In vivo blood glucose quantification using Raman spectroscopy

Noninvasive Glucose Measurements Through Transcutaneous Raman Spectroscopy: A Review

BACKGROUND

People living with diabetes need constant glucose monitoring to avoid health complications. However, they do not monitor their glucose levels as often as recommended, probably because glucose measurement devices can be painful, costly, need testing strips or sensors, require lancing the finger or inserting a sensor with risk of infection, and can be inaccurate or have failures. Therefore, developing new alternatives for noninvasive glucose measurements that overcome these disadvantages is necessary, being Raman spectroscopy (RS) a solution.

OBJECTIVE

This review aims to provide an overview of the current glucose-monitoring technologies and the uses and advantages of RS to improve noninvasive transcutaneously glucose-monitoring devices.

RESULTS

The skin has been used to assess glucose levels noninvasively because it is an accessible tissue where glucose can be measured in the interstitial fluid (ISF) in the epidermis (especially in the stratum corneum). The most selected skin sites to apply RS for noninvasive glucose measurements were the nailfold, finger, and forearm because, in these sites, the penetration depth of the excitation light can reach the stratum corneum (10-20 µm) and the ISF. Studies found that RS is a good optical technique to measure glucose noninvasively by comparing glucose levels obtained by RS with those from invasive methods such as glucose meters with testing strips during an oral glucose tolerance test (OGTT).

CONCLUSIONS

New alternatives for noninvasive glucose measurements that overcome the disadvantages of current devices is necessary, and RS is a possible solution. However, more research is needed to evaluate the stability, accuracy, costs, and acceptance.

Label-free analysis of the β-hydroxybutyricacid drug on mitochondrial redox states repairment in type 2 diabetic mice by resonance raman scattering

BACKGROUND

Mitochondrial redox imbalance underlies the pathophysiology of type2 diabetes mellitus (T2DM), and is closely related to tissue damage and dysfunction. Studies have shown the beneficial effects of dietary strategies that elevate β-hydroxybutyrate (BHB) levels in alleviating T2DM. Nevertheless, the role of BHB has not been clearly elucidated.

METHODS

We performed a spectral study to visualize the preventive effects of BHB on blood and multiorgan mitochondrial redox imbalance in T2DM mice via using label-free resonance Raman spectroscopy (RRS), and further explored the impact of BHB therapy on the pathology of T2DM mice by histological and biochemical analyses.

FINDINGS

Our data revealed that RRS-based mitochondrial redox states assay enabled clear and reliable identification of the improvement of mitochondrial redox imbalance by BHB, evidenced by the reduction of Raman peak intensity at 750 cm-1, 1128 cm-1 and 1585 cm-1 in blood, tissue as well as purified mitochondria of db/db mice and the increase of tissue mitochondrial succinic dehydrogenase (SDH) staining after BHB treatment. Exogenous supplementation of BHB was also found to attenuate T2DM pathology related to mitochondrial redox states, involving organ injury, blood glucose control, insulin resistance and systemic inflammation.

INTERPRETATION

Our findings provide strong evidence for BHB as a potential therapeutic strategy targeting mitochondria for T2DM.

Subsecond lung cancer detection within a heterogeneous background of normal and benign tissue using single-point Raman spectroscopy

Significance

Lung cancer is the most frequently diagnosed cancer overall and the deadliest cancer in North America. Early diagnosis through current bronchoscopy techniques is limited by poor diagnostic yield and low specificity, especially for lesions located in peripheral pulmonary locations. Even with the emergence of robotic-assisted platforms, bronchoscopy diagnostic yields remain below 80%.

Aim

The aim of this study was to determine whether in situ single-point fingerprint (800 to 1700    cm - 1 ) Raman spectroscopy coupled with machine learning could detect lung cancer within an otherwise heterogenous background composed of normal tissue and tissue associated with benign conditions, including emphysema and bronchiolitis.

Approach

A Raman spectroscopy probe was used to measure the spectral fingerprint of normal, benign, and cancer lung tissue in 10 patients. Each interrogated specimen was characterized by histology to determine cancer type, i.e., small cell carcinoma or non-small cell carcinoma (adenocarcinoma and squamous cell carcinoma). Biomolecular information was extracted from the fingerprint spectra to identify biomolecular features that can be used for cancer detection.

Results

Supervised machine learning models were trained using leave-one-patient-out cross-validation, showing lung cancer could be detected with a sensitivity of 94% and a specificity of 80%.

Conclusions

This proof of concept demonstrates fingerprint Raman spectroscopy is a promising tool for the detection of lung cancer during diagnostic procedures and can capture biomolecular changes associated with the presence of cancer among a complex heterogeneous background within less than 1 s.

Noninvasive Glucose Sensing In Vivo

Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions.

Fundamentals and Applications of Raman-Based Techniques for the Design and Development of Active Biomedical Materials

Raman spectroscopy is an analytical method based on light-matter interactions that can interrogate the vibrational modes of matter and provide representative molecular fingerprints. Mediated by its label-free, non-invasive nature, and high molecular specificity, Raman-based techniques have become ubiquitous tools for in situ characterization of materials. This review comprehensively describes the theoretical and practical background of Raman spectroscopy and its advanced variants. The numerous facets of material characterization that Raman scattering can reveal, including biomolecular identification, solid-to-solid phase transitions, and spatial mapping of biomolecular species in bioactive materials, are highlighted. The review illustrates the potential of these techniques in the context of active biomedical material design and development by highlighting representative studies from the literature. These studies cover the use of Raman spectroscopy for the characterization of both natural and synthetic biomaterials, including engineered tissue constructs, biopolymer systems, ceramics, and nanoparticle formulations, among others. To increase the accessibility and adoption of these techniques, the present review also provides the reader with practical recommendations on the integration of Raman techniques into the experimental laboratory toolbox. Finally, perspectives on how recent developments in plasmon- and coherently-enhanced Raman spectroscopy can propel Raman from underutilized to critical for biomaterial development are provided.

Synthesis and Characterization of TiO2 Thick Films for Glucose Sensing

In this paper, we present the results of a non-enzymatic electrochemical glucose biosensor based on TiO₂. An anatase working electrode was synthesized using the spin coating technique with the polymeric precursor method and dispersed TiO₂ nanoparticles. Through scanning electron microscopy, it was observed that the electrode presented an irregular surface with clusters of nanoparticles. Electrochemical characterization indicated that the response was directly related to the morphology of the electrode. In the presence of glucose, the electrode exhibited adsorption behavior toward the molecules, enabling their recognition. The electrode was tested by employing PBS (phosphate buffer solutions) with varying pH values (from 4 to 9), demonstrating its electrochemical stability, even in the presence of glucose. Amperometric characterization was used to determine that the working region appeared from 0.2 mM to 2 mM, with a sensitivity of 4.46 μAcm^-2mM^-1 in PBS pH 7. The obtained results suggest that TiO₂-based electrodes could be used for the detection of glucose concentration in sweat (0.277-1 mM) and saliva (0.23-1.77 mM).

Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management?

Diabetes has no well-established cure; thus, its management is critical for avoiding severe health complications involving multiple organs. This requires frequent glycaemia monitoring, and the gold standards for this are fingerstick tests. During the last decades, several blood-withdrawal-free platforms have been being studied to replace this test and to improve significantly the quality of life of people with diabetes (PWD). Devices estimating glycaemia level targeting blood or biofluids such as tears, saliva, breath and sweat, are gaining attention; however, most are not reliable, user-friendly and/or cheap. Given the complexity of the topic and the rise of diabetes, a careful analysis is essential to track scientific and industrial progresses in developing diabetes management systems. Here, we summarize the emerging blood glucose level (BGL) measurement methods and report some examples of devices which have been under development in the last decades, discussing the reasons for them not reaching the market or not being really non-invasive and continuous. After discussing more in depth the history of Raman spectroscopy-based researches and devices for BGL measurements, we will examine if this technique could have the potential for the development of a user-friendly, miniaturized, non-invasive and continuous blood glucose-monitoring device, which can operate reliably, without inter-patient variability, over sustained periods.

Imaging with Raman photons: a novel use of mixed-mode spectroscopy

Surface enhanced Raman spectroscopy is today an established technique used for chemical fingerprinting. Here, we showcase an engineered hierarchical substrate, in which the plasmonically active regions, restricted to a micron scale, two dimensional hexagonal pattern are examined. Spatial variation of the enhanced Raman signal from any analyte, uniformly coating the substrate, consequently bears a high registry with the underlying pattern. This spatially contrasted enhancement allows optical imaging of the 2D pattern solely using the Raman scattered photons from the analyte. While the pattern brightness and contrast determine analyte identification and detection sensitivity, hyperspectral imaging can be exploited for increasing specificity. Proof of concept demonstration of the technique is carried out via the acquisition of Raman images with rhodamine and fluorescein dyes and then applied to detect glucose in 40 mM concentration. The large area optical imaging and the requirement of long-range uniformity in the detected patterns for positive analyte detection, is implemented using a machine learning based pattern recognition protocol which also increases the statistical confidence of detection. This simultaneous, large area signal detection sacrifices continuous spectral information at the cost of speed, reproducibility and minimising human error via automation of detection in the hyperspectral imaging technique presented here.

Functionalized Mesoporous Silica as Doxorubicin Carriers and Cytotoxicity Boosters

Mesoporous silica nanoparticles (MSNs) bearing methyl, thiol or glucose groups were synthesized, and their encapsulation and release behaviors for the anticancer drug Doxorubicin (Dox) were investigated in comparison with nonporous homologous materials. The chemical modification of thiol-functional silica with a double bond glucoside was completed for the first time, by green thiol-ene photoaddition. The MSNs were characterized in terms of structure (FT-IR, Raman), morphology (TEM), porosity (nitrogen sorption–desorption) and Zeta potential measurements. The physical interactions responsible for the Dox encapsulation were investigated by analytic methods and MD simulations, and were correlated with the high loading efficiency of MSNs with thiol and glucose groups. High release at pH 5 was observed in most cases, with thiol-MSN exhibiting 98.25% cumulative release in sustained profile. At pH 7.4, the glucose-MSN showed 75.4% cumulative release, while the methyl-MSN exhibited a sustained release trend. The in vitro cytotoxicity was evaluated on NDHF, MeWo and HeLa cell lines by CellTiter-Glo assay, revealing strong cytotoxic effects in all of the loaded silica at low equivalent Dox concentration and selectivity for cancer cells. Atypical applications of each MSN as intravaginal, topical or oral Dox administration route could be proposed.

Study of ergocalciferol and cholecalciferol (Vitamin D): Modeled optical properties and optical detection using absorption and Raman spectroscopy

The task of assembling and calculating spectrally significant lines of Vitamin D2 and D3 is related to a wider goal of establishing if it is possible to develop non-invasive optical sensors for these substances present at concentrations on the order of tens of nmol/L. Such a non-invasive in vivo sensor would be helpful for medical considerations, among others, related to multiple sclerosis prevention, reduced risk of mortality in D3-treated acute in-patients admitted with COVID 19, systemic infection, acute respiratory tract infections, including epidemic influenza, community-acquired pneumonia at concentrations <50nmol/L(<20ng/mL), dental health (90-100nmol/L) of 25(OH) D, general health and others. Currently, to determine the concentration of these substances, it is necessary to draw a sample from a vein in ambulatory settings and analyse the sample with the gold standard of mass spectroscopy or immunoassay. In this article Vitamin D optical properties are studied by density functional theory calculations, compared to reported data, and the new calculated and measured D2 and D3 optical absorption lines are presented, as well as the calculations compared with spectral measurements of optical transmission, FTIR ATR and Raman spectra.

Micro-Lensed Negative-Curvature Fibre Probe for Raman Spectroscopy

We developed a novel miniature micro-lensed fibre probe for Raman spectroscopy. The fibre probe consists of a single negative-curvature fibre (NCF) and a spliced, cleaved, micro-lensed fibre cap. Using a single NCF, we minimized the Raman background generated from the silica and maintained the diameter of the probe at less than 0.5 mm. In addition, the cap provided fibre closure by blocking the sample from entering the hollow parts of the fibre, enabling the use of the probe in in vivo applications. Moreover, the micro-lensed cap offered an improved collection efficiency (1.5-times increase) compared to a cleaved end-cap. The sensing capabilities of the micro-lensed probe were demonstrated by measuring different concentrations of glucose in aqueous solutions.

Online analysis of D-glucose and D-mannose aqueous mixtures using Raman spectroscopy: an in silico and experimental approach

Raman spectroscopy was applied to an aqueous solution containing D-mannose and D-glucose at a fixed dry matter content. The Raman measurement apparatus was adapted online at the industrial scale to monitor a bioprocess including an epimerization reaction. Online Raman spectroscopy and deconvolution techniques were successfully applied to monitor in real time the D-mannose and D-glucose concentrations using the Raman shifts at 960 cm-1 and 974 cm-1 respectively. The two anomeric forms, α and β of D-mannose in the pyranose conformation were quantified. In silico analysis of vibrational frequencies and Raman intensities of hydrated structure of D-mannose and D-glucose in the pyranose form for α and β anomers were carried out using a two-step procedure. First molecular dynamics was used to generate the theoretical carbohydrates' structures keeping the experimental dry matter content, then quantum mechanics was used to compute the Raman frequencies and intensities. Computed vibrational frequencies are in satisfactory agreement with the experimental spectra considering a hydration shell approach. Raman intensities are qualitatively in accordance with the experimental data. The interpretation of Raman frequencies and intensities led to acceptable results regarding the current possible structures of D-mannose and D-glucose in aqueous solution. Online Raman spectroscopy coupled with in silico approaches such as quantum mechanics and molecular dynamics methodology is proved to be a valuable tool to quantify the carbohydrates and stereoisomers content in complex aqueous mixtures. This methodology offers a new way to monitor any bioprocesses that encounter aqueous mixtures of D-glucose and D-mannose.

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

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

Microfluidic Raman Sensing Using a Single Ring Negative Curvature Hollow Core Fiber

A compact microfluidic Raman detection system based on a single-ring negative-curvature hollow-core fiber is presented. The system can be used for in-line qualitative and quantitative analysis of biochemicals. Both efficient light coupling and continuous liquid injection into the hollow-core fiber were achieved by creating a small gap between a solid-core fiber and the hollow-core fiber, which were fixed within a low-cost ceramic ferrule. A coupling efficiency of over 50% from free-space excitation laser to the hollow core fiber was obtained through a 350 μm-long solid-core fiber. For proof-of-concept demonstration of bioprocessing monitoring, a series of ethanol and glucose aqueous solutions at different concentrations were used. The limit of detection achieved for the ethanol solutions with our system was ~0.04 vol.% (0.32 g/L). Such an all-fiber microfluidic device is robust, provides Raman measurements with high repeatability and reusability, and is particularly suitable for the in-line monitoring of bioprocesses.

Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance

The present study comprises a comparison of the Mel Frequency Cepstral Coefficients (MFCC), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as feature extraction methods using ten different regression algorithms (AdaBoost, Bayesian Ridge, Decision Tree, Elastic Net, k-NN, Linear Regression, MLP, Random Forest, Ridge Regression and Support Vector Regression) to quantify the blood glucose concentration. A total of 122 participants—healthy and diagnosed with type 2 diabetes—were invited to be part of this study. The entire set of participants was divided into two partitions: a training subset of 72 participants, which was intended for model selection, and a validation subset comprising the remaining 50 participants, to test the selected model. A 3D-printed chamber for providing a light-controlled environment and a low-cost microcontroller unit were used to acquire optical measurements. The MFCC, PCA and ICA were calculated by an open-hardware computing platform. The glucose levels estimated by the system were compared to actual glucose concentrations measured by venipuncture in a laboratory test, using the mean absolute error, the mean absolute percentage error and the Clarke error grid for this purpose. The best results were obtained for MCCF with AdaBoost and Random Forest (MAE = 11.6 for both).

Non-invasive monitoring of red blood cells during cold storage using handheld Raman spectroscopy

BACKGROUND

The current best practices allow for the red blood cells (RBCs) to be stored for prolonged periods in blood banks worldwide. However, due to the individual-related variability in donated blood and RBCs continual degradation within transfusion bags, the quality of stored blood varies considerably. There is currently no method for assessing the blood product quality without compromising the sterility of the unit. This study demonstrates the feasibility of monitoring storage lesion of RBCs in situ while maintaining sterility using an optical approach.

STUDY DESIGN AND METHODS

A handheld spatially offset Raman spectroscopy (RS) device was employed to non-invasively monitor hemolysis and metabolic changes in 12 red cell concentrate (RCC) units within standard sealed transfusion bags over 7 weeks of cold storage. The donated blood was analyzed in parallel by biochemical (chemical analysis, spectrophotometry, hematology analysis) and RS measurements, which were then correlated through multisource correlation analysis.

RESULTS

Raman bands of lactate (857 cm^-1 ), glucose (787 cm^-1 ), and hemolysis (1003 cm^-1 ) were found to correlate strongly with bioanalytical data over the length of storage, with correlation values 0.98 (95% confidence interval [CI]: 0.86-1.00; p = .0001), 0.95 (95% CI: 0.71-0.99; p = .0008) and 0.97 (95% CI: 0.79-1.00; p = .0004) respectively.

DISCUSSION

This study demonstrates the potential of collecting information on the clinical quality of blood units without breaching the sterility using Raman technology. This could significantly benefit quality control of RCC units, patient safety and inventory management in blood banks and hospitals.

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.

On the estimation of sugars concentrations using Raman spectroscopy and artificial neural networks

In this paper, we present an analysis of the performance of Raman spectroscopy, combined with feed-forward neural networks (FFNN), for the estimation of concentration percentages of glucose, sucrose, and fructose in water solutions. Indeed, we analysed our method for the estimation of sucrose in three solid industrialized food products: donuts, cereal, and cookies. Concentrations were estimated in two ways: using a non-linear fitting system, and using a classifier. Our experiments showed that both the classifier and the fitting systems performed better than a Support Vector Machine (SVM), a Linear Discriminant Analysis (LDA), a Linear Regression (LR), and interval Partial Least Squares (iPLS). The best-case obtained by an FFNN for water solutions was 93.33% of classification and 3.51% of Root Mean Square Error in Prediction (RMSEP), compared with 82.22% obtained by a LDA. Our proposed method got an RMSEP of 1% for the best-case obtained with the food products.

Rapid Detection of Glucose on Nanostructured Gold Film Biosensor by Surface-Enhanced Raman Spectroscopy

In this report, we summarized our development of biosensors for Rhodamine 6G and in vitro glucose detection based on surface-enhanced Raman scattering technology. For the detection of both Rhodamine 6G and in vitro glucose, a nature-patterned substrate with gold films over nanostructures (NPS-AuFON) was used as the surface-enhanced Raman scattering sensor platform. The enhancement factor was calculated at 9 × 10⁷. In the processing of the substrate, cyclic voltammetry was used to form nano-gold particles under different conditions. The Rhodamine 6G and glucose detection were then achieved on this substrate. Furthermore, we combined the potentiostatic technique and electrochemical adsorption to best detect glucose in low concentrations. The glucose oxidation potential (100 mV) was used to capture glucose close to the surface of the NPS-AuFON. The quantitative detection of glucose in solution and in situ inspection were confirmed. Further, we determined that this surface modification technology can reach the goal of experiments set by the World Health Organization to judge whether or not a patient is a diabetic by detecting a glucose concentration of 11.1 mmol/L (mg/dL) at a minimum.

Non-Invasive Blood Glucose Monitoring Technology: A Review

In recent years, with the rise of global diabetes, a growing number of subjects are suffering from pain and infections caused by the invasive nature of mainstream commercial glucose meters. Non-invasive blood glucose monitoring technology has become an international research topic and a new method which could bring relief to a vast number of patients. This paper reviews the research progress and major challenges of non-invasive blood glucose detection technology in recent years, and divides it into three categories: optics, microwave and electrochemistry, based on the detection principle. The technology covers medical, materials, optics, electromagnetic wave, chemistry, biology, computational science and other related fields. The advantages and limitations of non-invasive and invasive technologies as well as electrochemistry and optics in non-invasives are compared horizontally in this paper. In addition, the current research achievements and limitations of non-invasive electrochemical glucose sensing systems in continuous monitoring, point-of-care and clinical settings are highlighted, so as to discuss the development tendency in future research. With the rapid development of wearable technology and transdermal biosensors, non-invasive blood glucose monitoring will become more efficient, affordable, robust, and more competitive on the market.

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.

Influence of Raman Spectrometer Collection Efficiency on Performance of Noninvasive Blood Glucose Detection for Device Miniaturization

Recently the world population with diabetes has increased significantly, and the market demand for noninvasive blood glucose monitoring has increased accordingly. Our previous study demonstrated the capability to detect glucose through the direct observation of glucose Raman fingerprint peaks from in vivo skin but using a benchtop device. From the perspective of commercialization, miniaturized devices are expected to make more impact on the market than bulky benchtop devices. In this study, as an effort for commercialization of noninvasive glucose sensing technology, we investigate the relationship between Raman spectrometer specification, especially collection efficiency, and glucose prediction performance. Raman spectra were synthesized at given spectrometer collection efficiencies in computer simulation, in which spectra are designed to contain glucose signal at specific concentrations. Then, we estimated glucose concentrations back using regression analysis and evaluated prediction performances. Finally, the relationship was analyzed between the collection efficiencies and glucose prediction performances. In order to mimic actual conditions with skin tissue, Monte-Carlo simulations were conducted to count the number of Raman photons escaping from the skin surface in a multi-layered skin model. As the collection efficiency decreased from 3.2 % to 0.2 %, the correlation coefficient between the actual and predicted glucose concentrations dropped from 0.91 to 0.35. The glucose Raman peaks at 1125 cm^-1 was identified as the most important wavelength for glucose sensing. This study may help identify optimal Raman spectrometer specifications for transcutaneous blood glucose sensing in miniaturized devices and commercialize noninvasive blood glucose sensors in Raman spectroscopy.

The FT-IR and Raman Spectroscopies as Tools for Biofilm Characterization Created by Cariogenic Streptococci

Fourier transform infrared (FT-IR) and Raman spectroscopy and mapping were applied to the analysis of biofilms produced by bacteria of the genus Streptococcus. Bacterial biofilm, also called dental plaque, is the main cause of periodontal disease and tooth decay. It consists of a complex microbial community embedded in an extracellular matrix composed of highly hydrated extracellular polymeric substances and is a combination of salivary and bacterial proteins, lipids, polysaccharides, nucleic acids, and inorganic ions. This study confirms the value of Raman and FT-IR spectroscopies in biology, medicine, and pharmacy as effective tools for bacterial product characterization.

Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone

Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy.

Statistical and spectral analysis of ECG signal towards achieving non-invasive blood glucose monitoring

Background

Globally, the cases of diabetes mellitus (diabetes) have increased in the past three decades, and it is recorded as one of the leading cause of death. This epidemic is a metabolic condition where the body cannot regulate blood glucose, thereby leading to abnormally high blood sugar. Genetic condition plays a significant role to determine a person susceptibility to the condition, a sedentary lifestyle and an unhealthy diet are behaviour that supports the current global epidemic. The complication that arises from diabetes includes loss of vision, peripheral neuropathy, cardiovascular complications and so on. Victims of this condition require constant monitoring of blood glucose which is done by the pricking of the finger. This procedure is painful, inconvenient and can lead to disease infection. Therefore, it is important to find a way to measure blood glucose non-invasively to minimize or eliminate the disadvantages encountered with the usual monitoring of blood glucose.

Method

In this paper, we performed two experiments on 16 participants while electrocardiogram (ECG) data was continuously captured. In the first experiment, participants are required to consume 75 g of anhydrous glucose solution (oral glucose tolerance test) and the second experiment, no glucose solution was taken. We explored statistical and spectral analysis on HRV, HR, R-H, P-H, PRQ, QRS, QT, QTC and ST segments derived from ECG signal to investigate which segments should be considered for the possibility of achieving non-invasive blood glucose monitoring. In the statistical analysis, we examined the pattern of the data with the boxplot technique to reveal the change in the statistical properties of the data. Power spectral density estimation was adopted for the spectral analysis to show the frequency distribution of the data.

Results

HRV segment obtained a statistical score of 81% for decreasing pattern and HR segment have the same statistical score for increasing pattern among the participants in the first quartile, median and mean properties. While ST segment has a statistical score of 81% for decreasing pattern in the third quartile, QT segment has 81% for increasing pattern for the median. From a total change score of 6, ST, QT, PRQ, P-H, HR and HRV obtained 4, 5, 4, 5 and 6 respectively. For spectral analysis, HRV and HR segment scored 81 and 75% respectively. ST, QT, PRQ have 75, 62 and 68% respectively.

Conclusions

The results obtained demonstrate that HR, HRV, PRQ, QT and ST segments under a normal, healthy condition are affected by glucose and should be considered for modelling a system to achieve the possibility of non-invasive blood glucose measurement with ECG.

Design of a Real-time Self-adjusting Calibration Algorithm to Improve the Accuracy of Continuous Blood Glucose Monitoring

The aim of this study is to establish a real-time self-adjusting calibration algorithm to compensate for signal drift and sensitivity attenuation of subcutaneous implantable glucose sensors. A real-time self-adjusting in vivo calibration method was designed based on the one-point calibration model. The current signal was compensated in real-time and the sensitivity was calibrated regularly. The least squares method was used to fit the initial parameters of the model, and then, the in vivo monitored current data was calibrated. Comparing with the mean absolute relative difference (MARD) of the blood glucose concentration by the traditional one-point calibration model (22.85 ± 5.76%), the MARD of the blood glucose concentration calibrated by the real-time self-adjusting in vivo calibration method was 6.28 ± 2.31%. The accuracy of the dynamic blood glucose monitoring was effectively improved. This calibration algorithm could compensate the signal drift in real time and correct sensitivity regularly to improve the accuracy of dynamic glucose monitoring, thus significantly enhancing diabetic self-management.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy’s multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.

An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi

Patients diagnosed with diabetes mellitus must monitor their blood glucose levels in order to control the glycaemia. Consequently, they must perform a capillary test at least three times per day and, besides that, a laboratory test once or twice per month. These standard methods pose difficulty for patients since they need to prick their finger in order to determine the glucose concentration, yielding discomfort and distress. In this paper, an Internet of Things (IoT)-based framework for non-invasive blood glucose monitoring is described. The system is based on Raspberry Pi Zero (RPi) energised with a power bank, using a visible laser beam and a Raspberry Pi Camera, all implemented in a glove. Data for the non-invasive monitoring is acquired by the RPi Zero taking a set of pictures of the user fingertip and computing their histograms. Generated data is processed by an artificial neural network (ANN) implemented on a Flask microservice using the Tensorflow libraries. In this paper, all measurements were performed in vivo and the obtained data was validated against laboratory blood tests by means of the mean absolute error (10.37%) and Clarke grid error (90.32% in zone A). Estimated glucose values can be harvested by an end device such as a smartphone for monitoring purposes.

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.

Non-invasive marker-independent high content analysis of a microphysiological human pancreas-on-a-chip model

The increasing prevalence of diabetes, its heterogeneity, and the limited number of treatment options drive the need for physiologically relevant assay platforms with human genetic background that have the potential to improve mechanistic understanding and e\xpedite diabetes-related research and treatment. In this study, we developed an endocrine pancreas-on-a-chip model based on a tailored microfluidic platform, which enables self-guided trapping of single human pseudo-islets. Continuous, low-shear perfusion provides a physiologically relevant microenvironment especially important for modeling and monitoring of the endocrine function as well as sufficient supply with nutrients and oxygen. Human pseudo-islets, generated from the conditionally immortalized EndoC-βH3 cell line, were successfully injected by hydrostatic pressure-driven flow without altered viability. To track insulin secretion kinetics in response to glucose stimulation in a time-resolved manner, dynamic sampling of the supernatant as well as non-invasive real-time monitoring using Raman microspectroscopy was established on-chip. Dynamic sampling indicated a biphasic glucose-stimulated insulin response. Raman microspectroscopy allowed to trace glucose responsiveness in situ and to visualize different molecular structures such as lipids, mitochondria and nuclei. In-depth spectral analyses demonstrated a glucose stimulation-dependent, increased mitochondrial activity, and a switch in lipid composition of insulin secreting vesicles, supporting the high performance of our pancreas-on-a-chip model.

Significant Reduction of Defect States and Surface Tailoring in ZnO Nanoparticles via Nano-Bio Interaction With Glucose for Bio-Applications

In this study, we have investigated the structural and optical properties of nanoconjugates (NJs) consisting of phase pure zinc oxide (ZnO) nanoparticles (NPs) with glucose biomolecules. All NJs were fabricated using a standard biochemical synthesis process. Structural, optical, vibrational, and biochemical interface properties of nano-bio composites are probed by different complementary characterization techniques. The XRD patterns of the NPs and NJs illustrate a highly phase pure ZnO structure. A visible green emission in the photoluminescence spectrum, mainly associated with the oxygen vacancies on the surface of ZnO nanostructure, is significantly reduced by the incorporation of glucose biomolecules. The strong binding interaction of glucose biomolecule on the surface of ZnO NPs results in the reduction in green-yellow-orange emission intensities. The interaction of glucose molecules modifies oxygen vacancies by capturing free electrons from the ZnO surface region. Significant changes in the peak intensity and relative peak position of some of the glucose and ZnO NPs in Raman spectra refer to the direct binding between these two nano- and bio-components. In the X-ray photoelectron spectroscopy, the binding energy of O 1s core level in NJs increases from 531 eV (O 1s core level position for ZnO) and the increment is more with higher initial glucose concentration in the solution during synthesis. This study serves as a promising platform for the development of new kinds of NJs and investigation of their interfacial properties which can take the frontier forward for integration and multifunctionality.

A Noninvasive Accurate Measurement of Blood Glucose Levels with Raman Spectroscopy of Blood in Microvessels

Raman spectra of human skin obtained by laser excitation have been used to non-invasively detect blood glucose. In previous reports, however, Raman spectra thus obtained were mainly derived from the epidermis and interstitial fluid as a result of the shallow penetration depth of lasers in skin. The physiological process by which glucose in microvessels penetrates into the interstitial fluid introduces a time delay, which inevitably introduces errors in transcutaneous measurements of blood glucose. We focused the laser directly on the microvessels in the superficial layer of the human nailfold, and acquired Raman spectra with multiple characteristic peaks of blood, which indicated that the spectra obtained predominantly originated from blood. Incorporating a multivariate approach combining principal component analysis (PCA) and back propagation artificial neural network (BP-ANN), we performed noninvasive blood glucose measurements on 12 randomly selected volunteers, respectively. The mean prediction performance of the 12 volunteers was obtained as an RMSEP of 0.45 mmol/L and R² of 0.95. It was no time lag between the predicted blood glucose and the actual blood glucose in the oral glucose tolerance test (OGTT). We also applied the procedure to data from all 12 volunteers regarded as one set, and the total predicted performance was obtained with an RMSEP of 0.27 mmol/L and an R² of 0.98, which is better than that of the individual model for each volunteer. This suggested that anatomical differences between volunteer fingernails do not reduce the prediction accuracy and 100% of the predicted glucose concentrations fall within Region A and B of the Clarke error grid, allowing acceptable predictions in a clinically relevant range. The Raman spectroscopy detection of blood glucose from microvessels is of great significance of non-invasive blood glucose detection of Raman spectroscopy. This innovative method may also facilitate non-invasive detection of other blood components.

Study on the pathological and biomedical characteristics of spinal cord injury by confocal Raman microspectral imaging

Confocal Raman microspectral imaging (CRMI) in combination with multivariate analysis was used to study pathological progression after spinal cord injury (SCI). By establishing moderate contusion in rat models, ex vivo longitudinal spinal cord tissue sections were prepared for microspectroscopic analysis. Comparative studies were then performed to determine the pathological distinctions among before injury (BI), one day post-injury (1 DPI), seven days post-injury (7 DPI), and 14 days post-injury (14 DPI) groups. Multivariate analysis algorithms, including K-mean cluster analysis (KCA) and principal component analysis (PCA), were conducted to highlight biochemical and structural variations after tissue damage. It is confirmed that typical spectral features and profiles can illustrate some fundamental and significant pathological processes post-injury, such as neuron apoptosis, hemorrhage, demyelination, and chondroitin sulfate proteoglycans (CSPGs) upregulation. Further, by establishing spectra-structure correlations, the reconstructed spectral images revealed some minute and important morphological characteristics following tissue injury, such as glial scar formation surrounding the cavity structure. The observed spectral phenomena also provide a detailed view on relevant pathobiological factors, which are involved in the spread of secondary damage after traumatic spinal cord injury. Our findings not only provide a spectral perspective to the well-known cellular mechanisms underlying SCI, but further provide a sound basis for developing real-time Raman methodologies to evaluate the prognostic factors and therapeutic results of SCI.

Current Advancements in Transdermal Biosensing and Targeted Drug Delivery

In this manuscript, recent advancements in the area of minimally-invasive transdermal biosensing and drug delivery are reviewed. The administration of therapeutic entities through the skin is complicated by the stratum corneum layer, which serves as a barrier to entry and retards bioavailability. A variety of strategies have been adopted for the enhancement of transdermal permeation for drug delivery and biosensing of various substances. Physical techniques such as iontophoresis, reverse iontophoresis, electroporation, and microneedles offer (a) electrical amplification for transdermal sensing of biomolecules and (b) transport of amphiphilic drug molecules to the targeted site in a minimally invasive manner. Iontophoretic delivery involves the application of low currents to the skin as well as the migration of polarized and neutral molecules across it. Transdermal biosensing via microneedles has emerged as a novel approach to replace hypodermic needles. In addition, microneedles have facilitated minimally invasive detection of analytes in body fluids. This review considers recent innovations in the structure and performance of transdermal systems.

Non-Invasive Heart State Monitoring an Article on Latest PPG Processing

Health Monitoring has become one of the most important task of this century with a change in population demography to build a smart healthcare system to give proper treatment to the correct patient with reduced cost, more consistently for better living. Heart & it's related parameters are very important for good health condition. Statistics from Centers for Disease Control and Prevention, in 2008, around 616K people died of heart disease and 25% cause of total death and in 2010 the percentage grew up to 31%. High blood pressure, high cholesterol, diabetes, smoking, overweight are some of the real cause of heart disease. To determine heart state, ECG is a proven and well accepted system. But, the device is expensive and requires training. ECG sensor measures the bio-potential generated by the electrical signals that is responsible to control the expansion and contraction of heart chambers. In this article, we have focused literature review on Non-Invasive cardiovascular monitoring researches undertaken so far to provide new possibilities and research trends so that we can monitor our health better and take precautions earlier with the use and advancement of Computer Science & Technology. Here we have primarily focused on PPG signal and its application to measure important blood parameters like Glucose, HB, SP02 that indirectly or directly can provide us a status of our health when required. Recent report suggests that PPG is very useful for measuring heart rates, arterial age (with PPG derivatives), blood pressure, oxygen saturation, emotion detection, respiratory rate etc. Accurate measurement of PPG can open up new possibilities in non-invasive computer aided cardiac research for smart care-giving.

Device and Method for Noninvasive Glucose Assessment

BACKGROUND

Intensive monitoring of blood glucose levels is crucial in diabetes management. This article presents a new device, the TensorTip Combo Glucometer (CoG), developed by Cnoga Medical Ltd, which enables to predict capillary tissue glucose concentration noninvasively.

METHODS

Noninvasive glucose readings usually provide irregular or disordered mathematical manifold over the measurement space. To establish a transfer function, which correctly correlates the noninvasive raw data and the actual invasive glucose level, we suggest a mathematical concept that employs a personal calibration procedure to associate glucose pattern and multiple optical signals derived from tissue response to light emission in the range of visible to IR. The traversed light is detected by a color image sensor to predict the tissue glucose concentration at the fingertip. This article presents the mathematical concept underlying the technology and the requirements for device operation.

RESULTS

The device was clinically evaluated and compared to standard invasive blood glucose monitoring devices in few medical centers and by home users. Based on consensus error grid analysis, more than 98% of the measurements of each study were in zones A (more than 81%) and B (more than 11%). Postmarketing evaluations showed high correlations comparing the CoG to other invasive reference devices.

CONCLUSIONS

The CoG device employs a unique mathematical approach to predict glucose concentrations based on multiple optical signals. The first clinical results indicate that the device may show appropriate agreement with reference methods to be used for pain-free glucose assessment in daily routine.

Amyloid β and tau are involved in sleep disorder in Alzheimer's disease by orexin A and adenosine A(1) receptor

Sleep disorder is confirmed as a core component of Alzheimer's disease (AD), while the accumulation of amyloid β (Aβ) in brain tissue is an important pathological feature of AD. However, how Aβ affects AD‑associated sleep disorder is not yet well understood. In the present study, experiments on animal and cell models were performed to detect the association between sleep disorder and Aβ. It was observed that Aβ25‑35 administration significantly decreased non‑rapid eye movement sleep, while it increased wakefulness in mice. In addition, reverse transcription‑quantitative polymerase chain reaction and western blot analysis revealed that the expression levels of tau, p‑tau, orexin A and orexin neurons express adenosine A1 receptor (A1R) were markedly upregulated in the brain tissue of AD mice compared with that in samples obtained from control mice. Furthermore, the in vitro study revealed that the expression levels of tau, p‑tau, orexin A and adenosine A1R were also significantly increased in human neuroblastoma SH‑SY5Y cells treated with Aβ25‑35 as compared with the control cells. In addition, the tau inhibitor TRx 0237 significantly reversed the promoting effects of Aβ25‑35 on tau, p‑tau, orexin A and adenosine A1R expression levels, and adenosine A1R or orexin A knockdown also inhibited tau and p‑tau expression levels mediated by Aβ25‑35 in AD. These results indicate that Aβ and tau may be considered as novel biomarkers of sleep disorder in AD pathology, and that they function by regulating the expression levels of orexin A and adenosine A1R.

Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing

Optical monitoring of blood glucose levels for non-invasive diagnosis is a growing area of research. Recent efforts in this direction have been inclined towards reducing the requirement of calibration framework. Here, we are presenting a systematic investigation on the influence of variation in the ratio of calibration and validation points on the prospective predictive accuracy of spectral models. A fiber-optic probe coupled Raman system has been employed for transcutaneous measurements. Limit of agreement analysis between serum and partial least square regression predicted spectroscopic glucose values has been performed for accurate comparison. Findings are suggestive of strong predictive accuracy of spectroscopic models without requiring substantive calibration measurements. Graphical abstract.

Exploring the margins of SERS in practical domain: An emerging diagnostic modality for modern biomedical applications

Excellent multiplexing capability, molecular specificity, high sensitivity and the potential of resolving complex molecular level biological compositions augmented the diagnostic modality of surface-enhanced Raman scattering (SERS) in biology and medicine. While maintaining all the merits of classical Raman spectroscopy, SERS provides a more sensitive and selective detection and quantification platform. Non-invasive, chemically specific and spatially resolved analysis facilitates the exploration of SERS-based nano probes in diagnostic and theranostic applications with improved clinical outcomes compared to the currently available so called state-of-art technologies. Adequate knowledge on the mechanism and properties of SERS based nano probes are inevitable in utilizing the full potential of this modality for biomedical applications. The safety and efficiency of metal nanoparticles and Raman reporters have to be critically evaluated for the successful translation of SERS in to clinics. In this context, the present review attempts to give a comprehensive overview about the selected medical, biomedical and allied applications of SERS while highlighting recent and relevant outcomes ranging from simple detection platforms to complicated clinical applications.

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.

Noninvasive and fast measurement of blood glucose in vivo by near infrared (NIR) spectroscopy

This research was to develop a method for noninvasive and fast blood glucose assay in vivo. Near-infrared (NIR) spectroscopy, a more promising technique compared to other methods, was investigated in rats with diabetes and normal rats. Calibration models are generated by two different multivariate strategies: partial least squares (PLS) as linear regression method and artificial neural networks (ANN) as non-linear regression method. The PLS model was optimized individually by considering spectral range, spectral pretreatment methods and number of model factors, while the ANN model was studied individually by selecting spectral pretreatment methods, parameters of network topology, number of hidden neurons, and times of epoch. The results of the validation showed the two models were robust, accurate and repeatable. Compared to the ANN model, the performance of the PLS model was much better, with lower root mean square error of validation (RMSEP) of 0.419 and higher correlation coefficients (R) of 96.22%.

Imaging mass spectrometry for metabolites: technical progress, multimodal imaging, and biological interactions

Imaging mass spectrometry (IMS) allows the study of the spatial distribution of small molecules in biological samples. IMS is able to identify and quantify chemicals in situ from whole tissue sections to single cells. Both vacuum mass spectrometry (MS) and ambient MS systems have advanced considerably over the last decade; however, some limitations are still hard to surmount. Sample pretreatment, matrix or solvent choices, and instrument improvement are the key factors that determine the successful application of IMS to different samples and analytes. IMS with innovative MS analyzers, powerful MS spectrum databases, and analysis tools can efficiently dereplicate, identify, and quantify natural products. Moreover, multimodal imaging systems and multiple MS-based systems provide additional structural, chemical, and morphological information and are applied as complementary tools to explore new fields. IMS has been applied to reveal interactions between living organisms at molecular level. Recently, IMS has helped solve many previously unidentifiable relations between bacteria, fungi, plants, animals, and insects. Other significant interactions on the chemical level can also be resolved using expanding IMS techniques. WIREs Syst Biol Med 2017, 9:e1387. doi: 10.1002/wsbm.1387 For further resources related to this article, please visit the WIREs website.

Raman Spectroscopy of Blood and Blood Components

Blood is a bodily fluid that is vital for a number of life functions in animals. To a first approximation, blood is a mildly alkaline aqueous fluid (plasma) in which a large number of free-floating red cells (erythrocytes), white cells (leucocytes), and platelets are suspended. The primary function of blood is to transport oxygen from the lungs to all the cells of the body and move carbon dioxide in the return direction after it is produced by the cells' metabolism. Blood also carries nutrients to the cells and brings waste products to the liver and kidneys. Measured levels of oxygen, nutrients, waste, and electrolytes in blood are often used for clinical assessment of human health. Raman spectroscopy is a non-destructive analytical technique that uses the inelastic scattering of light to provide information on chemical composition, and hence has a potential role in this clinical assessment process. Raman spectroscopic probing of blood components and of whole blood has been on-going for more than four decades and has proven useful in applications ranging from the understanding of hemoglobin oxygenation, to the discrimination of cancerous cells from healthy lymphocytes, and the forensic investigation of crime scenes. In this paper, we review the literature in the field, collate the published Raman spectroscopy studies of erythrocytes, leucocytes, platelets, plasma, and whole blood, and attempt to draw general conclusions on the state of the field.