Search Results (1,130)

Bifunctional materials present a promising route to develop advanced devices, yet the dual performance of CoNi₂S₄ nanosheets anchored on a porous scaffold is seldom reported. Herein, we propose a rational fabrication strategy to construct a three-dimensional hierarchical electrode via the in-situ growth of densely aligned CoNi₂S₄ nanosheets on a conductive fabric scaffold. This integrated porous architecture concurrently offers an ultrahigh specific surface area, efficient mass transport, and rapid electron conduction. As a supercapacitor, the electrode achieves a high areal capacitance of 3198 mF cm⁻² at 4 mA cm⁻² and retains 98.1% of its initial capacitance after 1000 cycles at 20 mA cm⁻². As a non-enzymatic glucose sensor, it exhibits outstanding selectivity (<4.1% interference), high sensitivity (1049 μA mM⁻¹ cm⁻²), a wide linear range (1–8 mM), and a low detection limit (1 μM). These results highlight the significant potential of this binder-free, scaffold-supported nanosheet design for advancing integrated energy storage and biosensing systems. Full article

This work presents a novel compact size and sensitive band-stop filter, whose notch frequency is 5.5 GHz, and it is suggested to estimate the concentration of blood glucose non-invasively. The filter is made on FR-4, with the size of the entire structure being 15 mm × 25 mm × 1.6 mm. A human finger-phantom model, comprising layers of skin, fat, blood, and bone, is built in an EM simulation environment (HFSS) to assess the sensing performance of the human finger-phantom. The glucose content in the blood layer is kept at a range of 0 to 500 mg/dL, with the ratio of the resonant frequency shift being assessed by placing the finger phantom on the proposed filter structure. The sensing principle is based on the fact that the resonant frequency of the microwave sensor changes with changes in glucose concentration in the tissue, and this is due to the changes in the dielectric properties of the tissue. The shifts obtained in the study are used for the evaluation of glucose concentration in blood as a non-invasive technique. This work explores five microstrip band-stop filters noted as Designs I, II, III, IV, and V. In these filters, better results of minimum and maximum frequency shifts of 0.1 and 1.4 MHz in Design I and 0.1 and 2 MHz in Design IV are observed. The simulated results of Design IV are verified with measured results. Good matching is also noted at the lower frequencies. The filters are compact, cost-effective, and give better sensitivity performance. Hence, the proposed design can be used for glucose monitoring in blood samples involving a non-invasive method. Full article

Herein, an electrochemical sensor is reported for the first time based on an ordered macro–microporous composite derived from metal–organic frameworks (MOFs) for the highly sensitive detection of auramine O (AO), a Group 2B carcinogen. The hierarchical pore architecture, integrating an ordered macroporous network with a microporous ZIF-8 framework, enables the uniform dispersion of a high density of catalytically active sites. The interconnected macroporous channels facilitate efficient mass transport and rapid removal of reaction byproducts, effectively preventing pore blockage and ensuring stable sensing performance during repeated measurements. Owing to these structural advantages, the proposed sensor exhibits outstanding analytical performance toward AO detection, with a sensitivity of 0.4843 μA μM⁻¹, a detection limit of 0.168 μM (S/N = 3), and a wide linear range from 0.5 to 50 μM. Moreover, the sensor demonstrates excellent selectivity and reproducibility, maintaining reliable responses even in the presence of 100-fold excess common food constituents such as tartrazine and glucose. Real sample analysis further confirms its high accuracy and operational stability. Overall, the electrochemical sensor based on silver nanoparticle-decorated ordered macro–microporous ZIF-8 synthesized via in situ reduction shows great potential as a portable and on-site tool for rapid AO detection in food. More broadly, ordered macro–microporous MOF-derived materials represent a promising platform for advanced electrochemical sensor applications. Full article

Rapid and real-time monitoring of blood glucose concentration is critical for the diagnosis and management of diabetes, while conventional invasive detection methods suffer from inconvenience and discomfort, making non-invasive detection a research hotspot. In this study, a dual complementary split-ring resonator (DS-CSRR) operating at 3.3 GHz was designed and fabricated for non-invasive glucose concentration detection, aiming to address the problems of low sensitivity and large size of existing microwave glucose sensors. The sensor was fabricated on a low-cost FR4 dielectric substrate with dimensions of 20 × 30 × 0.8 mm³, and two U-shaped slots were incorporated into the traditional DS-CSRR structure to realize cross-polarization excitation. This design not only enhances the interaction between the electric field and glucose solution but also optimizes the quality factor (Q) and electric field distribution of the resonator without changing the overall size. Compared with the traditional DS-CSRR, the Q factor of the modified structure is increased to 130 under no-load conditions. The transmission coefficient Signal Port 2 to Port 1 (S21) of the sensor loaded with glucose solutions of different concentrations was measured using a vector network analyzer (VNA). The experimental results show a good linear frequency shift with the increase in glucose concentration, with a measured sensitivity of 1.95 kHz/(mg·dL⁻¹). In addition, the sensor is characterized by miniaturization, low cost and easy fabrication due to the adoption of standard PCB fabrication processes. This study successfully demonstrates a non-invasive microwave sensor with high sensitivity for glucose concentration detection, which has promising application potential in personal continuous glucose monitoring, and also provides a useful design strategy for the development of miniaturized high-sensitivity microwave biosensors. Full article

This study employed near-infrared (NIR) spectroscopy for real-time spectral acquisition of fermentation broth in lab-scale bioreactors, comparing the performance of transflectance and transmission sensors through glucose modeling and prediction while optimizing modeling approaches. The results demonstrated superior adaptability of transflectance sensors in fermentation environments: in conventional fermentation, glucose models exhibited lower errors (RMSEC = 4.087 g/L, RMSEV = 9.829 g/L) compared to transmission sensors (RMSEC = 5.972 g/L, RMSEV = 10.904 g/L), with significantly higher predictive performance (RPD = 3.735 vs. 2.369), indicating enhanced fitting accuracy and stability. In complex natural media containing peptone and yeast extract, transmission sensor performance deteriorated dramatically due to turbidity interference (R²_cal = 0.134), whereas transflectance sensors maintained robust performance (R²_cal = 0.993), confirming their adaptability to complex matrices. Regarding modeling strategies, the 1550–1700 nm spectral region demonstrated optimal feature extraction capability (RMSEC = 3.269 g/L, R²_cal = 0.987). Basic preprocessing methods such as the moving average smoothing method have become the preferred preprocessing methods, as they strike a balance between calibration and prediction performance. Outlier removal analysis revealed that moderate elimination of 12 high-error samples (accounting for 30% of the total 39 samples) reduced RMSEC to 1.441 g/L and improved R²_cv to 0.996, optimizing model performance; however, excessive removal of outlier samples degraded model capability, necessitating judicious sample selection. For fixed total sample sizes, calibration sets comprising 70–80% of samples yielded more reliable predictions. In conclusion, transflectance sensors demonstrate superior compatibility with multicomponent fermentation systems. Combined with wavelength selection, moving average preprocessing, and rational sample removal and partitioning strategies, this approach provides an effective solution for NIR-based online glucose monitoring. Full article

In the context of research aimed at identifying the causes of the progressive decline in cellular and tissue functions characteristic of aging, in recent decades, increasing attention has been devoted to the sirtuin family. Sirtuins are named after the Sir2 protein of Saccharomyces cerevisiae, a product of the SIR gene family, known as “silent information regulator 2”. Sirtuins are NAD⁺-dependent protein deacetylases and deacylases characterized by a conserved catalytic domain of approximately 275 amino acids. The removal of acetyl groups from acetyl-lysine residues on proteins is critical in regulating a wide range of biological functions, including gene silencing, genome stability, longevity, metabolism, and cellular physiology. In humans, the sirtuin family comprises seven isoforms (SIRT1–SIRT7), each with specific substrate preferences and primarily, but not exclusively, localized in the nucleus (SIRT1, SIRT6, and SIRT7), cytoplasm (SIRT2), and mitochondria (SIRT3, SIRT4, and SIRT5). Sirtuins may regulate numerous cellular processes associated with survival and longevity, including transcription and DNA repair, inflammation, glucose and lipid metabolism, oxidative stress, mitochondrial function, apoptosis, autophagy, and stress resistance. Sirtuins’ dependence on NAD⁺ allows them to function as cellular energy sensors, linking metabolic demands to selective lysine deacylation in various subcellular organelles. The aim of this review is to provide an update on this family of molecules, describing their molecular structures, physiological functions, roles in aging processes, and potential to be modulated to serve as a strategy for promoting healthy aging. Full article

This systematic review examines how gait analysis has been applied to understand, detect, and manage diabetes and its complications, with a focus on wearable sensor technologies and computational methods. A total of 30 studies were identified from IEEE Xplore, Scopus, and Google Scholar databases using systematic search and screening processes. Data extraction followed a structured framework addressing research questions on gait applications, technologies, and associated parameters. Results indicate that wearable sensor technologies, coupled with advanced computational modeling and machine learning, can capture meaningful gait alterations associated with long-term metabolic dysregulation and neuropathic changes. Applications range from diabetic neuropathy detection and foot ulcer prevention to intervention evaluation and early biomarker identification. The review highlights current progress and outlines future directions toward predictive gait analytics that may serve as indirect, secondary markers of metabolic status and improve diabetes care outcomes. Furthermore, this synthesis provides evidence for integrating wearable gait assessment into diabetes management protocols, potentially enabling early detection of complications, personalized intervention strategies, and non-invasive monitoring approaches that complement traditional glucose measurements. Full article

In₂O₃ has high electron mobility, strong affinity for oxidizing gases, and abundant tunable surface oxygen species. These features enable efficient charge transfer during ozone adsorption, making In₂O₃ a promising ozone-sensing material. However, conventional In₂O₃-based gas sensors still suffer from insufficient sensitivity at low ozone concentrations and slow response/recovery rates, limiting their performance for high-precision gas detection. In this study, morphology-controlled In₂O₃ nanorods were synthesized via a glucose-assisted hydrothermal method, enabling coordinated regulation of the material structure and surface properties. Compared with conventional In₂O₃ nanocubes, the glucose-modulated In₂O₃ nanorods exhibited an approximately sevenfold increase in response toward 1 ppm O₃, indicating markedly improved capability for detecting low-concentration ozone. In addition, the sensor demonstrated a relatively low detection limit of about 80 ppb and fast response/recovery behavior (108 s/238 s). This strategy improves gas sensing performance through morphology optimization, increased surface active sites, and enhanced electron transport, offering a feasible materials design route for high-performance ozone gas sensors and showing potential for real-time environmental ozone monitoring and related applications. Full article

This work presents a compact microwave sensor for noninvasive blood glucose monitoring based on a substrate-integrated waveguide loaded with a complementary split-ring resonator on RO4350. The sensing principle uses shifts in resonance frequency and changes in S-parameters to track the dielectric dispersion of glucose-containing tissue. The resonator is constructed using Substrate-Integrated Waveguide (SIW) technology, which mimics the propagation characteristics of a conventional rectangular waveguide. To validate its versatility, the sensor implements three practical sample delivery modes: direct liquid contact with the sensing surface, a glass tube holder mounted over the active region, and a non-invasive fingertip interface. Electromagnetic simulations and benchtop measurements confirm clear glucose-dependent frequency shifts with stable matching and insertion levels. Across the physiological range of 20 to 200 mg·dL⁻¹, the sensor exhibits clear glucose-dependent resonance shifts in all configurations. In direct contact mode, the resonance frequency shifts from 10.83 GHz to 10.45 GHz with sensitivities up to 2.47 MHz per mg·dL⁻¹. The tube configuration shows a shift from 10.49 GHz to 10.38 GHz with sensitivity up to 0.80 MHz per mg·dL⁻¹, while reducing contamination. In the non-invasive fingertip mode, the resonance shifts from 2.56 GHz to 2.52 GHz with sensitivities up to 0.25 MHz per mg·dL⁻¹. These results confirm the sensor’s compactness, reliability, and suitability for portable, low-cost glucose monitoring. The results indicate that the proposed sensor can support practical continuous or spot monitoring and offers a clear path toward portable and low-cost glucose assessment. Full article

This paper provides a feasibility study of a non-invasive microwave-based glucose-sensing system based on a small printed slot antenna with etched step-impedance resonators (SIRs) on an FR4 substrate in the ground plane at approximately 5.7 GHz. The sensor proposed takes advantage of the effect of the antenna resonant frequency and reflection coefficient (S11) perturbation due to the dielectric loading of a human finger placed in the antenna near field. Instead of declaring direct glucose specificity, this paper is dedicated to understand whether the measures of RF can be translated to the invasive glucose values under the condition of controlled positioning. A vector network analyzer was used to measure the experimental values where resonant frequency and S11 magnitude were obtained at the point of peak sensitivity due to fixed finger placement at the point. These RF properties were associated with invasively measured glucose values using three modeling methods: a simple analytical linear formula, a second-degree Polynomial Ridge regression model, and a Random Forest machine learning model. The comparative analysis has established that nonlinear data-driven models outperform the analytical formulations significantly with the highest predictive accuracy being the Random Forest model (R² = 0.72, RMSE = 10.57 mg/dL, MAE = 5.16 mg/dL). The findings affirm that the impacts of antenna loading control the raw measurements, but the trend related to glucose can be extracted upon machine learning calibration under controlled conditions. The research provides a methodological framework of RF-based non-invasive glucose sensing and the need to employ various phantom-based validation, sub-subject-based modeling, or clinically based evaluation metrics in future studies. Full article

The flexible foam piezoresistive sensor demonstrates significant potential for wearable strain-sensing applications due to its substantial deformation capacity, excellent flexibility, and cost effectiveness. However, conventional flexible foam piezoresistive sensors often struggle to simultaneously achieve high sensitivity, a wide pressure detection range, fast response and long-term stability. This paper employed a glucose-based sugar-templating method to fabricate a fine-pore (50 μm) foam structure complemented by a dual-filler strategy to enhance overall performance. A robust porous conductive network was constructed by embedding zinc oxide (ZnO) and multi-walled carbon nanotubes (MWCNTs) into a polydimethylsiloxane (PDMS) matrix. The resulting sensor exhibits outstanding piezoresistive properties, featuring a wide linear detection range (0–80% strain) and a high sensitivity of 9.02 kPa⁻¹ within the 0–10 kPa pressure range. It demonstrates rapid response/recovery times of 50/70 ms and maintains stable output performance even after 5000 compression cycles at 300 kPa. The sensor also exhibits negligible environmental interference and excellent long-term stability. When attached to finger joints, feet soles, or the throat, the sensor enables functions such as finger bending recognition, race-walking violation discrimination, gait analysis, and vocal fold vibration recognition, thereby demonstrating its considerable potential for application in human–computer interaction and human motion detection. Full article

Wireless Body Area Networks (WBANs) enable real-time health monitoring essential for timely clinical intervention, yet their performance is frequently hindered by sensor degradation, noise interference, and strict low-latency constraints in resource-limited environments. Conventional preprocessing approaches indiscriminately reprocess all incoming data, including uncorrupted samples, thereby increasing computational overhead, introducing latency, and potentially distorting valid physiological trends. This study introduces a unified real-time monitoring framework tailored for WBAN systems. The key contributions include: (1) an adaptively gated multi-stage preprocessing pipeline that selectively restores corrupted samples while preserving clean data, (2) an overlap-aware sliding-window mechanism enabling low-latency operation, and (3) a clinically informed risk assessment strategy for early-warning support. By avoiding unnecessary modification of intact signals, the framework maintains physiological integrity while substantially improving reconstruction and predictive reliability. Across multiple vital signs, the proposed approach achieves substantial reconstruction gains, with Mean Squared Error (MSE) reductions ranging from 53% to 67% under strong degradation conditions. An adaptive ARIMA-based forecasting layer captures short-term physiological dynamics with directional accuracies of approximately 65–70% for one-step (10 s) ahead prediction. Early-warning behavior is intentionally conservative, prioritizing false alarm suppression over aggressive alerting. Per-signal evaluation reveals high sensitivity for blood pressure signals, whereas glucose and certain high-variability modalities exhibit conservative sensitivity under modality-specific thresholds. Importantly, the aggregated multi-modal risk decision achieves strong overall system-level performance, with sensitivity and specificity of 0.89 and 0.92, respectively. Overall, the proposed framework establishes a robust, low-latency, and computationally efficient foundation for dependable physiological monitoring in WBAN environments, leveraging selective processing to optimize both resource utilization and clinical reliability. Full article

Although natural enzymes have a high catalytic activity as biocatalysts, they still face many limitations in practical applications, including high preparation and purification costs, poor environmental stability, and difficulties in recovery and reuse. Nanozymes are a class of synthetic nanomaterials with enzymatic catalytic properties. They are regarded as promising alternatives to natural enzymes due to their low cost, good stability, adjustable catalytic activity, and easy surface modification. Among many nanozyme materials, metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) have attracted much attention due to their high specific surface area, adjustable porosity, and stable framework structure. This review summarizes the latest research progress of nanozymes based on MOFs and COFs and reveals the catalytic properties of different enzymes (oxidase, peroxidase, catalase, glucose oxidase, superoxide dismutase, hydrolase) simulated by them. In addition, their potential applications in sensors and medical fields are discussed. Finally, this review discusses the current challenges and developments of organic framework-based nanozymes and provides suggestions for future research directions. Full article

Background/Objectives: Effective glycemic management from the time of diagnosis is essential in the care of children and adolescents with type 1 diabetes (T1D), as early glycemic patterns can influence long-term health outcomes. Methods: This exploratory study evaluated a one-month interactive, group- and play-based education program designed to enhance food and carbohydrate counting skills among families of children and adolescents with newly diagnosed (ND) T1D (<1 year since diagnosis) or suboptimal glycemic control (SGC) (hemoglobin A1c (HbA1c) > 7.5% (58 mmol/mol)). The intervention included hands-on learning activities in food and carbohydrate counting, and peer interaction to support development of diabetes self-management skills. Data were collected at baseline, post-intervention, and at six-months follow-up through medical records, glucose sensor data, and a questionnaire assessing diabetes self-management skills, dietary practices, and carbohydrate counting. Results: Between September 2022 and April 2024, 55 children and adolescents were enrolled in the ND group and 22 in the SGC group. Post-intervention, carbohydrate counting skills improved, particularly in the ND group. Participants reported greater confidence and independence in carbohydrate counting and insulin dosing, with parents noting sustained benefits at six-months follow-up. No significant changes were observed in glycemic control, including time-in-range and postprandial glucose profiles. Conclusions: In this exploratory study, early interactive and play-based group education was associated with improvements in carbohydrate counting skills and self-care confidence in children and adolescents with newly diagnosed T1D. These improvements were not accompanied by changes in glycemic outcomes. The findings occurred during a complex and transitional phase following diagnosis. Further research is needed to examine sustainability and long-term clinical impact. Full article

Background: Streptococcus pneumonia is the primary etiological agent of community-acquired pneumonia (CAP). Pneumococci promote severe lung injury through the release of virulence factors, including pneumolysin (PLY). Obesity/diabetes increases pneumonia-associated mortality, but the mechanisms remain elusive. We found that obese db/db mice have increased pulmonary barrier disruption to PLY. Previously we showed that upregulation of NOX1 in endothelial cells (EC) of db/db mice drives endothelial dysfunction, but a role for NOX1 in PLY-induced lung injury, especially in diabetic conditions, has not yet been described. Results: Increased NOX1 in lung ECs dose-dependently increased superoxide and EC barrier disruption (p < 0.05). Even at low activity levels, NOX1 greatly potentiated PLY-induced EC barrier disruption, whereas loss of NOX1 activity, either pharmacological or genetic, reduced barrier disruption (p < 0.05). Blockade of calcium entry protected the EC barrier from combined PLY and NOX1, indicating a key role for calcium. Hyperglycemia amplified PLY-enduced EC barrier disruption and intracellular calcium and these effects were mitigated by NOX1 inhibition and silencing (p < 0.05). NOX1-enhanced calcium entry was reduced by knockout of calcium sensor STIM1, and PLY-induced barrier disruption was reduced by STIM1 inhibition. Levels of STIM1, Orai1, TRPV4, or TRPC4 were unchanged by HG, but TRPC1 significantly increased (p < 0.05). NOX1 and HG promoted increased STIM1 and TRPC1 binding, and silencing TRPC1 ameliorated PLY-induced barrier disruption (p < 0.05). Increased calcium promoted mitochondrial permeability transition pore (MPTP) opening and PPIF inhibition protected EC barrier function (p < 0.05). Conclusions: These results suggest that elevated glucose levels in obesity primes EC barrier disruption by amplifying PLY-induced calcium influx via a novel NOX1, STIM1, TRPC1 and MPTP signaling axis. Full article