A Lab-on-a-Chip-Based Non-Invasive Optical Sensor for Measuring Glucose in Saliva

High-sensitivity flip chip blue Mini-LEDs miniaturized optical instrument for non-invasive glucose detection

The colorimetric detection of glucose typically involves a peroxidase reaction producing a color, which is then recorded and analyzed. However, enzyme detection has difficulties with purification and storage. In addition, replacing enzyme detection with chemical methods involves time-consuming steps such as centrifugation and purification and the optical instruments used for colorimetric detection are often bulky and not portable. In this study, ammonium metavanadate and sulfuric acid were used to prepare the detection solution instead of peroxidase to produce color. We also analyzed the effect of different concentrations of detection solution on absorbance sensitivity. Finally, a flip chip blue Mini-LEDs miniaturized optical instrument (FC blue Mini-LEDs MOI) was designed for glucose detection using optics fiber, collimating lenses, a miniaturized spectrometer, and an FC Blue Mini-LEDs with a center wavelength of 459 nm. While detecting glucose solutions in the concentration range of 0.1-10 mM by the developed MOI, the regression equation of y = 0.0941x + 0.1341, R2 of 0.9744, the limit of detection was 2.15 mM, and the limit of quantification was 7.163 mM. Furthermore, the preparation of the detection solution only takes 10 min, and the absorbance sensitivity of the optimized detection solution could be increased by 2.3 times. The detection solution remained stable with only a 0.6% decrease in absorbance compared to the original after storing it in a refrigerated environment at 3 °C for 14 days. The method proposed in this study for detecting glucose using FC blue light Mini-LEDs MOI reduces the use of peroxidase. In addition, it has a wide detection range that includes blood as well as non-invasive saliva and tear fluids, providing patients with a miniaturized, highly sensitive, and quantifiable glucose detection system.

Monitoring saliva compositions for non-invasive detection of diabetes using a colorimetric-based multiple sensor

The increasing population of diabetic patients, especially in developing countries, has posed a serious risk to the health sector, so that the lack of timely diagnosis and treatment process of diabetes can lead to threatening complications for the human lifestyle. Here, a multiple sensor was fabricated on a paper substrate for rapid detection and controlling the progress of the diabetes disease. The proposed sensor utilized the sensing ability of porphyrazines, pH-sensitive dyes and silver nanoparticles in order to detect the differences in saliva composition of diabetic and non-diabetic patients. A unique color map (sensor response) was obtained for each studied group, which can be monitored by a scanner. Moreover, a good correlation was observed between the colorimetric response resulting from the analysis of salivary composition and the fasting blood glucose (FBG) value measured by standard laboratory instruments. It was also possible to classify participants into two groups, including patients caused by diabetes and those were non-diabetic persons with a total accuracy of 88.9%. Statistical evaluations show that the multiple sensor can be employed as an effective and non-invasive device for continuous monitoring of diabetes, substantially in the elderly.

A Novel Salivary Sensor with Integrated Au Electrodes and Conductivity Meters for Screening of Diabetes

The rise in diabetes cases is a growing concern due to the aging of populations. This not only places a strain on healthcare systems but also creates serious public health problems. Traditional blood tests are currently used to check blood sugar levels, but they are invasive and can discourage patients from regularly monitoring their levels. We recently developed nano-sensing probes that integrate Au microelectrodes and conductivity meters, requiring only 50 μL of saliva for measurement. The usage of the co-planar design of coating-free Au electrodes makes the measurement more stable, precise, and easier. This study found a positive correlation between the participant's fasting blood sugar levels and salivary conductivity. We observed a diabetes prevalence of 11.6% among 395 adults under 65 years in this study, using the glycated hemoglobin > 6.5% definition. This study found significantly higher salivary conductivity in the diabetes group, and also a clear trend of increasing diabetes as conductivity levels rose. The prediction model, using salivary conductivity, age, and body mass index, performed well in diagnosing diabetes, with a ROC curve area of 0.75. The study participants were further divided into low and high groups based on salivary conductivity using the Youden index with a cutoff value of 5.987 ms/cm. Individuals with higher salivary conductivity had a 3.82 times greater risk of diabetes than those with lower levels, as determined by the odds ratio calculation. In conclusion, this portable sensing device for salivary conductivity has the potential to be a screening tool for detecting diabetes.

Salivary biomarkers: novel noninvasive tools to diagnose chronic inflammation

Several chronic disorders including type 2 diabetes (T2D), obesity, heart disease and cancer are preceded by a state of chronic low-grade inflammation. Biomarkers for the early assessment of chronic disorders encompass acute phase proteins (APP), cytokines and chemokines, pro-inflammatory enzymes, lipids and oxidative stress mediators. These substances enter saliva through the blood flow and, in some cases, there is a close relation between their salivary and serum concentration. Saliva can be easily collected and stored with non-invasive and cost-saving procedures, and it is emerging the concept to use it for the detection of inflammatory biomarkers. To this purpose, the present review aims to discuss the advantages and challenges of using standard and cutting-edge techniques to discover salivary biomarkers which may be used in diagnosis/therapy of several chronic diseases with inflammatory consequences with the pursuit to possibly replace conventional paths with detectable soluble mediators in saliva. Specifically, the review describes the procedures used for saliva collection, the standard approaches for the measurement of salivary biomarkers and the novel methodological strategies such as biosensors to improve the quality of care for chronically affected patients.

Reshaping healthcare with wearable biosensors

Wearable health sensors could monitor the wearer's health and surrounding environment in real-time. With the development of sensor and operating system hardware technology, the functions of wearable devices have been gradually enriched with more diversified forms and more accurate physiological indicators. These sensors are moving towards high precision, continuity, and comfort, making great contributions to improving personalized health care. At the same time, in the context of the rapid development of the Internet of Things, the ubiquitous regulatory capabilities have been released. Some sensor chips are equipped with data readout and signal conditioning circuits, and a wireless communication module for transmitting data to computer equipment. At the same time, for data analysis of wearable health sensors, most companies use artificial neural networks (ANN). In addition, artificial neural networks could help users effectively get relevant health feedback. Through the physiological response of the human body, various sensors worn could effectively transmit data to the control unit, which analyzes the data and provides feedback of the health value to the user through the computer. This is the working principle of wearable sensors for health. This article focuses on wearable biosensors used for healthcare monitoring in different situations, as well as the development, technology, business, ethics, and future of wearable sensors for health monitoring.

Saliva-based microfluidic point-of-care diagnostic

There has been a long-standing interest in point-of-care (POC) diagnostics as a tool to improve patient care because it can provide rapid, actionable results near the patient. Some of the successful examples of POC testing include lateral flow assays, urine dipsticks, and glucometers. Unfortunately, POC analysis is somewhat limited by the ability to manufacture simple devices to selectively measure disease specific biomarkers and the need for invasive biological sampling. Next generation POCs are being developed that make use of microfluidic devices to detect biomarkers in biological fluids in a non-invasive manner, addressing the above-mentioned limitations. Microfluidic devices are desirable because they can provide the ability to perform additional sample processing steps not available in existing commercial diagnostics. As a result, they can provide more sensitive and selective analysis. While most POC methods make use of blood or urine as a sample matrix, there has been a growing push to use saliva as a diagnostic medium. Saliva represents an ideal non-invasive biofluid for detecting biomarkers because it is readily available in large quantities and analyte levels reflect those in blood. However, using saliva in microfluidic devices for POC diagnostics is a relatively new and an emerging field. The overarching aim of this review is to provide an update on recent literature focused on the use of saliva as a biological sample matrix in microfluidic devices. We will first cover the characteristics of saliva as a sample medium and then review microfluidic devices that are developed for the analysis of salivary biomarkers.

Optical Measurement of Molar Absorption Coefficient of HbA1c: Comparison of Theoretical and Experimental Results

Diabetes can cause dangerous complications if not diagnosed in a timely manner. The World Health Organization accepts glycated hemoglobin (HbA1c) as a measure of diagnosing diabetes as it provides significantly more information on the glycemic behavior from a single blood sample than the fasting blood sugar reading. The molar absorption coefficient of HbA1c is needed to quantify the amount of HbA1c present in a blood sample. In this study, we measured the molar absorption coefficient of HbA1c in the range of 450 nm to 700 nm using optical methods experimentally. We observed that the characteristic peaks of the molar absorption coefficient of HbA1c (at 545 nm and 579 nm for level 1, at 544 nm and 577 nm for level 2) are in close agreement with those reported in previous studies. The molar absorption coefficient values were also found to be close to those of earlier reports. The average molar absorption coefficient values of HbA1c were found to be 804,403.5 M−1cm−1 at 545 nm and 703,704.5 M−1cm−1 at 579 nm for level 1 as well as 503,352.4 M−1cm−1 at 544 nm and 476,344.6 M−1cm−1 at 577 nm for level 2. Our experiments focused on calculating the molar absorption coefficients of HbA1c in the visible wavelength region, and the proposed experimental method has an advantage of being able to easily obtain the molar absorption coefficient at any wavelength in the visible wavelength region. The results of this study are expected to help future investigations on noninvasive methods of estimating HbA1c levels.

Smartphone-Based Electrochemical Systems for Glucose Monitoring in Biofluids: A Review

As a vital biomarker, glucose plays an important role in multiple physiological and pathological processes. Thus, glucose detection has become an important direction in the electrochemical analysis field. In order to realize more convenient, real-time, comfortable and accurate monitoring, smartphone-based portable, wearable and implantable electrochemical glucose monitoring is progressing rapidly. In this review, we firstly introduce technologies integrated in smartphones and the advantages of these technologies in electrochemical glucose detection. Subsequently, this overview illustrates the advances of smartphone-based portable, wearable and implantable electrochemical glucose monitoring systems in diverse biofluids over the last ten years (2012-2022). Specifically, some interesting and innovative technologies are highlighted. In the last section, after discussing the challenges in this field, we offer some future directions, such as application of advanced nanomaterials, novel power sources, simultaneous detection of multiple markers and a closed-loop system.

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.

Recent Advancement in Biofluid-Based Glucose Sensors Using Invasive, Minimally Invasive, and Non-Invasive Technologies: A Review

Biosensors have potentially revolutionized the biomedical field. Their portability, cost-effectiveness, and ease of operation have made the market for these biosensors to grow rapidly. Diabetes mellitus is the condition of having high glucose content in the body, and it has become one of the very common conditions that is leading to deaths worldwide. Although it still has no cure or prevention, if monitored and treated with appropriate medication, the complications can be hindered and mitigated. Glucose content in the body can be detected using various biological fluids, namely blood, sweat, urine, interstitial fluids, tears, breath, and saliva. In the past decade, there has been an influx of potential biosensor technologies for continuous glucose level estimation. This literature review provides a comprehensive update on the recent advances in the field of biofluid-based sensors for glucose level detection in terms of methods, methodology and materials used.

Application of a Novel Biosensor for Salivary Conductivity in Detecting Chronic Kidney Disease

The prevalence of chronic kidney disease (CKD) is increasing, and it brings an enormous healthcare burden. The traditional measurement of kidney function needs invasive blood tests, which hinders the early detection and causes low awareness of CKD. We recently designed a device with miniaturized coplanar biosensing probes for measuring salivary conductivity at an extremely low volume (50 μL). Our preliminary data discovered that the salivary conductivity was significantly higher in the CKD patients. This cross-sectional study aims to validate the relationship between salivary conductivity and kidney function, represented by the estimated glomerular filtration rate (eGFR). We enrolled 214 adult participants with a mean age of 63.96 ± 13.53 years, of whom 33.2% were male. The prevalence rate of CKD, defined as eGFR < 60 mL/min/1.73 m2, is 11.2% in our study. By multivariate linear regression analyses, we found that salivary conductivity was positively related to age and fasting glucose but negatively associated with eGFR. We further divided subjects into low, medium, and high groups according to the tertials of salivary conductivity levels. There was a significant trend for an increment of CKD patients from low to high salivary conductivity groups (4.2% vs. 12.5% vs. 16.9%, p for trend: 0.016). The receiver operating characteristic (ROC) curves disclosed an excellent performance by using salivary conductivity combined with age, gender, and body weight to diagnose CKD (AUC equal to 0.8). The adjusted odds ratio of CKD is 2.66 (95% CI, 1.10−6.46) in subjects with high salivary conductivity levels. Overall, salivary conductivity can serve as a good surrogate marker of kidney function; this real-time, non-invasive, and easy-to-use portable biosensing device may be a reliable tool for screening CKD.

Recent Advances in Wearable Optical Sensor Automation Powered by Battery versus Skin-like Battery-Free Devices for Personal Healthcare-A Review

Currently, old-style personal Medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patient discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin are used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Basic physiological signals comprise a lot of health-related data. The estimation of critical physiological characteristics, such as pulse inconstancy or variability using photoplethysmography (PPG) and oxygen saturation in arterial blood using pulse oximetry, is possible by utilizing an analysis of the pulsatile component of the bloodstream. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. Flexible skin-like sensing devices have accomplished several functionalities previously inaccessible for typical sensing devices due to their deformability, lightness, portability, and flexibility. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors, which brings a breakthrough in wearable sensing automation.

A Distance-Based Microfluidic Paper-Based Biosensor for Glucose Measurements in Tear Range

The prevalence of diabetes has increased over the past years. Therefore, developing minimally invasive, user-friendly, and cost-effective glucose biosensors is necessary especially in low-income and developing countries. Cellulose paper-based analytical devices have attracted the attention of many researchers due to affordability, not requiring trained personnel, and complex equipment. This paper describes a microfluidic paper-based analytical device (μPAD) for detecting glucose concentration in tear range with the naked eye. The paper-based biosensor fabricated by laser CO2; and glucose oxidase/horseradish peroxidase (GOx/HRP) enzyme solution coupled with tetramethylbenzidine (TMB) were utilized as reagents. A sample volume of 10 μl was needed for the biosensor operation and the results were observable within 5 min. The color intensity-based and distance-based results were analyzed by ImageJ and Tracker to evaluate the device performance. Distance-based results showed a linear behavior in 0.1-1.2 mM with an R² = 0.9962 and limit of detection (LOD) of 0.1 mM. The results could be perceived by the naked eye without needing additional equipment or trained personnel in a relatively short time (3-5 min).

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.

Nanoplasmonic Strip Test for Salivary Glucose Monitoring

Nowadays, there is an increasing interest in Point-of-care (POC) devices for the noninvasive glucose assessment. Despite the recent progress in glucose self-monitoring, commercially available devices still use invasive samples such as blood or interstitial fluids, and they are not equipment-free and affordable for the whole population. Here, we report a fully integrated strip test for the semi-quantitative detection of glucose in whole saliva. The colorimetric mechanism consists of an enzyme-mediated reshaping of multibranched gold nanoparticles (MGNPs) into nanospheres with an associated plasmonic shift and consequent blue-to-red color change, clearly detectable in less than 10 min.

Noninvasive Estimation of Glycated Hemoglobin In-Vivo Based on Photon Diffusion Theory and Genetic Symbolic Regression Models

The diagnosis and management of diabetes require frequent monitoring of blood sugar levels. Prolonged exposure of most of the monosaccharides in the bloodstream results in the glycation of hemoglobin. This glycated hemoglobin (HbA1c) based test plays an important role to avoid diabetic complications. However, noninvasive estimation of HbA1c is a very new, promising, and challenging topic in modern bioengineering scopes. The purpose of this study is to develop and verify mathematical models in order to quantify the glycated hemoglobin in-vivo percentage non-invasively. This research utilized photon diffusion theory to develop the finger models and genetic symbolic regression methods to solve the models to estimate the level of glycated hemoglobin in the blood. The validation of these models with human participants indicated a high degree of correlation (0.887 and 0.907 Pearsons r value), and high precision (2.56% and 2.96% coefficient of variation (%CV)) for transmission and reflection type noninvasive digital volume pulse-based signals. This research will be a breakthrough for the application of noninvasive HbA1c estimation.

Wearable Glucose Monitoring and Implantable Drug Delivery Systems for Diabetes Management

The global cost of diabetes care exceeds $1 trillion each year with more than $327 billion being spent in the United States alone. Despite some of the advances in diabetes care including continuous glucose monitoring systems and insulin pumps, the technology associated with managing diabetes has largely remained unchanged over the past several decades. With the rise of wearable electronics and novel functional materials, the field is well-poised for the next generation of closed-loop diabetes care. Wearable glucose sensors implanted within diverse platforms including skin or on-tooth tattoos, skin-mounted patches, eyeglasses, contact lenses, fabrics, mouthguards, and pacifiers have enabled noninvasive, unobtrusive, and real-time analysis of glucose excursions in ambulatory care settings. These wearable glucose sensors can be integrated with implantable drug delivery systems, including an insulin pump, glucose responsive insulin release implant, and islets transplantation, to form self-regulating closed-loop systems. This review article encompasses the emerging trends and latest innovations of wearable glucose monitoring and implantable insulin delivery technologies for diabetes management with a focus on their advanced materials and construction. Perspectives on the current unmet challenges of these strategies are also discussed to motivate future technological development toward improved patient care in diabetes management.

Quantitative Analysis of Different Multi-Wavelength PPG Devices and Methods for Noninvasive In-Vivo Estimation of Glycated Hemoglobin

Diabetes is a serious disease affecting the insulin cycle in the human body. Thus, monitoring blood glucose levels and the diagnosis of diabetes in the early stages is very important. Noninvasive in vivo diabetes-diagnosis procedures are very new and require thorough studies to be error-resistant and user-friendly. In this study, we compare two noninvasive procedures (two-wavelength- and three-wavelength-based methods) to estimate glycated hemoglobin (HbA1c) levels in different scenarios and evaluate them with error level calculations. The three-wavelength method, which has more model parameters, results in a more accurate estimation of HbA1c even when the blood oxygenation (SpO2) values change. The HbA1c-estimation error range of the two-wavelength model, due to change in SpO2, is found to be from −1.306% to 0.047%. On the other hand, the HbA1c estimation error for the three-wavelength model is found to be in the magnitude of 10−14% and independent of SpO2. The approximation of SpO2 from the two-wavelength model produces a lower error for the molar concentration based technique (−4% to −1.9% at 70% to 100% of reference SpO2) as compared to the molar absorption coefficient based technique. Additionally, the two-wavelength model is less susceptible to sensor noise levels (max SD of %error, 0.142%), as compared to the three-wavelength model (max SD of %error, 0.317%). Despite having a higher susceptibility to sensor noise, the three-wavelength model can estimate HbA1c values more accurately; this is because it takes the major components of blood into account and thus becomes a more realistic model.

Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform

Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s average blood glucose and blood oxygen levels. Digital volume pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood-vessel-only hypothesis, whereas the second model is based on a whole-finger model system. The two gray-box systems were validated on diabetic and nondiabetic patients. The mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO₂) were 0.375 and 1.676 for the blood-vessel model and 0.271 and 1.395 for the whole-finger model, respectively. The repeatability analysis indicated that these models resulted in a mean percent coefficient of variation (%CV) of 2.08% and 1.74% for %HbA1c and 0.54% and 0.49% for %SpO₂ in the respective models. Herein, both models exhibited similar performances (HbA1c estimation Pearson’s R values were 0.92 and 0.96, respectively), despite the model assumptions differing greatly. The bias values in the Bland–Altman analysis for both models were – 0.03 ± 0.458 and – 0.063 ± 0.326 for HbA1c estimation, and 0.178 ± 2.002 and – 0.246 ± 1.69 for SpO2 estimation, respectively. Both models have a very high potential for use in real-world scenarios. The whole-finger model with a lower standard deviation in bias and higher Pearson’s R value performs better in terms of higher precision and accuracy than the blood-vessel model.

Pparγ1 Facilitates ErbB2-Mammary Adenocarcinoma in Mice

HER2, which is associated with clinically aggressive disease, is overexpressed in 15-20% of breast cancers (BC). The host immune system participates in the therapeutic response of HER2⁺ breast cancer. Identifying genetic programs that participate in ErbB2-induced tumors may provide the rational basis for co-extinction therapeutic approaches. Peroxisome proliferator-activated receptor γ (PPARγ), which is expressed in a variety of malignancies, governs biological functions through transcriptional programs. Herein, genetic deletion of endogenous Pparγ1 restrained mammary tumor progression, lipogenesis, and induced local mammary tumor macrophage infiltration, without affecting other tissue hematopoietic stem cell pools. Endogenous Pparγ1 induced expression of both an EphA2-Amphiregulin and an inflammatory INFγ and Cxcl5 signaling module, that was recapitulated in human breast cancer. Pparγ1 bound directly to growth promoting and proinflammatory target genes in the context of chromatin. We conclude Pparγ1 promotes ErbB2-induced tumor growth and inflammation and represents a relevant target for therapeutic coextinction. Herein, endogenous Pparγ1 promoted ErbB2-mediated mammary tumor onset and progression. PPARγ1 increased expression of an EGF-EphA2 receptor tyrosine kinase module and a cytokine/chemokine 1 transcriptional module. The induction of a pro-tumorigenic inflammatory state by Pparγ1 may provide the rationale for complementary coextinction programs in ErbB2 tumors.

Reusable, Non-Invasive, and Ultrafast Radio Frequency Biosensor Based on Optimized Integrated Passive Device Fabrication Process for Quantitative Detection of Glucose Levels

The increase in the number of people suffering diabetes has been the driving force behind the development of glucose sensors to overcome the current testing shortcomings. In this work, a reusable, non-invasive and ultrafast radio frequency biosensor based on optimized integrated passive device fabrication process for quantitative detection of glucose level was developed. With the aid of the novel biosensor design with hammer-shaped capacitors for carrying out detection, both the resonance frequency and magnitude of reflection coefficient can be applied to map the different glucose levels. Meanwhile, the corresponding fabrication process was developed, providing an approach for achieving quantitative detection and a structure without metal-insulator-metal type capacitor that realizes low cost and high reliability. To enhance the sensitivity of biosensor, a 3-min dry etching treatment based on chlorine/argon-based plasma was implemented for realizing hydrophilicity of capacitor surface to ensure that the biosensor can be touched rapidly with glucose. Based on above implementation, a non-invasive biosensor having an ultrafast response time of superior to 0.85 s, ultralow LOD of 8.01 mg/dL and excellent reusability verified through five sets of measurements are realized. The proposed approaches are not limited the development of a stable and accurate platform for the detection of glucose levels but also presents a scheme toward the detection of glucose levels in human serum.

Quantitative measurements of inorganic analytes on a digital microfluidics platform

Two methods were studied for selectively measuring the on-chip absorbance of trace sulfate analytes in droplets on a digital microfluidics (DMF) platform. In one method, the direction of measurement was perpendicular to the flat upper and lower surfaces of the DMF platform (vertical), and in the second method, the measurement direction was parallel to the DMF platform surfaces (horizontal). The channel height or the vertical light path length was 0.24 mm, and the droplet diameter was 1 mm. The DMF system employed a silicone oil transport medium whereby a thin, non-uniform oil layer formed between the droplet and the upper/lower plates which was unstable, resulting in randomly formed local oil lenses. The mobile oil lenses caused vertical absorbance measurement errors and uncertainties. The effects of the oil lenses were verified by simulation. Horizontal absorbance measurements were taken with embedded optical fibers (0.2 mm in diameter) aligned over the bottom chip surface in contact with the sides of the droplet, resulting in a horizontal light path length approximately three times that of the vertical light path. Because no oil lenses could form on the droplet’s sides, the stability of repeated horizontal measurements outperformed repeated vertical measurements made on the same droplet and on multiple droplets actuated into the measurement positions. Comparisons were based on measurement standard deviations and limits of detection (LOD). The following LODs and measurement standard deviations were achieved for horizontal measurements of multiple sulfate concentrations in 1.5 µl droplets: 7 ppm for sulfate (0.3–2.7%) and an R2 value of 0.957 from a least square data fit. Measurements on a commercial plate reader gave comparable results (200 µl liquid in each well, LOD equals 11 ppm, CV equals to 0.2–4%), even though the absorbance path was larger (0.7 mm). This LOD value means that the chip could detect 10.5 ng of sulfate. LOD values on vertical measurements were also similar, but large measurement errors from numerous outlier points yielded an R2 value of 0.735 and large average measurement standard deviations (36%).

Biosensors for Detection of Human Placental Pathologies: A Review of Emerging Technologies and Current Trends

Substantial growth in the biosensor research has enabled novel, sensitive and point-of-care diagnosis of human diseases in the last decade. This paper presents an overview of the research in the field of biosensors that can potentially predict and diagnosis of common placental pathologies. A survey of biomarkers in maternal circulation and their characterization methods is presented, including markers of oxidative stress, angiogenic factors, placental debris, and inflammatory biomarkers that are associated with various pathophysiological processes in the context of pregnancy complications. Novel biosensors enabled by microfluidics technology and nanomaterials is then reviewed. Representative designs of plasmonic and electrochemical biosensors for highly sensitive and multiplexed detection of biomarkers, as well as on-chip sample preparation and sensing for automatic biomarker detection are illustrated. New trends in organ-on-a-chip based placental disease models are highlighted to illustrate the capability of these in vitro disease models in better understanding the complex pathophysiological processes, including mass transfer across the placental barrier, oxidative stress, inflammation, and malaria infection. Biosensor technologies that can be potentially embedded in the placental models for real time, label-free monitoring of these processes and events are suggested. Merger of cell culture in microfluidics and biosensing can provide significant potential for new developments in advanced placental models, and tools for diagnosis, drug screening and efficacy testing.

An Intra-Oral Optical Sensor for the Real-Time Identification and Assessment of Wine Intake

Saliva has gained considerable attention as a diagnostics alternative to blood analyses. A wide spectrum of salivary compounds is correlated to blood concentrations of biomarkers, providing informative and discriminative data regarding the state of health. Intra-oral detection and assessment of food and beverage intake can be correlated and provides valuable information to forecast the formation and modification of salivary biomarkers. In this context, the present work proposes a novel intra-oral optical fiber sensor, developed around an optical coupler topology, and exemplified on the detection and assessment of wine intake, which is accounted for example for the formation of N^ε-carboxymethyllysine Advanced Glycation End-products. A laboratory proof of concept validates the proposed solution on four white and four red wine samples. The novel optical sensor geometry shows good spectral properties, accounting for selectivity with respect to grape-based soft drinks. This enables intra-oral detection and objective quality assessment of wine. Moreover, its implementation exploits the advantages of fiber-optics sensing and facilitates integration into a mouthguard, holding considerable potential for real-time biomedical applications to investigate Advanced Glycation End-products in the saliva and their connection with consumption of wine, for the evaluation of risk factors in diet-related diseases.

An In-Vitro Optical Sensor Designed to Estimate Glycated Hemoglobin Levels

The purpose of this research was to design an optical sensor for evaluating glycated hemoglobin (HbA1c) percentages in hemoglobin. The A1c sensors available in the market use invasive methods, while our device offers the possibility of non-invasive monitoring of HbA1c levels in diabetic patients. A prototype is assembled using two light emitting diodes with peak emission wavelengths of 535 nm and 593 nm, a photodiode, and a microcontroller. The proposed sensor measures the transmitted intensity in the form of an output voltage. We devise an approach to estimate the percentage of HbA1c in hemoglobin for a given solution. This estimation is based on the relative change in absorbance due to change in path length and molar absorption coefficients of hemoglobin and HbA1c, at the two wavelengths. We calculate the molar absorption coefficient of HbA1c at 535 nm and 593 nm wavelengths using the sensor, which is performed by a multiple variable regression analysis algorithm fed through the microcontroller. Specifically, the sensor output voltage with respect to the sample concentration is fitted to an exponentially decaying equation model. We used a commercial chemical assay called Control FD Glycohemoglobin A1c with known percentage HbA1c levels to verify our device measurements.

Naked-Eye Detection of Glucose in Saliva with Bienzymatic Paper-Based Sensor

The high incidence of Diabetes Mellitus in low-income regions has promoted the development of low-cost alternatives to replace blood-based procedures. In this work, we present a bienzymatic paper-based sensor suitable for the naked-eye detection of glucose in saliva samples. The sensor was obtained by a stamping procedure and modified with chitosan to improve the colorimetric readout. The bienzymatic reaction of GOx-HRP coupled with 2,4,6-tribromo-3-hydroxy benzoic acid was applied for the detection of glucose within a range from 0 to 180 mgdL⁻¹ in buffer and artificial saliva solutions. The visual readout was perceived by the naked eye and registered with an office scanner to evaluate the analytical performance. The results showed a limit of detection of 0.37 mgdL⁻¹ (S/N = 3) with an R.S.D. of 1.69% and a linear range from 1 to 22.5 mgdL⁻¹ with an R² of 0.99235. The analysis of human saliva samples was performed without pre-processing, achieving recoveries from 92 to 114%. The naked-eye detection was evaluated under two different light settings, showing average recoveries of 108.58 and 90.65% for standard and low illumination. The proposed device showed potential for easy-to-use, sensitive, low-cost, fast, and device-free detection of salivary glucose suitable for untrained personnel operation and limited facilities.