Raman spectroscopy-based sensitive and specific detection of glycated hemoglobin

Emerging biosensor probes for glycated hemoglobin (HbA1c) detection

Glycated hemoglobin (HbA1c), originating from the non-enzymatic glycosylation of βVal1 residues in hemoglobin (Hb), is an essential biomarker indicating average blood glucose levels over a period of 2 to 3 months without external environmental disturbances, thereby serving as the gold standard in the management of diabetes instead of blood glucose testing. The emergence of HbA1c biosensors presents affordable, readily available options for glycemic monitoring, offering significant benefits to small-scale laboratories and clinics. Utilizing nanomaterials coupled with high-specificity probes as integral components for recognition, labeling, and signal transduction, these sensors demonstrate exceptional sensitivity and selectivity in HbA1c detection. This review mainly focuses on the emerging probes and strategies integral to HbA1c sensor development. We discussed the advantages and limitations of various probes in sensor construction as well as recent advances in diverse sensing strategies for HbA1c measurement and their potential clinical applications, highlighting the critical gaps in current technologies and future needs in this evolving field.

Quantification of Glycated Hemoglobin in Total Hemoglobin by a Simultaneous Dual-Signal Acquisition Approach

The glycated hemoglobin (HbA1c) level, which is defined as the ratio of HbA1c to total hemoglobin (tHb, including glycated and unglycated hemoglobin), is considered one of the preferred indicators for diabetes monitoring. Generally, assessment of the HbA1c level requires separate determination of tHb and HbA1c concentrations after a complex separation step. This undoubtedly increases the cost of the assay, and the loss or degradation of HbA1c during the separation process results in a decrease in the accuracy of the assay. Therefore, this study explored a dual-signal acquisition method for the one-step simultaneous evaluation of tHb and HbA1c. Quantification of tHb: graphene adsorbed carbon quantum dots and methylene blue were utilized as the substrate material and linked to the antibody. tHb was captured on the substrate by the antibody. The unique heme group on tHb catalyzed the production of •OH from H2O2 to degrade methylene blue on the substrate, and a quantitative relationship between the tHb concentration and the methylene blue oxidation current signal was constructed. Quantification of HbA1c: complex labels with HbA1c recognition were made of ZIF-8-ferrocene-gold nanoparticles-mercaptophenylboronic acid. The specific recognition of the boronic acid bond with the unique cis-diol structure of HbA1c establishes a quantitative relationship between the oxidation current of the label-loaded ferrocene and the concentration of HbA1c. Thus, the HbA1c level can be assessed with only one signal readout. The sensor exhibited extensive detection ranges (0.200-600 ng/mL for tHb and 0.100-300 ng/mL for HbA1c) and low detection limits (4.00 × 10-3 ng/mL for tHb and 1.03 × 10-2 ng/mL for HbA1c).

Power of Light: Raman Spectroscopy and Machine Learning for the Detection of Lung Cancer

Lung cancer is the leading cause of cancer-related deaths worldwide, emphasizing the urgent need for reliable and efficient diagnostic methods. Conventional approaches often involve invasive procedures and can be time-consuming and costly, thereby delaying the effective treatment. The current study explores the potential of Raman spectroscopy, as a promising noninvasive technique, by analyzing human blood plasma samples from lung cancer patients and healthy controls. In a benchmark study, 16 machine learning models were evaluated by employing four strategies: the combination of dimensionality reduction with classifiers; application of feature selection prior to classification; stand-alone classifiers; and a unified predictive model. The models showed different performances due to the inherent complexity of the data, achieving accuracies from 0.77 to 0.85 and areas under the curve for receiver operating characteristics from 0.85 to 0.94. Hybrid methods incorporating dimensionality reduction and feature selection algorithms present the highest figures of merit. Nevertheless, all machine learning models deliver creditable scores and demonstrate that Raman spectroscopy represents a powerful method for future in vitro diagnostics of lung cancer.

Comparison of Glycated Hemoglobin (HbA1c) Values Estimated by High-Performance Liquid Chromatography and Spectrophotometry: A Pilot Study

Background Invasive blood sample collection followed by high-performance liquid chromatography (HPLC) based analysis is the gold standard for estimating glycated hemoglobin level or HbA1c currently. Spectrophotometry could be an alternative that holds the potential to be translated into a portable, non-invasive device for glycated hemoglobin level estimation. This study compares HbA1c values obtained from HPLC and spectrophotometry. Methods Venous blood samples were collected from both diabetic and non-diabetic participants in a cross-sectional study. The samples were subjected to both HPLC and spectrophotometry-based estimation of HbA1c%. The results obtained were compared, and the relationship between the two estimations were assessed. Results About 15 diabetic and non-diabetic individuals participated in the study and 28 samples were included in the final analysis. The Pearson's correlation coefficient was 0.65 (95% CI, 0.37-0.82), indicating that there was a strong positive association. This was further supported by the findings from linear regression analysis with a p-value of <0.001. Conclusions The positive correlation between the HPLC and spectrophotometric values supports the hypothesis that spectrophotometry could be an alternative to conventional HPLC for the measurement of HbA1c. This needs to be further validated through larger, well-powered studies.

Raman spectroscopy analysis of plasma of diabetes patients with and without retinopathy, nephropathy, and neuropathy

Diabetes is now one of the major public health challenges, globally. Prolonged diabetes leads to various diabetic microvascular complications (DMCs) like retinopathy, nephropathy, and neuropathy. Multiple factors are likely to be involved in predisposing diabetic individuals to complications. Early detection or diagnosis is essential in developing strategies to reduce the risk factors and management costs of these diabetic complications. In this study, we employed Raman Spectroscopy (RS) to analyse the plasma samples of diabetes patients without and with DMCs along with the plasma samples of healthy subjects. Spectral comparisons revealed decrease in protein content in Diabetes group and further subsequent decrease in proteins in DMC groups when compared with control group, which corroborates with the fact that there exists increased secretion of proteins in urine and corresponding decreased protein content in their blood in case of diabetic individuals. Among all study groups, it was noted that 75% of control spectra show correct classification, while spectral misclassification is high amongst the subjects with Diabetes and DMCs. Interestingly, very few Diabetes and DMC plasma spectra are misclassified as control spectra. Findings demonstrate that 70% of the Diabetes subjects without complications can be correctly identified from diabetes with complications. Further, investigations could also attempt to explore the use of serum instead of plasma to reduce the spectral misclassifications as one of the abundant constituents namely clotting factors could be avoided. The outcome of RS study may be imminent for the early detection or diagnosis of DMCs.

Protein glycation in diabetes mellitus

Diabetes mellitus is the ninth leading cause of mortality worldwide. It is a complex disease that manifests as chronic hyperglycemia. Glucose exposure causes biochemical changes at the proteome level as reflected in accumulation of glycated proteins. A prominent example is hemoglobin A1c (HbA1c), a glycated protein widely accepted as a diabetic indicator. Another emerging biomarker is glycated albumin which has demonstrated utility in situations where HbA1c cannot be used. Other proteins undergo glycation as well thus impacting cellular function, transport and immune response. Accordingly, these glycated counterparts may serve as predictors for diabetic complications and thus warrant further inquiry. Fortunately, modern proteomics has provided unique analytic capability to enable improved and more comprehensive exploration of glycating agents and glycated proteins. This review broadly covers topics from epidemiology of diabetes to modern analytical tools such as mass spectrometry to facilitate a better understanding of diabetes pathophysiology. This serves as an attempt to connect clinically relevant questions with findings of recent proteomic studies to suggest future avenues of diabetes research.

Quantitative Label-Free SERS Detection of Trace Fentanyl in Biofluids with a Freestanding Hydrophobic Plasmonic Paper Biosensor

The prevalence of fentanyl abuse raises global public health concerns with an unprecedented surge in overdose deaths. Rapid identification and quantification of fentanyl in biofluids is of paramount importance to combat fentanyl abuse for law enforcement agencies and promptly treat patients for medical professionals. Herein, a freestanding surface-enhanced Raman spectroscopy (SERS) biosensor with excellent condensing enrichment capability, termed FrEnSERS biosensor, is reported for quantitative label-free detection of trace fentanyl in biofluids. This biosensor comprises a reduced graphene oxide membrane decorated with high-density hydrophobic Au nanostars. A combination of the high SERS enhancement and the focusing effect for analyte enrichment of the hydrophobic surface accounts for the remarkable SERS performance of the FrEnSERS biosensor. We demonstrate that the FrEnSERS biosensor achieves the sensitive and quantitative detection of fentanyl in both serum and urine over a wide dynamic range spanning more than 4 orders of magnitude, with a limit of detection of 0.47 ng/mL for serum samples and 0.73 ng/mL for urine samples. Our biosensor is sensitive, cost-effective, and reliable for rapid quantitative analysis of fentanyl in biofluids with great promise for forensic analysis and clinical diagnosis.

Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks

Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.

Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes

In this article, we investigated the feasibility of using Raman spectroscopy and multivariate analysis method to noninvasively screen for prediabetes and diabetes in vivo. Raman measurements were performed on the skin from 56 patients with diabetes, 19 prediabetic patients and 32 healthy volunteers. These spectra were collected along with reference values provided by the standard glycated hemoglobin (HbA1c) assay. A multiclass principal component analysis and support vector machine (PCA-SVM) model was created from the labeled Raman spectra and was validated through a two-layer cross-validation scheme. Classification accuracy of the model was 94.3% with an area under the receiver operating characteristic curve AUC of 0.76 (0.65-0.84) for the prediabetic group, 0.86 (0.71-0.93) for the diabetic group and 0.97(0.93-0.99) for the control group. Our results suggest the feasibility of using Raman spectroscopy for the classification of prediabetes and diabetes in vivo.

A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept

Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a "coffee ring". MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R²_CV.

Label-free in vivo assessment of brain mitochondrial redox states during the development of diabetic cognitive impairment using Raman spectroscopy

Mitochondrial redox imbalance has been recognized as a unifying cause for diabetic cognitive impairment. Currently, a robust method for the in vivo assessment of brain mitochondrial redox imbalance is still lacking. Here, we conducted a spectral study to assess brain mitochondrial redox imbalance in the process of diabetic cognitive impairment by using label-free resonance Raman spectroscopy (RRS). Our findings showed that mitochondrial redox imbalance in cultured neurons and organotypic cortical slices exposed to high glucose were quantified by the reduction of Raman peak area at 750 cm^-1 and 1128 cm^-1, which were also associated with synaptic injury and neuron apoptosis. Raman peak area at 750 cm^-1 and 1128 cm^-1 were also decreased in db/db mice at the age of 8, 16 and 24 weeks, and had a high correlation with the mitochondrial NAD⁺/NADH redox couple. Of note, this mitochondrial redox imbalance occurred before measurable cognitive decline in 8-week-old diabetic mice, and might signal impending diabetic cognitive impairment. In summary, RRS-based mitochondrial redox states assay enabled the in vivo assessment of brain mitochondrial redox imbalance, and might provide an early indicator to enhance the prediction of diabetic cognitive impairment and inform on the response to therapies targeting mitochondrial redox imbalance.

Noninvasive Spectroscopic Detection of Blood Glucose and Analysis of Clinical Research Status

Frequent measurement of blood glucose concentration in diabetic patients is an important means for diabetes control. Blood glucose monitoring with noninvasive detection technology can not only avoid the pain of patients and eliminate the harm of some biological materials for measuring glucose in vivo but also improve the frequency of detection, so as to control blood glucose concentration more closely. Traditional blood glucose detection methods are invasive and have some limitations. In this study, the significance of noninvasive blood glucose testing was analyzed and was pointed out that noninvasive blood glucose testing can monitor the blood glucose concentration of patients and relieve the pain of patients. Then, this study analyzed the spectral detection methods of noninvasive blood glucose, including conservation of energy metabolism, near infrared spectroscopy, and other spectral detection methods. Finally, this study made a comprehensive analysis of the domestic and international clinical application of noninvasive glucose spectrum monitoring and summarized the clinical application status of noninvasive glucose spectrum monitoring.

IgG N-glycans

Glycosylation, one of the most common post-translational modifications in mammalian cells, impacts many biological processes such as cell adhesion, proliferation and differentiation. As the most abundant glycoprotein in human serum, immunoglobulin G (IgG) plays a vital role in immune response and protection. There is a growing body of evidence suggests that IgG structure and function are modulated by attached glycans, especially N-glycans, and aberrant glycosylation is associated with disease states. In this chapter, we review IgG glycan repertoire and function, strategies for profiling IgG N-glycome and recent studies. Mass spectrometry (MS) based techniques are the most powerful tools for profiling IgG glycome. IgG glycans can be divided into high-mannose, biantennary complex and hybrid types, modified with mannosylation, core-fucosylation, galactosylation, bisecting GlcNAcylation, or sialylation. Glycosylation of IgG affects antibody half-life and their affinity and avidity for antigens, regulates crystallizable fragment (Fc) structure and Fcγ receptor signaling, as well as antibody effector function. Because of their critical roles, IgG N-glycans appear to be promising biomarkers for various disease states. Specific IgG glycosylation can convert a pro-inflammatory response to an anti-inflammatory activity. Accordingly, IgG glycoengineering provides a powerful approach to potentially develop effective drugs and treat disease. Based on the understanding of the functional role of IgG glycans, the development of vaccines with enhanced capacity and long-term protection are possible in the near future.

A microsphere nanoparticle based-serum albumin targeted adsorption coupled with surface-enhanced Raman scattering for breast cancer detection

The serum albumin level is inseparable associated with survival in patients with breast cancer, and simultaneously serve as a good indicator of prognosis of cancer. Here, we proposed a novel extraction-isolation analysis method of albumin for breast cancer detection utilizing hydroxyapatite particles (HAp) to targeted adsorb albumin from serum relying on its specific adsorption capacity. An ideal protein-release reagent was used for isolating albumin from the surface of HAp, and meanwhile ensuring that the structure and property of albumin was not suffered damage. The surface-enhanced Raman scattering (SERS) signal of extracted albumin was obtained, and partial least squares (PLS) and linear discriminant analysis (LDA) analysis approach were employed to analyze SERS spectra data, with the aim to assess the capability of HAp method for identifying breast cancer, yielding an ideal diagnostic accuracy of 98.6%, demonstrating promising potential as a non-invasive and sensitive nanotechnology for breast cancer screening.

Simultaneous detection of glucose, triglycerides, and total cholesterol in whole blood by Fourier-Transform Raman spectroscopy

In this paper, a reagent-free simultaneous and direct detection method of three analytes in human blood based on Fourier-transform Raman (FT-Raman) spectroscopy with 1064 nm laser radiation was proposed for the first time. A total of 161 human blood samples were characterized by FT-Raman spectroscopy under the excitation laser source of 1064 nm. In order to achieve a robust regression model, the Nonlinear Iterative Partial Least Squares (NIPALS) with orthogonal signal correction (OSC) algorithm and sample set partition based on a joint x-y distance (SPXY) is used to establish multivariate calibration models. The root means square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), correlation coefficients (R²) and ratio of performance to deviation (RPD) were 0.34255 mg/dL, 0.3662 mg/dL, 0.99982 and 56.3524 for glucose, 0.33656 mg/dL, 0.75736 mg/dL, 0.99967 and 34.9169 for total cholesterol (TC), and 0.29956 mg/dL, 0.27469 mg/dL, 0.99998 and 173.5098 for triglycerides (TG), respectively. The analysis results showed that the proposed method could be able to accurately predict the concentration of glucose, TC and TG in blood. This method can instantaneous multi-component detection on whole blood.

Vibrational Spectroscopy for Detection of Diabetes: A Review

Type II diabetes mellitus (T2DM) is a metabolic disorder that is characterized by chronically elevated glucose caused by insulin resistance. Although T2DM is manageable through insulin therapy, the disorder itself is a risk factor for much more dangerous diseases including cardiovascular disease, kidney disease, retinopathy, Alzheimer's disease, and more. T2DM affects 450 million people worldwide and is attributed to causing over four million deaths each year. Current methods for detecting diabetes typically involve testing a person's glycated hemoglobin levels as well as blood sugar levels randomly or after fasting. However, these methods can be problematic due to an individual's levels differing on a day-to-day basis or being affected by diet or environment, and due to the lack of sensitivity and reliability within the tests themselves. Vibrational spectroscopic methods have been pursued as a novel method for detecting diabetes accurately and early in a minimally invasive manner. This review summarizes recent research, since 2015, which has used infrared or Raman spectroscopy for the purpose of developing a fast and accurate method for diagnosing diabetes. Based on critical evaluation of the reviewed work, vibrational spectroscopy has the potential to improve and revolutionize the way diabetes is diagnosed, thereby allowing for faster and more effective treatment of the disorder.

Clinical assessment of W-band spectroscopy for non-invasive detection and monitoring of sustained hyperglycemia

HbA1c is the gold standard test for monitoring medium/long term glycemia conditions in diabetes care, which is a critical factor in reducing the risk of chronic diabetes complications. Current technologies for measuring HbA1c concentration are invasive and adequate assays are still limited to laboratory-based methods that are not widely available worldwide. The development of a non-invasive diagnostic tool for HbA1c concentration can lead to the decrease of the rate of undiagnosed cases and facilitate early detection in diabetes care. We present a preliminary validation diagnostic study of W-band spectroscopy for detection and monitoring of sustained hyperglycemia, using the HbA1c concentration as reference. A group of 20 patients with type 1 diabetes mellitus and 10 healthy subjects were non-invasively assessed at three different visits over a period of 7 months by a millimeter-wave spectrometer (transmission mode) operating across the full W-band. The relationship between the W-band spectral profile and the HbA1c concentration is studied using longitudinal and non-longitudinal functional data analysis methods. A potential blind discrimination between patients with or without diabetes is obtained, and more importantly, an excellent relation (R-squared = 0.97) between the non-invasive assessment and the HbA1c measure is achieved. Such results support that W-band spectroscopy has great potential for developing a non-invasive diagnostic tool for in-vivo HbA1c concentration monitoring in humans.

On-Site Detection of SARS-CoV-2 Antigen by Deep Learning-Based Surface-Enhanced Raman Spectroscopy and Its Biochemical Foundations

A rapid, on-site, and accurate SARS-CoV-2 detection method is crucial for the prevention and control of the COVID-19 epidemic. However, such an ideal screening technology has not yet been developed for the diagnosis of SARS-CoV-2. Here, we have developed a deep learning-based surface-enhanced Raman spectroscopy technique for the sensitive, rapid, and on-site detection of the SARS-CoV-2 antigen in the throat swabs or sputum from 30 confirmed COVID-19 patients. A Raman database based on the spike protein of SARS-CoV-2 was established from experiments and theoretical calculations. The corresponding biochemical foundation for this method is also discussed. The deep learning model could predict the SARS-CoV-2 antigen with an identification accuracy of 87.7%. These results suggested that this method has great potential for the diagnosis, monitoring, and control of SARS-CoV-2 worldwide.

Very Selective Detection of Low Physiopathological Glucose Levels by Spontaneous Raman Spectroscopy with Univariate Data Analysis

After decades of research on non-invasive glucose monitoring, invasive devices based on finger blood sampling are still the predominant reference for diabetic patients for accurately measuring blood glucose levels. Meanwhile, research continues improving point-of-care technology toward the development of painless and more accurate devices. Raman spectroscopy is well-known as a potentially valuable and painless approach for measuring glucose levels. However, previous Raman studies deal with glucose concentrations that are still order of magnitudes away with respect to human tissues’ physiological concentrations, or they propose enhancement methodologies either invasive or much complex to assure sufficient sensitivity in the physiological range. Instead, this study proposes an alternative non-enhanced Raman spectroscopy approach sensitive to glucose concentrations from 1 to 5 mmol/l, which correspond to the lowest physiopathological glucose level in human blood. Our findings suggest a very selective detection of glucose with respect to other typical metabolites, usually interfering with Raman spectroscopy’s glucose detection. We validate the proposed univariate sensing methodology on glucose solutions mixed with lactate and urea, the two most common molecules found in human serum with concentrations similar to glucose and similar features in the Raman spectra. Our findings clearly illustrate that reliable detection of glucose by Raman spectroscopy is feasible by exploiting the shifted peak at 1125 ± 10 cm–1 within physiopathological ranges.

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.

Targeting improved reproducibility in surface-enhanced Raman spectroscopy with planar substrates using 3D printed alignment holders

Drop-casting is frequently used to deliver a sample for surface-enhanced Raman spectroscopy (SERS) and can result in inhomogeneous sample distribution during solvent evaporation. While soaking can provide better analyte homogeneity, it may require more sample than is available. Failure to optically sample analyte-rich substrate locations can compromise measurement outcomes. We developed and tested 3D printed SERS substrate holders that provided spatial registry of the dried sample droplet center for subsequent optical measurements. We found that deliberate and controlled spatial offsets (0-900 µm) between the analyte drop center and the laser excitation prevented signal intensity drops of as much as ∼3× and improved reproducibility. Thus, the use of offset-controlled 3D printed holders provided a quick and inexpensive way to improve the reliability of SERS measurements when using the convenient and popular choice of sample drop-casting.

Vibrational Spectroscopic Investigation of Blood Plasma and Serum by Drop Coating Deposition for Clinical Application

In recent decades, vibrational spectroscopic methods such as Raman and FT-IR spectroscopy are widely applied to investigate plasma and serum samples. These methods are combined with drop coating deposition techniques to pre-concentrate the biomolecules in the dried droplet to improve the detected vibrational signal. However, most often encountered challenge is the inhomogeneous redistribution of biomolecules due to the coffee-ring effect. In this study, the variation in biomolecule distribution within the dried-sample droplet has been investigated using Raman and FT-IR spectroscopy and fluorescence lifetime imaging method. The plasma-sample from healthy donors were investigated to show the spectral differences between the inner and outer-ring region of the dried-sample droplet. Further, the preferred location of deposition of the most abundant protein albumin in the blood during the drying process of the plasma has been illustrated by using deuterated albumin. Subsequently, two patients with different cardiac-related diseases were investigated exemplarily to illustrate the variation in the pattern of plasma and serum biomolecule distribution during the drying process and its impact on patient-stratification. The study shows that a uniform sampling position of the droplet, both at the inner and the outer ring, is necessary for thorough clinical characterization of the patient’s plasma and serum sample using vibrational spectroscopy.

Characterization of glycated hemoglobin based on Raman spectroscopy and artificial neural networks

The World Health Organization has declared the glycated hemoglobin (HbA1c) as a gold standard biomarker for diabetes diagnosis; this has led to relevant research on the spectral behavior and characterization of HbA1c. This paper presents an analysis of Raman peaks of commercial lyophilized HbA1c, diluted in distilled water, using concentrations of 4.76% and 9.09%, as well as pure powder (100% concentration). Vibrational Raman peak positions of HbA1c powder were found at 1578, 1571, 1536, 1436, 1311, 1308, 1230, 1222, 1114, 1106, 969, 799 and 665 cm^-1; these values are consistent with results reported in other works. Besides, a nonlinear regression model based on a Feed-Forward Neural Network (FFNN) was built to quantify percentages of HbA1c for unknown concentrations. Using the Raman spectra as independent variables, the regression provided a Root Mean Square Error in Cross-Validation (RMSECV) of 0.08% ± 0.04. We also include a detailed molecular assignment of the average spectra of lyophilized powder of HbA1c.

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.

Probing blood plasma samples for the detection of diabetes using SERS aided by PCA and LDA multivariate data analyses

Fabrication of a SERS-active substrate using Langmuir–Blodgett and self-assembly techniques for the detection of diabetes from blood plasma samples.

Biophotonics methods for functional monitoring of complications of diabetes mellitus

The prevalence of diabetes complications is a significant public health problem with a considerable economic cost. Thus, the timely diagnosis of complications and prevention of their development will contribute to increasing the length and quality of patient life, and reducing the economic costs of their treatment. This article aims to review the current state-of-the-art biophotonics technologies used to identify the complications of diabetes mellitus and assess the quality of their treatment. Additionally, these technologies assess the structural and functional properties of biological tissues, and they include capillaroscopy, laser Doppler flowmetry and hyperspectral imaging, laser speckle contrast imaging, diffuse reflectance spectroscopy and imaging, fluorescence spectroscopy and imaging, optical coherence tomography, optoacoustic imaging and confocal microscopy. Recent advances in the field of optical noninvasive diagnosis suggest a wider introduction of biophotonics technologies into clinical practice and, in particular, in diabetes care units.

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.

Separation detection of hemoglobin and glycated hemoglobin fractions in blood using the electrochemical microfluidic channel with a conductive polymer composite sensor

Separation and detection of hemoglobin (Hb) and glycated hemoglobin fractions (HbA1c, HbAld₁₊₂, HbAle, HbAld3a, HbAl_a+b, HbA2, and HbAld3b) was performed using an electrochemical AC field modulated separation channel (EMSC) coupled with a sensor probe. The sensor was fabricated based on immobilization of a redox mediator on the poly(2,2':5',5″-terthiophene-3'-p-benzoic acid, pTTBA) and N,S-doped porous carbon (NSPC) nanocomposite. The different types of catalytic redox mediators such as Nile Blue (NB), toluidine blue O (TBO), and Neutral Red (NR) were evaluated to achieve the efficient detection. Of these, the NB-based sensor showed the best analytical signal for Hb and HbA1c, thus it was characterized using various electrochemical and surface analysis methods. After that, the sensor was coupled with the EMSC to achieve the separation detection of the Hb family. The frequency and amplitude of the AC electrical field applied onto the EMSC walls were the main driving forces for the separation and sensitive detection of the analytes. Under optimized conditions, linear dynamic ranges for Hb and HbA1c among their fractions were obtained between 1.0 × 10^-6 to 3.5 mM and 3.0 × 10^-6 to 0.6 mM with the detection limit of 8.1 × 10^-7 ± 3.0 × 10^-8 and 9.2 × 10^-7 ± 5 × 10^-8 mM, respectively. Interference effects of other biomolecules were also investigated and the clinical applicability of the device was evaluated by the determination of total Hb and % HbA1c in real human blood samples.

Rapid and non-invasive screening of high renin hypertension using Raman spectroscopy and different classification algorithms

This study presents a rapid and non-invasive method to screen high renin hypertension using serum Raman spectroscopy combined with different classification algorithms. The serum samples taken from 24 high renin hypertension patients and 22 non-high renin hypertension samples were measured in this experiment. Tentative assignments of the Raman peaks in the measured serum spectra suggested specific biomolecular changes between the groups. Principal component analysis (PCA) was first used for feature extraction and reduced the dimension of high-dimension spectral data. Then, support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (KNN) algorithms were employed to establish the discriminant diagnostic models. The accuracies of 93.5%, 93.5% and 89.1% were obtained from PCA-SVM, PCA-LDA and PCA-KNN models, respectively. The results from our study demonstrate that the serum Raman spectroscopy technique combined with multivariate statistical methods have great potential for the screening of high renin hypertension. This technique could be used to develop a portable, rapid, and non-invasive device for screening high renin hypertension.

Label-free quantitation of glycated hemoglobin in single red blood cells by transient absorption microscopy and phasor analysis

As a stable and accurate biomarker, glycated hemoglobin (HbA1c) is clinically used to diagnose diabetes with a threshold of 6.5% among total hemoglobin (Hb). Current methods such as boronate affinity chromatography involve complex processing of large-volume blood samples. Moreover, these methods cannot measure HbA1c fraction at single-red blood cell (RBC) level, thus unable to separate the contribution from other factors such as RBC lifetime. Here, we demonstrate a spectroscopic transient absorption imaging approach that is able to differentiate HbA1c from Hb on the basis of their distinct excited-state dynamics. HbA1c fraction inside a single RBC is derived quantitatively through phasor analysis. HbA1c fraction distribution of diabetic blood is apparently different from that of healthy blood. A mathematical model is developed to derive the long-term blood glucose concentration. Our technology provides a unique way to study heme modification and to derive clinically important information void of bloodstream glucose fluctuation.

Raman spectroscopic evaluation of human serum using metal plate and 785- and 1064-nm excitation lasers

In this study, we utilized a stainless steel (SUS304) plate for measuring the Raman scattering spectra of body fluid samples. Using this stainless steel plate, we recorded the Raman scattering spectra of 99.5% ethanol and human serum samples by performing irradiation with 785- and 1064-nm lasers. Raman scattering spectra with intensities equal to or greater than those reported previously were obtained. In addition, the Raman scattering spectra acquired using the 1064-nm laser were less influenced by autofluorescence than those obtained via use of the shorter-wavelength laser. Moreover, the shapes of the spectra did not show any dependence on integration time, and denaturation of the samples was minimal. Our method, based on 1064-nm laser and the stainless steel plate, provides performance equal to or better than the methods reported thus far for the measurement of Raman scattering spectra from liquid samples. This method can be employed to rapidly evaluate the components of serum in liquid form without using surface-enhanced Raman scattering.

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.

Label-free spectrochemical probe for determination of hemoglobin glycation in clinical blood samples

Glycated hemoglobin, HbA1c, is an important biomarker that reveals the average value of blood glucose over the preceding 3 months. While significant recent attention has been focused on the use of optical and direct molecular spectroscopic methods for determination of HbA1c, a facile test that minimizes sample preparation needs and turnaround time still remains elusive. Here, we report a label-free approach for identifying low, mid and high-HbA1c groups in hemolysate and in whole blood samples featuring resonance Raman (RR) spectroscopy and support vector machine (SVM)-based classification of spectral patterns. The diagnostic power of RR measurements stems from its selective enhancement of hemoglobin-specific features, which simultaneously minimizes the blood matrix spectral interference and permits detection in the native solution. In this pilot study, our spectroscopic observations reveal that glycation of hemoglobin results in subtle but reproducible changes even when detected in the whole blood matrix. Leveraging SVM analysis of the principal component scores determined from the RR spectra, we show high degree of accuracy in classifying clinical specimen. We envisage that the promising findings will pave the way for more extensive clinical specimen investigations with the ultimate goal of translating molecular spectroscopy for routine point-of-care testing.

Molecular Affinity Agents for Intrinsic Surface-Enhanced Raman Scattering (SERS) Sensors

Research at the interface of synthetic materials, biochemistry, and analytical techniques has enabled sensing platforms for applications across many research communities. Herein we review the materials used as affinity agents to create surface-enhanced Raman spectroscopy (SERS) sensors. Our scope includes those affinity agents (antibody, aptamer, small molecule, and polymer) that facilitate the intrinsic detection of targets relevant to biology, medicine, national security, environmental protection, and food safety. We begin with an overview of the analytical technique (SERS) and considerations for its application as a sensor. We subsequently describe four classes of affinity agents, giving a brief overview on affinity, production, attachment chemistry, and first uses with SERS. Additionally, we review the SERS features of the affinity agents, and the analytes detected by intrinsic SERS with that affinity agent class. We conclude with remarks on affinity agent selection for intrinsic SERS sensing platforms.

Fluorescent 2D WS2 Nanosheets Bearing Chemical Affinity Elements for the Recognition of Glycated Hemoglobin

It is required to exfoliate and functionalize 2D transition metal dichalcogenides (TMDs) in an aqueous solution for biological and medical applications. Herein, the approach for the simultaneous exfoliation and functionalization of 2D WS₂ nanosheets using boronic acid-modified poly(vinyl alcohol) (B-PVA) in an aqueous solution is reported, and the B-PVA-functionalized WS₂ nanosheets (B-PVA-WS₂ ) are exploited as a fluorescent biosensor for the detection of glycated hemoglobin, HbA1c. The synthetic B-PVA polymer facilitates the exfoliation and functionalization of WS₂ nanosheets from the bulk counterpart in the aqueous solution via a pulsed sonication process, resulting in fluorescent B-PVA-WS₂ nanohybrids with a specific recognition of HbA1c. The fluorescence of the B-PVA-WS₂ is quenched in the presence of HbA1c, whereas PVA-functionalized WS₂ (PVA-WS₂ ), not bearing boronic acid as a recognition moiety, shows no fluorescence changes upon the addition of the target. The B-PVA-WS₂ is able to selectively detect HbA1c at the concentration as low as 3.3 × 10^-8 m based on its specific fluorescence quenching.

Impedance measurement system for automatic determination of glycated hemoglobin

In this study, an automatic glycated hemoglobin (HbA1c) impedance measurement system (AHMS) is developed for the detection of HbA_1c. The proposed device removes some of the drawbacks of common instruments for HbA_1c detection (i.e., large, expensive, difficult to operate) by detecting the ratio of HbA_1c to Hb. The method is label-free and requires only a small sample volume; no additional reagents are required. The manpower consumption and bulk of the instrument are also reduced. The method provides a simple way to analyze impedance deviation and effectively reduces the effort required by the operator. The ratios of HbA_1c to Hb (4%-7%) are well distinguished, and the experiment is used to build a database for AHMS. To check the reliability of the proposed system, a sample test using three different ratios of HbA_1c is applied in this study. The sample test uses HbA_1c to Hb ratios of 4.7%, 5.6%, and 6.8%, and the determined experimental values are 4.93%, 5.8%, and 6.83%, respectively. The sample test has an accuracy of approximately 96.99%. Based on these results, the proposed system for detecting HbA_1c through protein coverage is both effective and feasible.

Application of a near-infrared laser tweezers Raman spectroscopy system for label-free analysis and differentiation of diabetic red blood cells

A home-made near-infrared laser tweezers Raman spectroscopy (LTRS) system was applied to detect hemoglobin variation in red blood cells (RBCs) from diabetes without exogenous labeling. Results showed significant spectral differences existed between the diabetic and normal RBCs, including the peaks dominated by protein components (e.g. 1003 cm^-1) and heme groups (e.g. 753 cm^-1) in RBCs, and accurate classification results for diabetes detection were obtained by linear discriminant analysis with 100% sensitivity (i.e. no false negatives in the study). This work indicated the great promise of LTRS as a label-free RBC analytical tool for improving the accurate detection of type II diabetes.

A review on suppression and utilization of the coffee-ring effect

Evaporation of sessile droplets containing non-volatile solutes dispersed in a volatile solvent leaves behind ring-like solid stains. As the volatile species evaporates, pinning of the contact line gives rise to capillary flows that transport non-volatile solutes to the contact line. This phenomenon, called the coffee-ring effect, compromises the overall performance of industrially relevant manufacturing processes involving evaporation such as printing, biochemical analysis, manufacturing of nano-structured materials through colloidal and macromolecular patterning. Various approaches have been developed to suppress this phenomenon, which is otherwise difficult to avoid. The coffee-ring effect has also been leveraged to prepare new materials through convection induced assembly. This review underlines not only the strategies developed to suppress the coffee-ring effect but also sheds light on approaches to arrive at novel processes and materials. Working principles and applicability of these strategies are discussed together with a critical comparison.

Sensitive and uniform detection using Surface-Enhanced Raman Scattering: Influence of colloidal-droplets evaporation based on Au-Ag alloy nanourchins

Surface Enhanced Raman Scattering (SERS) has been developed into a powerful vibrational spectroscopy technique for chemical detection. However, the fabrication of colloidal droplets-based SERS substrates with well reproducibility and uniformity still remains challenging. In this paper, colloidal suspensions of hollow Au-Ag alloy nanourchins (HAAA-NUs) and Au nanoparticles (Au NPs) with different morphologies were employed as SERS-active substrates. After evaporation of colloidal suspensions, we evaluated the SERS performance based on the following features: "Coffee Ring Effects", adsorption processes of probe molecule and colloidal NPs, spin coating and morphologies of suspended NPs. The results demonstrated that SERS signals could be enhanced enormously in the marginal region of Coffee Ring patterns. The limit of detection (LOD) for amaranth molecule would be reached 10^-8 M. Moreover, by combining the droplets evaporation of HAAA-NUs suspensions with spin coating, the relative standard deviation (RSD) could be down to 3.5%, showing excellent reproducibility. The investigation here would provide a simple, practical and portable SERS detection method with excellent signal uniformity.

Drop coating deposition Raman spectroscopy of proteinogenic amino acids compared with their solution and crystalline state

The Raman spectra of 20 proteinogenic amino acids were recorded in the solution, glass phase (as drop coating deposition Raman (DCDR) samples) and crystalline forms in the wide spectral range of 200-3200cm^-1. The most apparent spectral differences between the Raman spectra of the crystalline forms, glass phases and aqueous solutions of amino acids were briefly discussed and described in the frame of published works. The possible density dependencies of spectral bands were noted. In some cases, a strong influence of the sample density, as well as of the organization of the water envelope, was observed. The most apparent changes were observed for Ser and Thr. Nevertheless, for the majority of amino acids, the DCDR sample form is an intermediate between the solution and crystalline forms. In contrast, aromatic amino acids have only a small sensitivity to the form of the sample. Our reference set of Raman spectra is useful for revealing discrepancies between the SERS and solid/solution spectra of amino acids. We also found that some previously published Raman spectra of polycrystalline samples resemble glassy state rather than crystalline spectra. Therefore, this reference set of spectra will find application in every branch of Raman spectroscopy where the spectra of biomolecules are collected from coatings.

Application of Raman spectroscopy in type 2 diabetes screening in blood using leucine and isoleucine amino-acids as biomarkers and in comparative anti-diabetic drugs efficacy studies

Diabetes is an irreversible condition characterized by elevated blood glucose levels. Currently, there are no predictive biomarkers for this disease and the existing ones such as hemoglobin A1c and fasting blood glucose are used only when diabetes symptoms are noticed. The objective of this work was first to explore the potential of leucine and isoleucine amino acids as diabetes type 2 biomarkers using their Raman spectroscopic signatures. Secondly, we wanted to explore whether Raman spectroscopy can be applied in comparative efficacy studies between commercially available anti-diabetic drug pioglitazone and the locally used anti-diabetic herbal extract Momordica spinosa (Gilg.)Chiov. Sprague Dawley (SD) rat’s blood was used and were pipetted onto Raman substrates prepared from conductive silver paste smeared glass slides. Prominent Raman bands associated with glucose (926, 1302, 1125 cm⁻¹), leucine (1106, 1248, 1302, 1395 cm⁻¹) and isolecucine (1108, 1248, 1437 and 1585 cm⁻¹) were observed. The Raman bands centered at 1125 cm⁻¹, 1395 cm⁻¹ and 1437 cm⁻¹ associated respectively to glucose, leucine and isoleucine were chosen as biomarker Raman peaks for diabetes type 2. These Raman bands displayed decreased intensities in blood from diabetic SD rats administered antidiabetic drugs pioglitazone and herbal extract Momordica spinosa (Gilg.)Chiov. The intensity decrease indicated reduced concentration levels of the respective biomarker molecules: glucose (1125 cm⁻¹), leucine (1395 cm⁻¹) and isoleucine (1437 cm⁻¹) in blood. The results displayed the power and potential of Raman spectroscopy in rapid (10 seconds) diabetes and pre-diabetes screening in blood (human or rat’s) with not only glucose acting as a biomarker but also leucine and isoleucine amino-acids where intensities of respectively assigned bands act as references. It also showed that using Raman spectroscopic signatures of the chosen biomarkers, the method can be an alternative for performing comparative efficacy studies between known and new anti-diabetic drugs. Reports on use of Raman spectroscopy in type 2 diabetes mellitus screening with Raman bands associated with leucine and isoleucine molecules acting as reference is rare in literature. The use of Raman spectroscopy in pre-diabetes screening of blood for changes in levels of leucine and isoleucine amino acids is particularly interesting as once elevated levels are noticed, necessary interventions to prevent diabetes development can be initiated.

Raman spectroscopy and PCA-SVM as a non-invasive diagnostic tool to identify and classify qualitatively glycated hemoglobin levels in vivo

In this study we identify and classify high and low levels of glycated hemoglobin (HbA1c) in healthy volunteers (HV) and diabetic patients (DP). Overall, 86 subjects were evaluated. The Raman spectrum was measured in three anatomical regions of the body: index fingertip, right ear lobe, and forehead. The measurements were performed to compare the difference between the HV and DP (22 well controlled diabetic patients (WCDP) (HbA1c <6.5%), and 49 not controlled diabetic patients (NCDP) (HbA1c ≥6.5%)). Multivariable methods such as principal components analysis (PCA) combined with support vector machine (SVM) were used to develop effective diagnostic algorithms for classification among these groups. The forehead of HV versus WCDP showed the highest sensitivity (100%) and specificity (100%). Sensitivity (100%) and specificity (60%), were highest in the forehead of WCDP, versus NCDP. In HV versus NCDP, the fingertip had the highest sensitivity (100%) and specificity (80%). The efficacy of the diagnostic algorithm by receiver operating characteristic (ROC) curve was confirmed. Overall, our study demonstrated that the combination of Raman spectroscopy and PCA-SVM are feasible non-invasive diagnostic tool in diabetes to classify qualitatively high and low levels of HbA1c in vivo.