Non-invasive monitoring of blood glucose using optical methods for skin spectroscopy-opportunities and recent advances

In vivo Raman spectroscopy for non-invasive transcutaneous glucose monitoring on animal models and human subjects

Non-invasive glucose monitoring represents a significant advancement in diabetes management and treatment as non-painful alternatives than finger-sticks tests. After developing an integrated Raman spectral system with a 785 nm laser, this study systematically explores the application of in vivo Raman spectroscopy for quantitative, noninvasive glucose monitoring. In addition to observing characteristic glucose spectral information from a mouse model, a strong spectral correlation was also recognized with the blood glucose concentration. The glucose fingerprint information detected from the nailfolds of 30 human volunteers exhibited concentration dependent changes, especially when the intraspectrum intensity ratio was calculated between 1125 cm-1 and 1445 cm-1 to monitor normalized differences in the glucose Raman band. Furthermore, by accounting for all intersubject variations observed in the acquired spectral features, a particle swarm optimization-backpropagation artificial neural network (PSO-BP-ANN) model was proposed for linking measured Raman information with actual glucose concentrations quantitatively. Following model training and testing, the prediction accuracy of the PSO-BP-ANN model was evaluated using 12 spectra acquired from an additional three volunteers. Statistical evaluations indicated that the proposed methodology may have a good application potential for in vivo transcutaneous spectral glucose monitoring.

Photoacoustic Resonators for Non-Invasive Blood Glucose Detection Through Photoacoustic Spectroscopy: A Systematic Review

Diabetes mellitus is a prevalent disease with a rapidly increasing incidence projected worldwide, affecting both industrialized and developing regions. Effective diabetes management requires precise therapeutic strategies, primarily through self-monitoring of blood glucose levels to achieve tight glycemic control, thereby mitigating the risk of severe complications. In recent years, there have been significant advancements in non-invasive techniques for measuring blood glucose using photoacoustic spectroscopy (PAS), as it shows great promise for the detection of glucose using the infrared region (e.g., MIR and NIR) of light. A critical aspect of this method is the detection of the photoacoustic signal generated from blood glucose, which needs to be amplified through a photoacoustic resonator (PAR). In this work, an overview of various types of PARs used for non-invasive glucose sensing is reviewed, highlighting their operating principle, design requirements, limitations, and potential improvements needed to enhance the analysis of photoacoustic signals. The motivation behind this review is to identify and discuss main parameters crucial to the efficient design of PARs used in non-invasive glucose detection, which will be helpful for furthering the basic understanding of this technology and achieving the highly sensitive PAR required for non-invasive glucose monitoring.

High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm

Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human blood using Raman spectroscopy. This paper researches a novel integrated machine learning algorithm called Bagging-ABC-ELM. The optimal input weights and biases of extreme learning machine (ELM) model are obtained by artificial bee colony (ABC) algorithm. The bagging algorithm is used to obtain a better the stability of the model and higher performance than ELM algorithm. The results show that the mean value of coefficient of determination is 0.9928, and root mean square error is 0.1928. Compared to other regression models, the Bagging-ABC-ELM model exhibited superior prediction accuracy, robustness, and generalization capability. The Bagging-ABC-ELM model presents a promising alternative for analyzing blood glucose levels in clinical and research settings.

Computational Modeling as a Tool to Drive the Development of a Novel, Chemical Device for Monitoring the Injured Brain and Body

Real-time measurement of dynamic changes, occurring in the brain and other parts of the body, is useful for the detection and tracked progression of disease and injury. Chemical monitoring of such phenomena exists but is not commonplace, due to the penetrative nature of devices, the lack of continuous measurement, and the inflammatory responses that require pharmacological treatment to alleviate. Soft, flexible devices that more closely match the moduli and shape of monitored tissue and allow for surface microdialysis provide a viable alternative. Here, we show that computational modeling can be used to aid the development of such devices and highlight the considerations when developing a chemical monitoring probe in this way. These models pave the way for the development of a new class of chemical monitoring devices for monitoring neurotrauma, organs, and skin.

Micro interstitial fluid extraction and detection device integrated with the optimal extraction conditions for noninvasive glucose monitoring

Interstitial fluid glucose sensors have promising prospects in noninvasive glucose monitoring. However, the commonly used method of extracting interstitial fluid, reverse iontophoresis (RI), still remains to be optimized to solve problems such as insufficient extraction flux and skin irritation. To find the optimal RI conditions, in this study we explored the effects of multiple factors such as current frequency, duration, duty cycle and their interactions on extraction with the design of experiments (DOE) method. A multifunctional extraction and detection device was designed to control extraction conditions and measure the surface water content of the extraction electrode in situ and real time. A micro glucose monitoring device (MicroTED) combined with a cheap and flexible paper-based electrode was developed under the determined optimal extraction conditions. In on-body continuous glucose monitoring tests carried out to verify the performance of the device, the optimized conditions can facilitate stable extraction of up to 1.0 mg without any skin discomfort. The mean Pearson correlation coefficient between the measurement results of MicroTED and commercial glucometer is above 0.9. In the Clarke error grid analysis, all data points fell within Clarke error grid areas A and B, demonstrating the feasibility of further clinical application of the device.

Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection

The prevalence of diabetes is rapidly increasing worldwide and can lead to a range of severe health complications that have the potential to be life-threatening. Patients need to monitor and control blood glucose levels as it has no cure. The development of non-invasive techniques for the measurement of blood glucose based on photoacoustic spectroscopy (PAS) has advanced tremendously in the last couple of years. Among them, PAS in the mid-infrared (MIR) region shows great promise as it shows the distinct fingerprint region for glucose. However, two problems are generally encountered when it is applied to monitor real samples for in vivo measurements in this MIR spectral range: (i) low penetration depth of MIR light into the human skin, and (ii) the effect of other interfering components in blood, which affects the selectivity of the detection system. This review paper systematically describes the basics of PAS in the MIR region, along with recent developments, technical challenges, and data analysis strategies, and proposes improvements for the detection sensitivity of glucose concentration in human bodies. It also highlights the recent trends of incorporating machine learning (ML) to enhance the detection sensitivity of the overall system. With further optimization of the experimental setup and incorporation of ML, this PAS in the MIR spectral region could be a viable solution for the non-invasive measurement of blood glucose in the near future.

Estimation of the Differential Pathlength Factor for Human Skin Using Monte Carlo Simulations

Near-infrared technology is an emerging non-invasive technique utilized for various medical applications. Recently, there have been many attempts to utilize NIR technology for the continues monitoring of blood glucose levels through the skin. Different approaches and designs have been proposed for non-invasive blood glucose measurements. Light photons penetrating the skin can undergo multiple scattering events, and the actual optical pathlength becomes larger than the source-to-detector separation (optode spacing) in the reflection-mode configuration. Thus, the differential pathlength factor (DPF) must be incorporated into the modified Beer-Lambert law. The accurate estimation of the DPF values will lead to an accurate quantification of the physiological variations within the tissue. In this work, the aim was to systematically estimate the DPF for human skin for a range of source-to-detector separations and wavelengths. The Monte Carlo (MC) method was utilized to mimic the different layers of human skin with different optical properties and blood and water volume fractions. This work could help improve the accuracy of the near-infrared technique in the measurement of physiological variations within skin tissue.

In Silico Investigation of SNR and Dermis Sensitivity for Optimum Dual-Channel Near-Infrared Glucose Sensor Designs for Different Skin Colors

Diabetes is a serious health condition that requires patients to regularly monitor their blood glucose level, making the development of practical, compact, and non-invasive techniques essential. Optical glucose sensors-and, specifically, NIR sensors-have the advantages of being non-invasive, compact, inexpensive, and user-friendly devices. However, these sensors have low accuracy and are yet to be adopted by healthcare providers. In our previous work, we introduced a non-invasive dual-channel technique for NIR sensors, in which a long channel is utilized to measure the glucose level in the inner skin (dermis) layer, while a short channel is used to measure the noise signal of the superficial skin (epidermis) layer. In this work, we investigated the use of dual-NIR channels for patients with different skin colors (i.e., having different melanin concentrations). We also adopted a Monte Carlo simulation model that takes into consideration the differences between different skin layers, in terms of blood content, water content, melanin concentration in the epidermis layer, and skin optical proprieties. On the basis of the signal-to-noise ratio, as well as the sensitivities of both the epidermis and dermis layers, we suggest the selection of wavelengths and source-to-detector separation for optimal NIR channels under different skin melanin concentrations. This work facilitates the improved design of a compact and non-invasive NIR glucose sensor that can be utilized by patients with different skin colors.

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.

Enhanced terahertz sensitivity for glucose detection with a hydrogel platform embedded with Au nanoparticles

We presented a strategy for enhancing the sensitivity of terahertz glucose sensing with a hydrogel platform pre-embedded with Au nanoparticles. Physiological-level glucose solutions ranging from 0 to 0.8 mg/mL were measured and the extracted absorption coefficients can be clearly distinguished compared to traditional terahertz time domain spectroscopy performed directly on aqueous solutions. Further, Isotherm models were applied to successfully describe the relationship between the absorption coefficient and the glucose concentration (R²= 0.9977). Finally, the origin of the sensitivity enhancement was investigated and verified to be the pH change induced by the catalysis of Au nanoparticles to glucose oxidation.

Optimized multilayer structure for sensitive THz characterization of thin-film glucose solutions

Terahertz time-domain spectroscopy (THz-TDS) has shown promise in biomedical sample characterization and high characterization sensitivity is in demand due to the thin-film (TF) feature of the sample. This paper proposes an optimized multilayer structure for sensitive characterization of TF aqueous solutions in reflection THz-TDS. Theoretical simulations are conducted for structural optimization and the 75 µm window-sample-mirror structure displays the best sensitivity compared to other sandwich structures and traditional THz measurement geometries. 0-20% TF glucose solutions are then measured; and a spectral peak introduced by the proposed structure is observed to result in the high sensitivity. Our work provides a new way of customizing multilayer structure for THz thin-film characterization.

Performance improvement in a supercontinuum fiber-coupled system for near infrared absorption spectroscopy

Accurate, in-field-compatible, sensing based on near infrared spectroscopy (NIRS) requires development of instrumentation with low noise and long-term stability. Here, we present a fully fiber-optic spectroscopy setup using a supercontinuum source in the long-pulse regime (2 ns) and a balanced detector scheme to demonstrate high-accuracy NIRS-based sensing. The noise sources of the system are studied theoretically and experimentally. The relative intensity noise was reduced from typical values up to 6% to less than 0.1% by deploying a balanced detector and averaging. At well-balanced wavelengths, the system without transmission cells achieved a signal to noise ratio (SNR) above 70 dB, approaching the shot noise limit. With transmission cells and long-term measurements, the overall SNR was 55 dB. Glucose in physiological concentrations was measured as a model system, yielding a root mean square error of 4.8 mM, approaching the needed accuracy for physiological glucose monitoring.

A Single Wavelength Mid-Infrared Photoacoustic Spectroscopy for Noninvasive Glucose Detection Using Machine Learning

According to the International Diabetes Federation, 530 million people worldwide have diabetes, with more than 6.7 million reported deaths in 2021. Monitoring blood glucose levels is essential for individuals with diabetes, and developing noninvasive monitors has been a long-standing aspiration in diabetes management. The ideal method for monitoring diabetes is to obtain the glucose concentration level with a fast, accurate, and pain-free measurement that does not require blood drawing or a surgical operation. Multiple noninvasive glucose detection techniques have been developed, including bio-impedance spectroscopy, electromagnetic sensing, and metabolic heat conformation. Nevertheless, reliability and consistency challenges were reported for these methods due to ambient temperature and environmental condition sensitivity. Among all the noninvasive glucose detection techniques, optical spectroscopy has rapidly advanced. A photoacoustic system has been developed using a single wavelength quantum cascade laser, lasing at a glucose fingerprint of 1080 cm-1 for noninvasive glucose monitoring. The system has been examined using artificial skin phantoms, covering the normal and hyperglycemia blood glucose ranges. The detection sensitivity of the system has been improved to ±25 mg/dL using a single wavelength for the entire range of blood glucose. Machine learning has been employed to detect glucose levels using photoacoustic spectroscopy in skin samples. Ensemble machine learning models have been developed to measure glucose concentration using classification techniques. The model has achieved a 90.4% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke's error grid analysis. This finding fulfills the US Food and Drug Administration requirements for glucose monitors.

Skin Patchable Sensor Surveillance for Continuous Glucose Monitoring

Diabetes mellitus is a physiological and metabolic disorder affecting millions of people worldwide, associated with global morbidity, mortality, and financial expenses. Long-term complications can be avoided by frequent, continuous self-monitoring of blood glucose. Therefore, this review summarizes the current state-of-art glycemic control regimes involving measurement approaches and basic concepts. Following an introduction to the significance of continuous glucose sensing, we have tracked the evolution of glucose monitoring devices from minimally invasive to non-invasive methods to present an overview of the spectrum of continuous glucose monitoring (CGM) technologies. The conveniences, accuracy, and cost-effectiveness of the real-time CGM systems (rt-CGMs) are the factors considered for discussion. Transdermal biosensing and drug delivery routes have recently emerged as an innovative approach to substitute hypodermal needles. This work reviews skin-patchable glucose monitoring sensors for the first time, providing specifics of all the major findings in the past 6 years. Skin patch sensors and their progressive form, i.e., microneedle (MN) array sensory and delivery systems, are elaborated, covering self-powered, enzymatic, and non-enzymatic devices. The critical aspects reviewed are material design and assembly techniques focusing on flexibility, sensitivity, selectivity, biocompatibility, and user-end comfort. The review highlights the advantages of patchable MNs' multi-sensor technology designed to maintain precise blood glucose levels and administer diabetes drugs or insulin through a "sense and act" feedback loop. Subsequently, the limitations and potential challenges encountered from the MN array as rt-CGMs are listed. Furthermore, the current statuses of working prototype glucose-responsive "closed-loop" insulin delivery systems are discussed. Finally, the expected future developments and outlooks in clinical applications are discussed.

Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring

The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the amount of sugar in a small sample of blood, usually drawn from pricking the fingertip, and placed on a disposable test strip. Therefore, there is a need for non-invasive continuous glucose monitoring, which is possible using a sweat sensor-based approach. As sweat sensors have garnered much interest in recent years, this study attempts to summarize recent developments in non-invasive continuous glucose monitoring using sweat sensors based on different approaches with an emphasis on the devices that can potentially be integrated into a wearable platform. Numerous research entities have been developing wearable sensors for continuous blood glucose monitoring, however, there are no commercially viable, non-invasive glucose monitors on the market at the moment. This review article provides the state-of-the-art in sweat glucose monitoring, particularly keeping in sight the prospect of its commercialization. The challenges relating to sweat collection, sweat sample degradation, person to person sweat amount variation, various detection methods, and their glucose detection sensitivity, and also the commercial viability are thoroughly covered.

Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies

BACKGROUND

Conventional home blood glucose measurements require a sample of blood that is obtained by puncturing the skin at the fingertip. To avoid the pain associated with this procedure, there is high demand for medical products that allow glucose monitoring without blood sampling. In this review article, all such products are presented.

METHODS

In order to identify such products, four different sources were used: (1) PubMed, (2) Google Patents, (3) Diabetes Technology Meeting Startup Showcase participants, and (4) experts in the field of glucose monitoring. The information obtained were filtered by using two inclusion criteria: (1) regulatory clearance, and/or (2) significant coverage in Google News starting in the year 2016, unless the article indicated that the product had been discontinued. The identified bloodless monitoring products were classified into three categories: (1) noninvasive optical, (2) noninvasive fluid sampling, and (3) minimally invasive devices.

RESULTS

In total, 28 noninvasive optical, 6 noninvasive fluid sampling, and 31 minimally invasive glucose monitoring products were identified. Subsequently, these products were characterized according to their regulatory, technological, and consumer features. Products with regulatory clearance are described in greater detail according to their advantages and disadvantages, and with design images.

CONCLUSIONS

Based on favorable technological features, consumer features, and other advantages, several bloodless products are commercially available and promise to enhance diabetes management. Paths for future products are discussed with an emphasis on understanding existing barriers related to both technical and non-technical issues.

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.

Monte Carlo Characterization of Short-Wave Infrared Optical Wavelengths for Biosensing Applications

Short-wave infrared (SWIR) spectroscopy has shown great promise in probing the composition of biological tissues. Currently there exists an enormous drive amongst researchers to design and develop SWIR-based optical sensors that can predict the concentration of various biomarkers non-invasively. However, there is limited knowledge regarding the interaction of SWIR light with vascular tissue, especially in terms of parameters like the optimal source-detector separation, light penetration depth, optical pathlength, etc., all of which are essential components in designing optical sensors. With the aim to determine these parameters, Monte Carlo simulations were carried out to examine the interaction of SWIR light with vascular skin. SWIR photons were found to penetrated only 1.3 mm into the hypodermal fat layer. The highest optical pathlength and penetration depths were seen at 1mm source-detector separation, and the lowest being 0.7mm. Although the optical pathlength varied significantly with increasing source-detector separation at SWIR wavelengths, penetration depth remained constant. This may explain why collecting optical spectra from depth of tissue at SWIR wavelengths is more challenging than collecting optical spectra from near-infrared wavelengths, where both the optical pathlength and penetration depth change rapidly with source-detector separation.

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Perioperative challenges in management of diabetic patients undergoing non-cardiac surgery

Prediabetes and diabetes are important disease processes which have several perioperative implications. About one third of the United States population is considered to have prediabetes. The prevalence in surgical patients is even higher. This is due to the associated micro and macrovascular complications of diabetes that result in the need for subsequent surgical procedures. A careful preoperative evaluation of diabetic patients and patients at risk for prediabetes is essential to reduce perioperative mortality and morbidity. This preoperative evaluation involves an optimization of preoperative comorbidities. It also includes optimization of antidiabetic medication regimens, as the avoidance of unintentional hypoglycemic and hyperglycemic episodes during the perioperative period is crucial. The focus of the perioperative management is to ensure euglycemia and thus improve postoperative outcomes. Therefore, prolonged preoperative fasting should be avoided and close monitoring of blood glucose should be initiated and continued throughout surgery. This can be accomplished with either analysis in blood gas samples, venous phlebotomy or point-of-care testing. Although capillary and arterial whole blood glucose do not meet standard guidelines for glucose testing, they can still be used to guide insulin dosing in the operating room. Intraoperative glycemic control goals may vary slightly in different protocols but overall the guidelines suggest a glucose range in the operating room should be between 140 mg/dL to 180 mg/dL. When hyperglycemia is detected in the operating room, blood glucose management may be initiated with subcutaneous rapid-acting insulin, with intravenous infusion or boluses of regular insulin. Fluid and electrolyte management are other perioperative challenges. Notably diabetic ketoacidosis and hyperglycemic hyperosmolar nonketotic state are the two most serious acute metabolic complications of diabetes that must be recognized early and treated.

Diabetes Technology Meeting 2020

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

[Treatment of diabetes mellitus in perioperative medicine-an update]

Patients with diabetes who undergo a surgical intervention have an increased risk of metabolic derailment, anesthesiological complications, postoperative infections and cardiovascular events. The treatment of diabetes mellitus is subject to a continuous further development due to pharmaceutical and technical innovations. This article presents the implications of the current concepts of diabetes treatment for perioperative medicine, particularly the changes due to new oral antidiabetic agents and insulin pump treatment. Some of the currently available guidelines are discussed with respect to the care of diabetes patients in childhood and adulthood in connection with an operation. Finally, possible perspectives in the field of monitoring and treatment of diabetes patients are discussed.

Glucose Level Sensing Using Single Asymmetric Split Ring Resonator

In this article, a biosensor composed of a single metamaterial asymmetric resonator is specifically designed for sensing the glucose level of 1 µL of solution. The resonator has two gaps, and one of them ends with a semicircle shape on which the glucose solution is placed. This design helps in confining the drops of glucose solutions in a specific area where the field is maximally confined in order to enhance the electromagnetic wave-matter interaction. Six samples of glucose solutions with concentrations that cover hypoglycemia, normal and hyperglycemia conditions that vary from around 41 to 312 mg/dL were prepared and examined by this biosensor. The resonance frequency redshift was used as a measure of the changes in the glucose level of the solutions. Without glucose solution, an excellent agreement between the measured and simulated transmission amplitude was observed. The increase in glucose concentrations exhibited clear and noticeable redshifts in the resonance frequency. This biosensor revealed a 0.9997 coefficient of determination, which implies an excellent prediction fitting model. More importantly, a sensitivity of 438 kHz/(mg/dL) was observed over the range of concentrations of the aqueous solution.

Noninvasive Monitoring of Glucose Using Near-Infrared Reflection Spectroscopy of Skin-Constraints and Effective Novel Strategy in Multivariate Calibration

For many years, successful noninvasive blood glucose monitoring assays have been announced, among which near-infrared (NIR) spectroscopy of skin is a promising analytical method. Owing to the tiny absorption bands of the glucose buried among a dominating variable spectral background, multivariate calibration is required to achieve applicability for blood glucose self-monitoring. The most useful spectral range with important analyte fingerprint signatures is the NIR spectral interval containing combination and overtone vibration band regions. A strategy called science-based calibration (SBC) has been developed that relies on a priori information of the glucose signal ("response spectrum") and the spectral noise, i.e., estimates of the variance of a sample population with negligible glucose dynamics. For the SBC method using transcutaneous reflection skin spectra, the response spectrum requires scaling due to the wavelength-dependent photon penetration depth, as obtained by Monte Carlo simulations of photon migration based on estimates of optical tissue constants. Results for tissue glucose concentrations are presented using lip NIR-spectra of a type-1 diabetic subject recorded under modified oral glucose tolerance test (OGTT) conditions. The results from the SBC method are extremely promising, as statistical calibrations show limitations under the conditions of ill-posed equation systems as experienced for tissue measurements. The temporal profile differences between the glucose concentration in blood and skin tissue were discussed in detail but needed to be further evaluated.

Recent advances in biofluid detection with micro/nanostructured bioelectronic devices

Most biofluids contain a wide variety of biochemical components that are closely related to human health. Analyzing biofluids, such as sweat and tears, may deepen our understanding in pathophysiologic conditions associated with human body, while providing a variety of useful information for the diagnosis and treatment of disorders and disease. Emerging classes of micro/nanostructured bioelectronic devices for biofluid detection represent a recent breakthrough development of critical importance in this context, including traditional biosensors (TBS) and micro/nanostructured biosensors (MNBS). Related biosensors are not restricted to flexible and wearable devices; solid devices are also involved here. This article is a timely overview of recent technical advances in this field, with an emphasis on the new insights of constituent materials, design architectures and detection methods of MNBS that support the necessary levels of biocompatibility, device functionality, and stable operation for component analysis. An additional section discusses and analyzes the existing challenges, possible solutions and future development of MNBS for detecting biofluids.

Evaluation of Opportunities and Limitations of Mid-Infrared Skin Spectroscopy for Noninvasive Blood Glucose Monitoring

BACKGROUND

A wide range of optical techniques has recently been presented for the development of noninvasive methods for blood glucose sensing based on multivariate skin spectrum analysis, and most recent studies are reviewed in short by us. The vibrational spectral fingerprints of glucose, as especially found in the mid-infrared or Raman spectrum, have been suggested for achieving largest selectivity for the development of noninvasive blood glucose methods.

METHODS

Here, the different aspects on integral skin measurements are presented, which are much dependent on the absorption characteristics of water as the main skin constituent. In particular, different mid-infrared measurement techniques as realized recently are discussed. The limitations of the use of the attenuated total reflection technique in particular are elaborated, and confounding skin or saliva spectral features are illustrated and discussed in the light of recently published works, claiming that the attenuated total reflection technique can be utilized for noninvasive measurements.

RESULTS

It will be shown that the penetration depth of the infrared radiation with wavelengths around 10 µm is the essential parameter, which can be modulated by different measurement techniques as with photothermal or diffuse reflection. However, the law of physics is limiting the option of using the attenuated total reflection technique with waveguides from diamond or similar optical materials.

CONCLUSIONS

There are confounding features from mucosa, stratum corneum, or saliva, which have been misinterpreted for glucose measurements. Results of an earlier study with multivariate evaluation based on glucose fingerprint features are again referred to as a negative experimental proof.

Proof of Concept for a New Raman-Based Prototype for Noninvasive Glucose Monitoring

BACKGROUND

Noninvasive glucose monitoring (NIGM) in diabetes is a long-sought-for technology. Among the many attempts Raman spectroscopy was considered as the most promising because of its glucose specificity. In this study, a recently developed prototype (GlucoBeam, RSP Systems A/S, Denmark) was tested in patients with type 1 diabetes to establish calibration models and to demonstrate proof of concept for this device in real use.

METHODS

The NIGM table-top prototype was used by 15 adult subjects with type 1 diabetes for up to 25 days at home and in an in-clinic setting. On each day, the subjects performed at least six measurement units throughout the day. Each measurement unit comprised two capillary blood glucose measurements, two scans with an intermittent scanning continuous glucose monitoring (CGM) system, and two NIGM measurements using the thenar of the subject's right hand.

RESULTS

Calibration models were established using data from 19 to 24 days. The remaining 3-8 days were used for independent validation. The mean absolute relative difference of the NIGM prototype was 23.6% ± 13.1% for the outpatient days, 28.2% ± 9.9% for the in-clinic day, and 26.3% ± 10.8% for the complete study. Consensus error grid analysis of the NIGM prototype for the complete study showed 93.6% of values in clinically acceptable zones A and B.

CONCLUSIONS

This proof of concept study demonstrated a practical realization of a Raman-based NIGM device, with performance on par with early-generation CGM systems. The findings will assist in further performance improvements of the device.

A highly sensitive quartz crystal microbalance sensor modified with antifouling microgels for saliva glucose monitoring

Saliva glucose detection based on quartz crystal microbalance (QCM) technology has become an important research direction of non-invasive blood glucose monitoring. However, the performance of this label-free glucose sensor is heavily deteriorated by the large amount of protein contaminants in saliva. Here, we successfully achieved the direct detection of saliva glucose by endowing the microgels on the QCM chip with superior protein-resistive and glucose-sensitive properties. Specifically, the microgel networks provide plenty of boric acid binding sites to amplify the signals of targeted glucose. The amino acid layer wrapped around the microgel and crosslinking layer can effectively eliminate the impact of non-specific proteins in saliva. The designed QCM sensor has a good linearity in the glucose concentration range of 0-40 mg L^-1 in the pH range of 6.8-7.5, satisfying the physiological conditions of saliva glucose. Moreover, the sensor has excellent ability to tolerate proteins, enabling it to detect glucose in 50% human saliva. This result provides a new approach for non-invasive blood glucose monitoring based on QCM.

Perspective on the increasing role of optical wearables and remote patient monitoring in the COVID-19 era and beyond

SIGNIFICANCE

The COVID-19 pandemic is changing the landscape of healthcare delivery in many countries, with a new shift toward remote patient monitoring (RPM).

AIM

The goal of this perspective is to highlight the existing and future role of wearable and RPM optical technologies in an increasingly at-home healthcare and research environment.

APPROACH

First, the specific changes occurring during the COVID-19 pandemic in healthcare delivery, regulations, and technological innovations related to RPM technologies are reviewed. Then, a review of the current state and potential future impact of optical physiological monitoring in portable and wearable formats is outlined.

RESULTS

New efforts from academia, industry, and regulatory agencies are advancing and encouraging at-home, portable, and wearable physiological monitors as a growing part of healthcare delivery. It is hoped that these shifts will assist with disease diagnosis, treatment, management, recovery, and rehabilitation with minimal in-person contact. Some of these trends are likely to persist for years to come. Optical technologies already account for a large portion of RPM platforms, with a good potential for future growth.

CONCLUSIONS

The biomedical optics community has a potentially large role to play in developing, testing, and commercializing new wearable and RPM technologies to meet the changing healthcare and research landscape in the COVID-19 era and beyond.

Surface Enhanced Raman Spectroscopy Based Biosensor with a Microneedle Array for Minimally Invasive In Vivo Glucose Measurements

To monitor blood glucose levels reliably, diabetic patients usually have to undergo frequent fingerstick tests to draw out fresh blood, which is painful and inconvenient with the potential risk of cross contamination especially when the lancet is reused or not properly sterilized. This work reports a novel surface-enhanced Raman spectroscopy (SERS) sensor for the in situ intradermal detection of glucose based on a low-cost poly(methyl methacrylate) microneedle (PMMA MN) array. After incorporating 1-decanethiol (1-DT) onto the silver-coated array surface, the sensor was calibrated in the range of 0-20 mM in skin phantoms then tested for the in vivo quantification of glucose in a mouse model of streptozocin (STZ)-induced type I diabetes. The results showed that the functional poly(methyl methacrylate) microneedle (F-PMMA MN) array was able to directly measure glucose in the interstitial fluid (ISF) in a few minutes and retain its structural integrity without swelling. The Clarke error grid analysis of measured data indicated that 93% of the data points lie in zones A and B. Moreover, the MN array exhibited minimal invasiveness to the skin as the skin recovered well without any noticeable adverse reaction in 10 min after measurements. With further improvement and proper validation, this polymeric MN array-based SERS biosensor has the potential to be used in painless glucose monitoring of diabetic patients in the future.

A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography

Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created the necessity for continuous noninvasive glucose monitoring. This article presents a wearable system for glucose monitoring based on a single wavelength near-infrared (NIR) Photoplethysmography (PPG) combined with machine-learning regression (MLR). The PPG readout circuit consists of a switched capacitor Transimpedance amplifier with 1 MΩ gain and a 10-Hz switched capacitor LPF. It allows a DC bias current rejection up to 20 μA with an input-referred current noise of 7.3 pA/√Hz. The proposed digital processor eliminates motion artifacts, and baseline drifts from PPG signal, extracts six distinct features and finally predicts the blood glucose level using Support Vector Regression with Fine Gaussian kernel (FGSVR) MLR. A novel piece-wise linear (PWL) approach for the exponential function is proposed to realize the FGSVR on-chip. The overall system is implemented using a 180 nm CMOS process with a chip area of 4.0 mm² while consuming 1.62 mW. The glucose measurements are performed for 200 subjects with R² of 0.937. The proposed system accurately predicts the sugar level with a mean absolute relative difference (mARD) of 7.62%.