biomolecules. Variations in spectral signatures were further exploited by using a neural network classifier towards machine-assisted classification of the two groups. Evaluation metrics of the classifier showed the diagnostic accuracy of (0.993), sensitivity ( = 0.992), specificity ( = 0.994), positive predictive value ( = 0.992) and negative predictive value ( = 0.994). The observed variations in the molecular concentration may be important markers of the hepatic performance and can be used in the diagnosis and machine-assisted classification of HBV infection.

Raman spectral signatures of urinary extracellular vesicles from diabetic patients and hyperglycemic endothelial cells as potential biomarkers in diabetes

Raman spectroscopy was applied to the measurement of urinary and in vitro endothelium-derived extracellular vesicles (EVs) isolated by hydrostatic filtration dialysis (HFD) method. Raman spectra obtained for urinary EVs (UEVs) showed distinct differences in the fingerprint region. In contrast, average Raman spectra of endothelium-derived EVs samples were almost identical. Cluster Analysis of UEVs significantly discriminated diabetic samples from control, moreover endothelium-derived EVs revealed stronger similarity between long hyperglycemia and normoglycemia samples compared to short hyperglycemia. Results obtained from Partial Least Squares analysis corresponded well with integral intensities of selected bands. Our proof-of-concept approach demonstrates the potential for Raman spectroscopy to be used both for identification of EVs molecular signatures in urine samples from patients with type 2 diabetes mellitus and good glycemic control and unsatisfactory glycemic control as well as for in vitro hyperglycemic model. This noninvasive technique may be useful in identifying new biomarkers of diabetes and renal complications.

Light Concentration by Metal-Dielectric Micro-Resonators for SERS Sensing

Metal-dielectric micro/nano-composites have surface plasmon resonances in visible and near-infrared domains. Excitation of coupled metal-dielectric resonances is also important. These different resonances can allow enhancement of the electromagnetic field at a subwavelength scale. Hybrid plasmonic structures act as optical antennae by concentrating large electromagnetic energy in micro- and nano-scales. Plasmonic structures are proposed for various applications such as optical filters, investigation of quantum electrodynamics effects, solar energy concentration, magnetic recording, nanolasing, medical imaging and biodetection, surface-enhanced Raman scattering (SERS), and optical super-resolution microscopy. We present the review of recent achievements in experimental and theoretical studies of metal-dielectric micro and nano antennae that are important for fundamental and applied research. The main impact is application of metal-dielectric optical antennae for the efficient SERS sensing.

Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools

Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy. Using artificial neural networks (ANN), we accurately discriminated between DM2 and Ctrl groups with 88.9-90.9% accuracy, depending on the sampling site. In order to compare the ANN performance to more traditional methods used in spectroscopy, principal component analysis (PCA) was carried out. A subset of features from PCA was used to generate a support vector machine (SVM) model, albeit with decreased accuracy (76.0-82.5%). The 10-fold cross-validation model was performed to validate both classifiers. This technique is relatively low-cost, harmless, simple and comfortable for the patient, yielding rapid diagnosis. Furthermore, the performance of the ANN-based method was better than the typical performance of the invasive measurement of capillary blood glucose. These characteristics make our method a promising screening tool for identifying DM2 in a non-invasive and automated fashion.

Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM

A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly, the real GA content in human serum was determined by GA enzymatic method, meanwhile, the ATR-FTIR spectra of serum samples from the population of health examination were obtained. The spectral data of the whole spectra mid-infrared region (4000-600 cm^-1) and GA's characteristic region (1800-800 cm^-1) were used as the research object of quantitative analysis. Secondly, several preprocessing steps including first derivative, second derivative, variable standardization and spectral normalization, were performed. Lastly, quantitative analysis regression models were established by using SiPLS and SVM respectively. The SiPLS modeling results are as follows: root mean square error of cross validation (RMSECV_T) = 0.523 g/L, calibration coefficient (R_C) = 0.937, Root Mean Square Error of Prediction (RMSEP_T) = 0.787 g/L, and prediction coefficient (R_P) = 0.938. The SVM modeling results are as follows: RMSECV_T = 0.0048 g/L, R_C = 0.998, RMSEP_T = 0.442 g/L, and R_p = 0.916. The results indicated that the model performance was improved significantly after preprocessing and optimization of characteristic regions. While modeling performance of nonlinear SVM was considerably better than that of linear SiPLS. Hence, the quantitative analysis model for GA in human serum based on ATR-FTIR combined with SiPLS and SVM is effective. And it does not need sample preprocessing while being characterized by simple operations and high time efficiency, providing a rapid and accurate method for GA content determination.