Non-invasive Glucose Research Articles

A NON-INVASIVE BLOOD GLUCOSE MONITORING FOR DIABETICS WITH BREATH BIOMARKERS: AN ENSEMBLE-OF-CLASSIFIERS MODEL

As a supplemental technique for disease diagnosis, expelled breath monitoring is becoming extremely prevalent. Nevertheless, because of the significant number of factors that must be considered, researchers are forced to create new algorithms for accurate data interpretations. In our study, a novel noninvasive breath biomarkers-based diabetic detection strategy is introduced by upcoming three main processes “pre-processing, feature extraction and diabetes detection”. Raw data (breath signal) are initially pre-processed via data normalization to increase acquired breath signal quality. Most important features, such as “Improved empirical wavelet functions (IEWF), empirical mode decomposition (EMD), local mean decomposition (LMD), continuous wavelet transform (CWT) method, and entropy-based feature,” are extracted from the pre-processed data. These attributes are fused together and are subsequently used to train the ensemble of classifiers in the diabetes detection stage. An ensemble of classifiers, including an “optimized quantum deep neural network (QDNN), a convolutional neural network (CNN), and a recurrent neural network (RNN)”, is used to depict the diabetes detection phase. The CNN and RNN are trained using the retrieved fused features. The optimized QDNN receives the output of the CNN and RNN. The final detected result is provided by the optimized QDNN. A unique hybrid optimization model, Archimedes Principle with Enhanced Honey Badger Exploitation (APEHBE) algorithm, that combines the Archimedes Optimization Algorithm (ArchOA) and the Honey Badger Algorithm (HBA) is used to fine-tune the weight of QDNN. The effectiveness of the anticipated model is then verified by a comparison analysis.

Treatment of small intestinal bacterial overgrowth: Conventional antibiotic therapy and alternative therapy - probiotics and low FODMAP diet

Introduction and purpose of the study: Small intestinal bacterial overgrowth syndrome (SIBO) is a syndrome characterized by an increased number of bacteria in the small intestine. The condition causes many non-specific symptoms such as diarrhea, abdominal pain and bloating. The purpose of this study is to review the current knowledge regarding treatment options for SIBO. 
 Methodology: A literature review was conducted based on PubMed, GoogleScholar databases and American College of Gastroenterology (ACG) guidelines.
 Current knowledge: SIBO is a heterogeneous syndrome. Symptoms of bacterial overgrowth include bloating, abdominal pain and abnormal bowel motility. In more severe cases, patients may experience malabsorption leading to weight loss and malnutrition. SIBO can occur in healthy individuals, but very often accompanies other conditions. The primary test for diagnosis is non-invasive lactulose or glucose breath tests. For treatment, the antibiotic rifaximin is preferred. It exhibits a broad spectrum of activity and a low toxicity profile. Treatment may also include diet therapy and the use of probiotics.
 Summary: SIBO continues to be both a diagnostic and therapeutic problem. Effective treatment includes not only elimination of the bacteria but also treatment of predisposing conditions. Conclusions of the study show a positive effect of rifaximin on the reduction of SIBO symptoms and improvement of patients' quality of life. The use of certain probiotics has a proven effect. According to the currently available literature, the effectiveness of the low-FODMAP diet in SIBO is hypothetical and further studies are needed to unequivocally confirm its efficacy.

Noninvasive Blood Glucose Detection Using an Improved Sparrow Search Algorithm Combined with an Extreme Learning Machine Based on Near-Infrared Spectroscopy

Noninvasive Blood Glucose Detection Using an Improved Sparrow Search Algorithm Combined with an Extreme Learning Machine Based on Near-Infrared Spectroscopy

Screen-Printed Textile-Based Electrochemical Biosensor for Noninvasive Monitoring of Glucose in Sweat.

Wearable sweat biosensors for noninvasive monitoring of health parameters have attracted significant attention. Having these biosensors embedded in textile substrates can provide a convenient experience due to their soft and flexible nature that conforms to the skin, creating good contact for long-term use. These biosensors can be easily integrated with everyday clothing by using textile fabrication processes to enhance affordable and scalable manufacturing. Herein, a flexible electrochemical glucose sensor that can be screen-printed onto a textile substrate has been demonstrated. The screen-printed textile-based glucose biosensor achieved a linear response in the range of 20-1000 µM of glucose concentration and high sensitivity (18.41 µA mM-1 cm-2, R2 = 0.996). In addition, the biosensors show high selectivity toward glucose among other interfering analytes and excellent stability over 30 days of storage. The developed textile-based biosensor can serve as a platform for monitoring bio analytes in sweat, and it is expected to impact the next generation of wearable devices.

Towards self-driven and enzyme-free sweat glucose photoelectrochemical sensing via decorating CuO nanoparticles on TiO2 hierarchical nanotubes

It is of great importance to develop a non-invasive and portable glucose sensor to address challenges and obstacles brought by invasive glucose sensors, such as negative physical and psychological impacts to patients during the fingertip blood collection, limited environment of use for enzyme-based electrodes, and cumbersome active-driven integrated devices. Photoelectrochemical (PEC) sensors have great potential in this regard. In this work, self-driven and enzyme-free sweat glucose PEC sensing is realized by constructing TiO2 hierarchical nanotubes modified by CuO nanoparticles (CuO@TiO2 HNT). A sensitivity of 138.9 μA mM−1 cm−2 in the low concentration range (< 200 μM) and a detection limit of 0.7 μM (Signal/Noise=3) are achieved by the as-prepared CuO@TiO2 HNT photoelectrode at relatively zero bias. The testing results for sweat glucose detection before and after meal consumption are consistent with those from commercial glucometers. The CuO@TiO2 HNT photoelectrode works well at absolutely zero bias and in a wide temperature range (e.g., 0–60 °C). The extraordinary sensing performance can be mainly attributed to the following two factors: (1) the TiO2 hierarchical nanotubes have an excellent light-capturing property and large specific surface area, and (2) the CuO nanoparticles substantially facilitate surface-carrier transfer for the glucose oxidation reaction. This work provides an enzyme-free sensing photoelectrode for self-driven sweat glucose detection and an alternative route to portable and noninvasive sensing of glucose.

Glycemia control using remote technologies

Diabetes mellitus is a chronic metabolic disease with a rapidly increasing prevalence. Glycemic control in diabetes mellitus remains the key to improving the effectiveness of therapy, reducing the risk of hypoglycemia, preventing microvascular complications, and reducing the long-term risk of macrovascular complications. However, regular glycemic control is only a part of this process, since an equally important step is the timely and correct interpretation of the data obtained, as well as the decision on further therapeutic tactics. Technological advances are providing tools to help diabetic patients reach their glycemic targets and facilitate ongoing monitoring of blood glucose levels. Currently, there is the possibility of remote monitoring of glycemia, transmission of data to medical professionals and caregivers: blood glucose meters with wireless glucose reporting, continuous glucose monitors, flash glucose monitors, and non-invasive glucose monitoring systems. Large scientific studies have proven the effectiveness and prospects of telemedicine technologies in the treatment of diabetes. Today, the digitalization of healthcare is actively developing from telemedicine and remote interaction with patients to new digital approaches to diagnostics and information exchange. Thus, in the Russian Federation, within the framework of the digital transformation strategy of the Healthcare industry until 2024 and for the planned period until 2030, the Personal Medical Assistants project is being implemented, aimed at creating technologies for dynamic remote monitoring of patients using platforms of centralized diagnostic services based on a unified state information system in the field of healthcare.

Fast, Multiple-Use Optical Biosensor for Point-of-Care Glucose Detection with Mobile Devices Based on Bienzyme Cascade Supported on Polyamide 6 Microparticles.

Non-invasive glucose determination provides major advantages in health monitoring and protection. It enables widespread point-of-care testing, which is affordable, sensitive, specific, rapid and equipment-free. This work reports on the development and analytical performance of a colorimetric biosensor in detecting glucose in human urine. Highly porous polyamide microparticles were synthesized as the support for the glucose oxidase (GOx) and horseradish peroxidase (HRP) dyad, which was immobilized randomly or consecutively-first HRP and then GOx. The latter system was superior, as GH@PA-C showed much higher activity than the random system, and it was used to prepare the biosensor, along with the 3,3',5,5'-tetramethylbenzidine chromogen. When in contact with urine, the biosensor displayed a strict linear correlation between the color difference and the glucose concentration in urine in the range of 0.01-3.0 mM, as established by the CIELab image processing algorithm and UV-VIS measurements. The biosensor acted in 20 s and had a detection limit of 30.7 µM in urine, high operational activity at pH = 4-8 and unchanged detection performance after 30 days of storage. Its unique feature is the possibility of multiple consecutive uses without the serious deterioration of the recovery and dispersion values. These characteristics can open the way for new routines in non-invasive personal diabetes detection.

130-LB: GWave—A Novel Noninvasive Glucose Blood Monitoring Radio Wave–Based Technology

Attempts to achieve an accurate noninvasive glucose monitor to date have been unsuccessful. The GWave device (Hagar, Israel) filters out the “white noise” and uses radiofrequency that detects capillary blood glucose across the physiological range. Measuring blood as opposed to interstitial fluid glucose, allows elimination of the “lag time” and poor accuracy seen in the first day of the current invasive continuous glucose monitors. Calibration of the GWave device is not required. Original data with the first prototype device revealed a 96% and 97% zone A (N = 53) in the Clarke Error Grid (CEG) comparing to venous and capillary blood, respectively. Initial results with second generation devices in non-diabetic subjects compared to capillary blood noted 99% in CEG Zone A and a MARD of 7.1% (N = 120 measurements) (Figure). These data were reproducible with two GWave devices (MARD 5.1%). Combining first and second-generation devices (N = 43 measurements) with a range of glucose of 69 to 400 mg/dL), CEG in Zone A was 98%. Subjects with both type 1 and type 2 diabetes are now being studied to examine accuracy at a wider physiological range of glycemia. Early results from the GWave glucose monitor shows promise as the first noninvasive technology. The current on-going studies will provide more insight for its use in diabetes care. Disclosure I. B. Hirsch: Consultant; Abbott Diabetes, Lifecare, Inc., Hagar, Research Support; Beta Bionics, Inc., Insulet Corporation, Dexcom, Inc. A. Navon: Consultant; GWave. Funding Hagar Technologies

Power‐Free Contact Lens for Glucose Sensing

AbstractRecent advances in wearable devices have enabled noninvasive monitoring for healthcare applications. Smart contact lenses have gained substantial attention for medical diagnosis through the analysis of vital signs in tear fluids. However, previous studies have mostly focused on designs embedded with electronic devices or antennas for wireless transmission, which are power‐intensive and require external receivers around the ocular system. Here, the study reports a power‐free smart contact lens for noninvasive glucose sensing according to the color changes of multiple electrochromic electrodes to achieve direct data transmission without the external wireless system. The device detects various glucose concentrations, from the ordinary range (0.16–0.5 mm) to abnormally high concentrations (0.9 mm). The multi‐electrode design exhibits acceptable accuracy, with a correlation coefficient r = 0.99543 to the controlled sample and allowed low‐glucose detections with concentrations down to 0.05 mm. The device shows good reproducibility, with standard deviations of determined glucose levels of 0.0462 and 0.025 for four continuous cycles and for an interval of several days, respectively. It is believed that the reported smart contact lens has the potential for daily health monitoring by ordinary users without a power supply and external devices. Its simple electronics‐free structure will allow for immediate application to the market with cost‐effective manufacturing.

Integrated Device Based on a Sudomotor Nanomaterial for Sweat Detection

The compositions of sweat and blood are related. Therefore, sweat is an ideal noninvasive test body fluid that could replace blood for linear detection of many biomarkers, especially blood glucose. However, access to sweat samples remains limited to physical exercise, thermal stimulation, or electrical stimulation. Despite intensive research, a continuous, innocuous, and stable method for sweat stimulation and detection has not yet been developed. In this study, a nanomaterial for a sweat-stimulating gel based on the transdermal drug delivery system is presented, which transports acetylcholine chloride into the receptors of sweat glands to achieve the function of biological stimulation of skin sweating. The nanomaterial was applied to a suitable integrated sweat glucose detection device for noninvasive blood glucose monitoring. The total amount of evaporated sweat enabled by the nanomaterial is up to 35 μL·cm–2 for 24 h, and the device detects up to 17.65 μM glucose under optimal conditions, showing stable performance regardless of the user’s activity level. In addition, the in vivo test was performed and compared with several studies and products, which showed excellent detection performance and osmotic relationship. The nanomaterial and associated integrated device represent a significant advance in continuous passive sweat stimulation and noninvasive sweat glucose measurement for point-of-care applications.

Spectral analysis of multiple scattering factors of turbid media for glucose measurement using near-infrared spectroscopy.

Near-infrared (NIR) diffuse reflectance spectroscopy has been widely used for non-invasive glucose measurement in humans, as glucose can induce a significant and detectable optical signal change in tissue. However, the scattering-dominated glucose spectrum in the range of 1000 to 1700nm is easily confused with many other scattering factors, such as particle density, particle size, and tissue refractive index. Our aim is to identify the subtle distinctions between glucose and these factors through theoretical analysis and experimental verification, in order to employ suitable methods for eliminating these interferences, thus increasing the accuracy of non-invasive glucose measurement. We present a theoretical analysis of the spectra of 1000 to 1700nm for glucose and some scattering factors, which is then verified by an experiment on a 3% Intralipid solution. We found that both the theoretical and experimental results show that the effective attenuation coefficient of glucose has distinct spectral characteristics, which are distinct from the spectra caused by particle density and refractive index, particularly in the range of 1400 to 1700nm. Our findings can offer a theoretical foundation for eliminating these interferences in non-invasive glucose measurement, aiding mathematical methods to model appropriately and enhance the accuracy of glucose prediction.

A Non-Invasive Glucose Monitoring Device using NIR (Near Infra Red)

In a developing country like India, systemic chronic diseases reach unsustainable heights as the population grows. According to WHO, diabetes affects an estimated 28.7 million people worldwide, or 28.5% of the population. Diabetes affects around 8.5 million people who have yet to be diagnosed (2022). Diabetes is one of these metabolic systemic chronic diseases that has captured the world's attention. The primary goal of our research is to develop non-invasive blood glucose monitoring technology that will benefit a large number of patients. We analyze the research progress and significant problems of non-invasive blood glucose detecting technologies in our project evaluation. In our study, we present an effective NIR-based optical detecting system. The existing non-invasive glucose monitoring device had a 60% accuracy level, however the proposed model will have an accuracy of 80% and will have a significant advantage over the existing system. Furthermore, the information is continuously delivered to the personal computer via serial communication via the UART protocol.

Hybrid model with optimal features for non-invasive blood glucose monitoring from breath biomarkers

Analysis of exhaled breath is becoming more and more used as an additional diagnostic technique in medicine. Researchers must create unique algorithms for accurate data interpretation due to the sheer quantity of factors that must be considered. Therefore, a new NICBGM-based model from exhaled breath is introduced in this study. This work exploited median filtering (MF) for pre-processing. Then, “Improved Empirical Wavelet Functions (I-EWF), R-peak detection, QT intervals, PR intervals, Entropy-based feature, improved Discrete wavelet transform (DWT), Continuous Wavelet Transform (CWT), and short-time Fourier transformation (I-STFT)” are extracted. Further, optimal features are chosen, which are then put through a hybrid scheme that combines “Deep Max out (DMO) and Long Short-Term Memory (LSTM)”. Then, the mean is taken by DMO and LSTM to attain the fine result. Here, the Wild Beest Updated HGSO (WBU-HGSO) model is used to optimize the LSTM weights. The final step is an analysis that proves the superiority of the WBU-HGSO-based model.

Wearable non-invasive glucose sensors based on metallic nanomaterials.

The development of wearable non-invasive glucose sensors provides a convenient technical means to monitor the glucose concentration of diabetes patients without discomfortability and risk of infection. Apart from enzymes as typical catalytic materials, the active catalytic materials of the glucose sensor are mainly composed of polymers, metals, alloys, metal compounds, and various metals that can undergo catalytic oxidation with glucose. Among them, metallic nanomaterials are the optimal materials applied in the field of wearable non-invasive glucose sensing due to good biocompatibility, large specific surface area, high catalytic activity, and strong adsorption capacity. This review summarizes the metallic nanomaterials used in wearable non-invasive glucose sensors including zero-dimensional (0D), one-dimensional (1D), and two-dimensional (2D) monometallic nanomaterials, bimetallic nanomaterials, metal oxide nanomaterials, etc. Besides, the applications of wearable non-invasive biosensors based on these metallic nanomaterials towards glucose detection are summarized in detail and the development trend of the wearable non-invasive glucose sensors based on metallic nanomaterials is also outlook.

Machine Learning–Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study

Background Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person’s home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations. Objective This study aimed to propose a machine learning–based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns. Methods Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis. Results In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P&lt;.001), and time in range (P&lt;.001) and thus had different management needs. Conclusions The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients’ GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.

Non-invasive, intelligent, and continuous monitoring of human blood glucose with UWB dual-antenna and cascade CNN

Finger-prick blood collection process has become unrealistic for a long-term and frequent blood glucose detection. Hence, an appropriate non-invasive detection system is highly desirable to effectively address this concern. A non-invasive and intelligent dual-sensing system is forwarded in this paper. The feasibility of the proposed system has been verified using glucose solution, animal serum, and human trials. In the in vivo experiments, the detection signal exhibited a high correlation (r = 0.96) with blood glucose levels. An improved cascade convolution neural network is suggested to accurately predict the BGL. For the estimation results of BGL, the root mean squared error of 7.3217 mg dl−1 and a mean absolute relative difference of 4.7209% are achieved. The estimated results also fell by 100% in the clinically acceptable zones of the Clarke error grid analysis, indicating that the proposed system could potentially be used for clinical measurements.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions.

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

(Invited) Non-Enzymatic Glucose Sensor Fabricated By Ni-Nanowires Decorated Graphene Gated FETs

In this study, an innovative non-invasive glucose sensor made of nickel nanowire-decorated graphene-gated field-effect transistors is demonstrated. Due to the redox reaction between nickel nanowires and glucose molecules, electron exchange occurs when Ni(III) reacts with glucose to form Ni(II) and gluconolactone. Changes in electron concentration are amplified by transistors at the bottom, improving detection sensitivity. In this study, the electrical properties of glucose sensor fabricated from nickel nanowire/graphene/gold surfaces was studied. Since the CVD-grown graphene film has excellent electrical conductivity, the electrons generated by the redox reaction can be quickly and evenly dispersed. The results show that the glucose sensor with graphene film has good current stability, low detection limit and good linear relationship between current and concentration. The demonstrated nickel wire-decorated graphene-gated FET biosensor can be used for the quantification of glucose with a linear range of 10-50 mM and a detection limit of 51nM.

A smartphone-assisted “all-in-one” paper chip for one-pot noninvasive detection of salivary glucose level

Noninvasive glucose detection for diabetes monitoring, in comparison with the conventional finger-prick test that causes inevitable pain, is on demand. Salivary glucose level that possesses a well-established correlation with blood glucose level can be a promising alternative for noninvasive detection, however, the sensitivity and user-friendliness remained to be improved. Herein, an “all-in-one” paper chip was fabricated by one-pot method for sensitive and user-friendly detection of salivary glucose level. Glucose oxidase (GOx) and the chromogenic reagent, luminol, were both in situ encapsulated in a metal–organic framework, ZIF-67, to form the GOx&luminol@ZIF-67@Paper (G&L@ZIF@Paper). Owing to the specificity of GOx and the peroxidase activity of ZIF-67, the paper chips are highly sensitive and specific for glucose with a linear range from 0.2 to 2 mM, and a limit of detection of 0.12 mM (S/N = 3). Moreover, ascribing to the protection and “all-in-one” structure by ZIF-67, the chip is highly stable that maintains the detection performance after storage at room temperature in air for 4 weeks, and this strategy also facilitates the one-pot detection without multiple steps of sampling and incubation. With the assistance of a smartphone application, the glucose level in saliva can be accurately detected with “sample-in-result-out” manner, and the results were highly consistent with those measured by commercial kit. This paper chip is a promising device for daily glucose monitoring, and the highly-integrated strategy provides an innovative resolution for the construction of colorimetric paper chips.

A salivary glucose biosensor based on immobilization of glucose oxidase in Nafion-carbon nanotubes nanocomposites modified on screen printed electrode

Due to unhealthy lifestyles, the population of diabetics is rapidly increasing, therefore, the demand of glucose meter is also increasing. However, not all the people could overcome the fear of jabbing a needle during blood glucose tests. In this regard, we report a non-invasive glucose sensing electrode composed of conductive and bio-compatible Nafion-carbon nanotubes (Nf-CNT) nanocomposite and glucose oxidase (GOx), which are sequentially coated on screen-printed electrode (SPE). The Nf-CNT nanocomposite is prepared through ultrasonication followed by drop casting it onto the SPE. Subsequently, Nf-CNT/SPEs are subjected to plasma treatment followed by drop-casting of GOx to fabricate GOx/Nf-CNT/SPE electrodes. The plasma treatment alters the hydrophobic nature of Nf-CNT/SPE, which not only exposes the surface of Nf-CNT layer, but also enables it for uniform coating of GOx. The results confirm that the mixing ratio of Nafion and CNT as well as plasma treatment time significantly affects sensing performance. The GOx/Nf-CNT/SPE subjected to 1 min of plasma treatment prior to the coating of GOx shows the best performance with a sensitivity of 99.13 μA/mMcm2 and a linear range of 20–700 μM. Moreover, the GOx/Nf-CNT/SPE electrodes are also tested under artificial saliva, which shows their high potential in the diagnosis of diabetes.