Search Results (126)

Milk lactose concentration has been proposed as a noninvasive indicator of metabolic health in dairy cows, particularly during early lactation when metabolic demands are elevated. This study aimed to investigate the relationship between milk lactose levels and physiological, biochemical, and behavioral parameters in early-lactation Holstein cows. Twenty-eight clinically healthy cows were divided into two groups: Group 1 (milk lactose < 4.70%, n = 14) and Group 2 (milk lactose ≥ 4.70%, n = 14). Both groups were monitored over a 21-day period using the Brolis HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania) and SmaXtec intraruminal boluses (SmaXtec Animal Care Technology^®, Graz, Austria). Parameters including milk yield, milk composition (lactose, fat, protein, and fat-to-protein ratio), blood biomarkers, and behavior were recorded. Cows with higher milk lactose concentrations (≥4.70%) produced significantly more milk (+12.76%) and showed increased water intake (+15.44%), as well as elevated levels of urea (+21.63%), alanine aminotransferase (ALT) (+22.96%), glucose (+4.75%), magnesium (+8.25%), and iron (+13.41%) compared to cows with lower lactose concentrations (<4.70%). A moderate positive correlation was found between milk lactose and urea levels (r = 0.429, p < 0.01), and low but significant correlations were observed with other indicators. These findings support the use of milk lactose concentration as a practical biomarker for assessing metabolic and physiological status in dairy cows, and highlight the value of integrating real-time monitoring technologies in precision livestock management. Full article

Mid-infrared passive spectroscopic imaging is a novel non-invasive and remote sensing method based on Planck’s law. It enables the acquisition of component-specific information from the human body by measuring naturally emitted thermal radiation in the mid-infrared region. Unlike active methods that require an external light source, our passive approach harnesses the body’s own emission, thereby enabling safe, long-term monitoring. In this study, we successfully demonstrated the simultaneous, non-invasive measurements of blood glucose and lactate levels of the human body using this method. The measurements, conducted over approximately 80 min, provided emittance data derived from mid-infrared passive spectroscopy that showed a temporal correlation with values obtained using conventional blood collection sensors. Furthermore, to evaluate localized metabolic changes, we performed k-means clustering analysis of the spectral data obtained from the upper arm. This enabled visualization of time-dependent lactate responses with spatial resolution. These results demonstrate the feasibility of multi-component monitoring without physical contact or biological sampling. The proposed technique holds promise for translation to medical diagnostics, continuous health monitoring, and sports medicine, in addition to facilitating the development of next-generation healthcare technologies. Full article

Diabetes Mellitus is considered one of the most widespread diseases in the world. Traditional glucose monitoring devices carry discomfort and risks associated with the frequent extraction of blood from users. The present article proposes a noninvasive glucose estimation system based on the application of Mel Frequency Cepstral Coefficients (MFCCs) for the characterization of photoplethysmographic signals (PPG). Two variants of the MFCC feature extraction methods are evaluated along with three machine learning techniques for the development of an effective regression function for the estimation of glucose concentration. A comparison between the performance of the algorithms revealed that the best combination achieved a mean absolute error of 9.85 mg/dL and a correlation of 0.94 between the estimated concentration and the real glucose values. Similarly, 99.53% of the validation samples were distributed within zones A and B of the Clarke Error Grid Analysis. The proposed system achieves levels of correlation comparable to analogous technologies that require earlier calibration for its operation, which indicates a strong potential for the future use of the algorithm as an alternative to invasive monitoring devices. Full article

Diabetes poses a significant global health challenge, underscoring the urgent need for accurate and continuous glucose monitoring technologies. This review provides a comprehensive analysis of both invasive and non-invasive sensor technologies, with a particular focus on antenna-sensors and their working principle. Key aspects, including the selection of substrates and conductive materials, fabrication techniques, and recent advancements in rigid and flexible antenna-sensor designs, are critically evaluated. Notably, textile antenna-sensors are gaining increasing attention due to their potential for seamless integration into daily clothing. Furthermore, the influence of the human body on antenna-sensor performance is examined, emphasizing the importance of human phantom simulation and fabrication for precise modeling and validation. Finally, this review highlights the current technical challenges in the development of flexible antenna-sensors and discusses their transformative potential in enabling next-generation, non-invasive, and patient-centric glucose monitoring solutions. Full article

This paper presents the design of an orientation-independent non-invasive glucose sensor based on a meta-structured antenna. The sensor is designed for blood glucose measurement through fingertip placement on the sensor and features a mushroom structure to generate zeroth-order resonance (ZOR). Moreover, the mushroom structure has a hexagonal patch for orientation-independent non-invasive sensing. The operating frequency of the sensor is 4 GHz, and the overall size is 55 mm × 55 mm. In our study, the range of glucose concentration is from 50 to 250 mg/dL, with a step size of 50 mg/dL. The simulated and measured results show a linear relationship between the resonance frequency and the glucose concentration in the solution, and the linear shift of 0.352 MHz/mg/dL has been observed. On the other hand, the reflection coefficient level variation is a nonlinear function of the glucose concentration for the considered concentration ranges. Mathematical models describing the sensor response across all fingertip orientations are developed for the designed sensor using the regression analysis (R² ≥ 0.993) relating the glucose concentration to the measured resonance frequency and reflection coefficient level. While the reflection coefficient shows a nonlinear response, the resonance frequency exhibits a strong linear correlation with glucose concentration, making it a more reliable parameter for accurate prediction in the proposed sensing model. Full article

Background/Objectives: The increasing prevalence of diabetes underscores the urgent need for non-invasive, rapid, and cost-effective diagnostic alternatives. This study presents a breath-based multiclass diabetes classification system leveraging only three gas sensors (CO, alcohol, and acetone) to analyze exhaled breath composition. Methods: Breath samples were collected from 58 participants (22 healthy, 7 prediabetic, and 29 diabetic), with blood glucose levels serving as the reference metric. To enhance classification performance, we introduced a novel biomarker, the alcohol-to-acetone ratio, through a feature engineering approach. Class imbalance was addressed using the Synthetic Minority Over-Sampling Technique (SMOTE), ensuring a balanced dataset for model training. A nested cross-validation framework with 3 outer and 3 inner folds was implemented. Multiple machine learning classifiers were evaluated, with Random Forest and Gradient Boosting emerging as the top-performing models. Results: An ensemble combining both yielded the highest overall performance, achieving an average accuracy of 98.86%, precision of 99.07%, recall of 98.81% and F1 score of 98.87%. These findings highlight the potential of gas sensor-based breath analysis as a highly accurate, scalable, and non-invasive method for diabetes screening. Conclusions: The proposed system offers a promising alternative to blood-based diagnostic approaches, paving the way for real-world applications in point-of-care diagnostics and continuous health monitoring. Full article

Non-invasive near-infrared (NIR) human glucose sensors have attracted great interest in managing diabetes mellitus and those with complex sensing backgrounds due to glucose absorption spectrum overlap. Here, we propose a non-invasive and highly accurate multi-wavelength light NIR glucose sensor using a multilevel metric learning-back propagation network, i.e., “HMML-BP”, based on the narrowband multi-wavelength light NIR system. Our human glucose sensing method combines the advantages of this system and an HMML-BP network. The latter is composed of multilevel metric learning modules and a BP network to predict blood glucose concentrations. The narrowband multi-wavelength light NIR sensing system consists of six-channel NIR filters with center wavelengths of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm and a spectral resolution below 12 nm. The six NIR channels measured were first entered into the MML modules to build 3D multi-wavelength light data. Next, 3D multi-wavelength light data were optimized by stochastic neighbor embedding. Diffusion maps and factor analysis algorithms were used to retain effective NIR information. Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. Our blood glucose sensing method provides a new way of utilizing multi-wavelength light and hyperspectral information for smart human glucose monitoring. Full article

Non-invasive glucose monitoring has become a critical area of research for diabetes management, offering a less intrusive and more patient-friendly alternative to traditional methods such as finger-prick tests. This study presents a novel approach using a semi-solid tissue-mimicking phantom designed to replicate the dielectric properties of human skin and blood vessels. The phantom was simplified to focus solely on the skin layer, with embedded channels representing veins to achieve realistic glucose monitoring conditions. These channels were filled with D-(+)-Glucose solutions at varying concentrations (60 mg/dL to 200 mg/dL) to simulate physiological changes in blood glucose levels. A miniature patch antenna optimized to operate at 14 GHz with a penetration depth of approximately 1.5 mm was designed and fabricated. The antenna was tested in direct contact with the skin phantom, allowing for precise measurements of the changes in glucose concentration without interference from deeper tissue layers. Simulations and experiments demonstrated the antenna’s sensitivity to variations in glucose concentration, as evidenced by measurable shifts in the dielectric properties of the phantom. Importantly, the system enabled stationary measurements by injecting glucose solutions into the same blood vessels, eliminating the need to reposition the sensor while ensuring reliable and repeatable results. This work highlights the importance of shallow penetration depth in targeting close vessels for noninvasive glucose monitoring, and emphasizes the potential of microwave-based sensing systems as a practical solution for continuous glucose management. Full article

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to PPG signals can predict BGLs with high accuracy. However, training DNN models requires large and diverse datasets, which are typically distributed across multiple healthcare institutions. Privacy concerns and regulatory restrictions further limit data sharing, making conventional centralized machine learning (ML) approaches less effective. To address these challenges, this study proposes a federated learning (FL)-based solution that enables multiple healthcare organizations to collaboratively train a global model without sharing raw patient data, thereby enhancing model performance while ensuring data privacy and security. In the data preprocessing stage, continuous wavelet transform (CWT) is applied to smooth PPG signals and remove baseline drift. Adaptive cycle-based segmentation (ACBS) is then used for signal segmentation, followed by particle swarm optimization (PSO) for feature selection, optimizing classification accuracy. The proposed system was evaluated on diverse datasets, including VitalDB and MUST, under various conditions with data collected during surgery and anesthesia. The model achieved a root mean square error (RMSE) of 19.1 mg/dL, demonstrating superior predictive accuracy. Clarke error grid analysis (CEGA) confirmed the model’s clinical reliability, with 99.31% of predictions falling within clinically acceptable limits. The FL-based approach outperformed conventional deep learning models, making it a promising method for non-invasive, privacy-preserving glucose monitoring. Full article

Noninvasive blood glucose monitoring is crucial for diabetes management, and photoacoustic spectroscopy (PAS) offers a promising solution by detecting glucose levels through human skin. However, weak acoustic signals in PAS systems require optimized resonator designs for enhanced detection sensitivity. Designing such resonators physically is complex, requiring the precise identification of critical parameters before practical implementation. This study focused on optimizing a T-shaped photoacoustic resonator using finite element modeling in a COMSOL Multiphysics environment. By systematically varying the geometric design parameters of the T-cell resonator, a maximum increase in the pressure amplitude of 12.76 times with a quality factor (Q-factor) of 47.5 was achieved compared to the previously designed reference acoustic resonator. This study took a significant step forward by identifying key geometric parameters that influence resonator performance, paving the way for more sensitive and reliable noninvasive glucose monitoring systems. Full article

This paper introduces a novel-shaped, compact, multiband monopole antenna sensor incorporating an irregular curved split-ring resonator (SRR) design for non-invasive, continuous monitoring of human blood glucose levels (BGL). The sensor operates at multiple resonance frequencies: 0.94, 1.5, 3, 4.6, and 6.3 GHz, achieving coefficient reflection impedance bandwidths ≤ −10 dB of 4%, 1%, 3.5%, 65%, and 50%, respectively. Additionally, novel shapes of two SRR metamaterial cells create notches at 1.7 GHz and 4.4 GHz. The antenna is fabricated on an economical FR4 substrate with compact dimensions of 35 × 50 × 1.6 mm³. The sensor’s performance is evaluated using 3D electromagnetic software, incorporating a human finger phantom model and applying the Cole–Cole model to mimic the blood layer’s sensitivity to blood glucose variations. The phantom model is positioned at different angles relative to the biosensor to detect frequency shifts corresponding to different glucose levels. Experimental validation involves placing a real human finger around the sensor to measure resonant frequency, magnitude, and phase changes. The fabricated sensor demonstrates a superior sensitivity of 24 MHz/mg/dL effectiveness compared to existing methods. This emphasizes its potential for practical, non-invasive glucose monitoring applications. Full article

The growing demand for real-time, non-invasive, and cost-effective health monitoring has driven significant advancements in portable point-of-care testing (POCT) devices. Among these, optical biosensors have emerged as promising tools for the detection of critical biomarkers such as uric acid (UA) and blood glucose. Different optical transduction methods, like fluorescence, surface plasmon resonance (SPR), and colorimetric approaches, are talked about, with a focus on how sensitive, specific, and portable they are. Despite considerable advancements, several challenges persist, including sensor stability, miniaturization, interference effects, and the need for calibration-free operation. This review also explores issues related to cost-effectiveness, data integration, and wireless connectivity for remote monitoring. The review further examines regulatory considerations and commercialization aspects of optical biosensors, addressing the gap between research developments and clinical implementation. Future perspectives emphasize the integration of artificial intelligence (AI) and healthcare for improved diagnostics, alongside the development of wearable and implantable biosensors for continuous monitoring. Innovative optical biosensors have the potential to change the way people manage their health by quickly and accurately measuring uric acid and glucose levels. This is especially true as the need for decentralized healthcare solutions grows. By critically evaluating existing work and exploring the limitations and opportunities in the field, this review will help guide the development of more efficient, accessible, and reliable POCT devices that can improve patient outcomes and quality of life. Full article

Diabetes, a chronic medical condition, affects millions of people worldwide and requires consistent monitoring of blood glucose levels (BGLs). Traditional invasive methods for BGL monitoring can be challenging and painful for patients. This study introduces a non-invasive, deep learning (DL)-based approach to estimate BGL using photoplethysmography (PPG) signals. Specifically, a Deep Sparse Capsule Network (DSCNet) model is proposed to provide accurate and robust BGL monitoring. The proposed model’s workflow includes data collection, preprocessing, feature extraction, and predictions. A hardware module was designed using a PPG sensor and Raspberry Pi to collect patient data. In preprocessing, a Savitzky–Golay filter and moving average filter were applied to remove noise and preserve pulse form and high-frequency components. The DSCNet model was then applied to predict the sugar level. Two models were developed for prediction: a baseline model, DSCNet, and an enhanced model, DSCNet with self-attention. DSCNet’s performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Relative Difference (MARD), and coefficient of determination (R²), yielding values of 3.022, 0.05, 0.058, 0.062, 10.81, and 0.98, respectively. Full article

Background/Objectives: Glaucoma, a leading cause of irreversible blindness, has been associated with systemic and ocular aging processes. This study aimed to investigate the relationship between glaucoma and accelerated biological aging using fundus-derived age prediction. Additionally, the role of systemic factors and retinal vascular changes in this association was explored. Methods: A total of 6023 participants, including 547 glaucoma patients and 547 matched controls, were analyzed. Fundus-derived predicted age was assessed using a deep learning model (EfficientNet). Systemic factors such as BMI, blood pressure, lipid profiles, liver function markers, glucose levels, and retinal vascular changes (Scheie classifications) were analyzed. Statistical comparisons and multivariate regression analyses were performed to evaluate the impact of glaucoma on predicted age acceleration, adjusting for age, gender, and systemic factors. Results: Glaucoma was significantly associated with higher predicted age acceleration (prediction difference: −1.5 ± 4.5 vs. −2.1 ± 4.5 years; p = 0.040). Multivariate regression confirmed that glaucoma independently influenced predicted age (p = 0.021) and prediction difference (p = 0.021). Among systemic factors, γ-GTP was positively associated with prediction difference (p = 0.036), while other factors, such as BMI, blood pressure, and glucose levels, showed no significant association. Retinal vascular changes, including hypertensive and sclerotic changes (Scheie classifications), were significantly more prevalent in glaucoma patients and correlated with predicted age acceleration. Conclusions: Glaucoma is associated with accelerated biological aging, as indicated by fundus-derived predicted age. Systemic factors such as γ-GTP and retinal vascular changes may play contributory roles. Fundus-derived predicted age holds promise as a non-invasive biomarker for monitoring systemic aging. Further longitudinal studies are warranted to establish causal relationships and enhance clinical applications. Full article

Background/Objectives: Diabetes mellitus is characterized by a dysregulated glucose metabolism, necessitating frequent and often invasive monitoring techniques for its effective management. Saliva, a non-invasive and readily accessible biofluid, has been proposed as a potential alternative for glycemic monitoring due to its biochemical correlation with blood glucose levels. This scoping review aims to evaluate the evidence regarding the use of salivary glucose as a biomarker to track glycemic changes in diabetic populations. Methods: This study adhered to the Joanna Briggs Institute guidelines and the PRISMA Extension for Scoping Reviews. A literature search was performed across the PubMed, Scopus, and Web of Science databases, supplemented by manual searches. Results: A total of fifty-seven studies were included, representing populations affected by type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes (GD). The findings indicated consistent positive correlations between the salivary and blood glucose levels in most studies, although there were significant variations in the sensitivity, specificity, and methodological approaches. Salivary glucose showed promise as a complementary biomarker for glycemic monitoring, particularly due to its non-invasive nature. Conclusions: Challenges such as variability in salivary composition, the absence of standardized collection protocols, and the limited availability of portable devices were noted. This review highlights the potential of saliva as an adjunct sample for diabetes management while stressing the need for further research to bridge existing gaps. Full article