Search Results (8,712)

In this study, a hybrid sensor based on a defective square-truncated patch antenna (STPA) and a frequency-selective surface (FSS) was analyzed numerically and experimentally for different glucose–distilled water solutions. Here, an FSS was employed to enhance the sensitivity of the hybrid sensor. The sensing principle relies on monitoring variations in the loss tangent ($tan\delta$) and relative permittivity (${\epsilon }_r$) caused by different glucose concentrations applied to the sample under test (SUT). An open-ended coaxial probe was used to measure the complex permittivity of the solutions, which was then fitted to the Debye relaxation model. The simulated and experimental results of the novel sensor showed good agreement in a glucose concentration monitoring application. The sensor spanned the glucose range from 0 mg/dL to 5000 mg/dL, exhibiting a sensitivity of 55.44 kHz/mgdL⁻¹ and a figure of merit (FOM) of 6.23 × ${10}^{-4}$ (1/mgdL⁻¹) in the experiments and 53.60 kHz/mgdL⁻¹ and 1.71 × ${10}^{-4}$ (1/mgdL⁻¹) FOM in the simulations. When solutions with different concentrations were tested in the SUT, the resonance frequency of the antenna ($f_0$, in GHz) changed. To further characterize the sensor response, the relationship between the glucose concentration (C, in mg/dL) and $f_0$ was examined. A regression-based prediction model was constructed to map the measured scattering parameters to the glucose concentration, yielding a coefficient of determination ($R^2$) of 0.976. The high sensitivity, compact size, and compatibility with planar fabrication suggest that the proposed hybrid sensor has the potential to contribute to the development of non-invasive glucose-monitoring systems. Full article

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes. Furthermore, we formalize the biological governance mechanisms, demonstrating that, unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency, energy budgets, and safety-case hooks that support certification and adoption across autonomous domains. Full article

Physiological monitoring has become an inherently interdisciplinary field, merging advances in engineering, chemistry, biology, medicine, and data analytics to create sensors that continuously track the vital signals of the body. These developments are enabling more personalized and preventive healthcare, as wearable (bio)sensors and intelligent algorithms can detect subtle physiological changes in real-time. In the Special Issue ‘Advances in (Bio)Sensors for Physiological Monitoring’, researchers from diverse domains contributed 18 papers showcasing cutting-edge sensor technologies and applications for health and performance monitoring. In this review, we summarize these contributions by grouping them into logical themes based on their focus: (1) cardiovascular and autonomic monitoring, (2) glucose and metabolic monitoring, (3) wearable sensors for movement and musculoskeletal health, (4) neurophysiological monitoring and brain–computer interfaces, and (5) innovations in sensor technology and methods. This thematic organization highlights the breadth of the research, spanning from fundamental sensor hardware to data-driven analytics, and underscores how modern (bio)sensors are breaking traditional boundaries in healthcare. Full article

Yeast biosensors represent a promising biotechnological innovation for ensuring the safety and quality of fermented beverages such as beer, wine, and kombucha. These biosensors employ genetically engineered yeast strains to detect specific contaminants, spoilage organisms, or hazardous compounds during fermentation or the final product. By integrating synthetic biology tools, researchers have developed yeast strains that can sense and respond to the presence of heavy metals (e.g., lead or arsenic), mycotoxins, ethanol levels, or unwanted microbial metabolites. When a target compound is detected, the biosensor yeast activates a reporter system, such as fluorescence, color change, or electrical signal, providing a rapid, visible, and cost-effective means of monitoring safety parameters. These biosensors offer several advantages: they can operate in real time, are relatively low-cost compared to conventional chemical analysis methods, and can be integrated directly into the fermentation system. Furthermore, as Saccharomyces cerevisiae is generally recognized as safe (GRAS), its use as a sensing platform aligns well with existing practices in beverage production. Yeast biosensors are being investigated for the early detection of contamination by spoilage microbes, such as Brettanomyces and lactic acid bacteria. These contaminants can alter the flavor profile and shorten the product’s shelf life. By providing timely feedback, these biosensor systems allow producers to intervene early, thereby reducing waste and enhancing consumer safety. In this work, we review the development and application of yeast-based biosensors as potential safeguards in fermented beverage production, with the overarching goal of contributing to the manufacture of safer and higher-quality products. Nevertheless, despite their substantial conceptual promise and encouraging experimental results, yeast biosensors remain confined mainly to laboratory-scale studies. A clear gap persists between their demonstrated potential and widespread industrial implementation, underscoring the need for further research focused on robustness, scalability, and regulatory integration. Full article

Background/Objectives: Kidney cancer (RC) is a significant global health burden. Renal cell carcinoma (RCC) is the most common form of kidney cancer. Its predominant histological subtype is clear cell renal cell carcinoma (ccRCC), which is frequently diagnosed at an advanced local stage or with metastases. Detecting cancer at an early stage significantly increases the likelihood of a cure; therefore, research on new markers and a thorough understanding of tumor biology are essential. This study investigated the significance of aromatase (ARO), cathepsin S (CTSS), and matrix metalloproteinase 1 (MMP-1) as potential biomarkers in ccRCC. Methods: ARO, CTSS, and MMP-1 concentrations in plasma were determined using SPRi biosensors. Appropriate antibodies were used as biorecognition molecules in the biosensors. The samples analyzed came from 60 patients with histopathologically confirmed clear cell renal cell carcinoma (ccRCC) and from 26 patients diagnosed with chronic cystitis or benign prostatic hyperplasia (BPH). Results: A statistically significant increase (p < 0.00001) in the concentration of all proteins compared with the control samples was observed at the T3–T4 stage. The ARO concentration was already statistically significantly higher at the T1–T2 stage (p < 0.00001). The ROC curve for aromatase demonstrated high sensitivity and specificity for detecting ccRCC, with a cut-off point of 7.53 ng mL⁻¹. A moderate positive correlation was also found between the concentrations of the three tested substances in renal cancer, which may indicate potential interactions in the tumor’s pathogenesis. Conclusions: SPRI testing has been shown to be an alternative to standard methods for detecting potential ccRCC markers. The biosensors used in the study can simultaneously determine ARO, CTSS, and MMP-1. The results obtained suggest the potential importance of these proteins in the development of ccRCC, and our work proposes a new diagnostic technique that may aid in the diagnosis of ccRCC. Full article

Microparticle detection technology uses materials that can specifically recognize complex biostructures, such as antibodies and aptamers, as trapping agents. The development of antibody production technology and simplification of sensing signal output methods have facilitated commercialization of disposable biosensors, making rapid diagnosis possible. Although this contributed to the early resolution of pandemics, traditional biosensors face issues with sensitivity, durability, and rapid response times. We aimed to fabricate microspaces using metallic materials to further enhance durability of mold fabrication technologies, such as molecular imprinting. Low-damage metal deposition was performed on target protozoa and Norovirus-like particles (NoV-LPs) to produce thin metallic films that adhere to the material. The procedure for fitting the object into the bio structured space formed on the thin metal film took less than a minute, and sensitivity was 10 fg/mL for NoV-LPs. Furthermore, because it was a metal film, no decrease in reactivity was observed even when the same substrate was stored at room temperature and reused repeatedly after fabrication. These findings underscore the potential of integrating stable metallic structures with bio-recognition elements to significantly enhance robustness and reliability of environmental monitoring. This contributes to public health strategies aimed at early detection and containment of infectious diseases. Full article

Paper-based rapid tests are widely used in point-of-care diagnostics due to their simplicity and low cost. However, their application in quantitative analysis remains limited. In this work, a nucleic acid lateral flow assay (NALFA) was integrated with an automated image acquisition system built on a Raspberry Pi platform for the quantitative detection of SARS-CoV-2 virus, increasing the accuracy of the test compared to subjective visual interpretation. The assay employed blue polystyrene microspheres as reporters, while automated image capturing, image processing and quantification were performed using custom Python software (version 3.12). Signal quantification was achieved by comparing the grayscale intensity of the test line with that of a simultaneously captured negative control strip, allowing correction for illumination and background variability. Calibration curves were used for the training of the algorithm. The system was applied for the analysis of a series of samples with varying DNA concentrations, yielding recoveries between 84 and 108%. The proposed approach integrates a simple biosensor with an accessible computational platform to achieve full low-cost automation. This work introduces the first Raspberry Pi-driven image processing approach for accurate quantification of NALFAs and establishes a foundation for future low-cost, portable diagnostic systems targeting diverse nucleic acids, proteins, and biomarkers. Full article

Ionic liquids (ILs) have emerged as multifunctional compounds with low volatility, high thermal stability, and tunable solvation capabilities, making them highly promising for biomedical applications. First explored in the late 1990s and early 2000s for enhancing the thermal stability of enzymes, antimicrobial agents, and controlled release systems, ILs have since gained significant attention in drug delivery, antimicrobial treatments, medical imaging, and biosensing. This review examines the diverse functions of ILs in contemporary therapeutics and diagnostics, highlighting their transformative capabilities in improving drug solubility, bioavailability, transdermal permeability, and pathogen inactivation. In drug delivery, ILs improve solubility of bioactive compounds, with several IL formulations achieving substantial solubility enhancements for poorly soluble drugs. Bio-ILs, in particular, show promise in enhancing drug delivery systems, such as improving transdermal permeability. ILs also exhibit significant antimicrobial and antiviral activity, offering new avenues for combating resistant pathogens. Despite their broad potential, challenges such as cytotoxicity, long-term metabolic effects, and the stability of ILs in physiological conditions persist. While much research has focused on their physicochemical properties, biological activity and in vivo studies are still underexplored. The future directions for ILs in biomedical applications include the development of bioengineered ILs and hybrid ILs, combining functional components like nanoparticles and polymers to create multifunctional materials. These ILs, derived from renewable resources, show great promise in personalized medicine and clinical applications. Further research is necessary to evaluate their pharmacokinetics, biodistribution, and long-term safety to fully realize their biomedical potential. This study emphasizes the potential of ILs to transform therapeutic and diagnostic technologies by highlighting present shortcomings and offering pathways for clinical translation, while also debating the need for continuous research to fully utilize their biomedical capabilities. Full article

The increasing demand for rapid identification of bacteria capable of degrading environmentally relevant organic compounds highlights the need for scalable and selective analytical tools. Cupriavidus necator catabolizes several hydroxybenzoic acids, including 2-hydroxybenzoate (salicylate, 2-HBA), 4-hydroxybenzoate (4-HBA), and 3-hydroxybenzoate (3-HBA), funneling them into central aromatic catabolism via monooxygenation to 2,5-dihydroxybenzoate (gentisate, 2,5-dHBA) and 3,4-dihydroxybenzoate (protocatechuate, 3,4-dHBA) followed by the oxidative cleavage reaction, enabling complete conversion to tricarboxylic acid (TCA) cycle intermediates. To quantify how readily C. necator is able to activate catabolic genes in response to hydroxybenzoic acid, an extracellular ligand, we applied an approach centered on a transcription-factor (TF)-based biosensor that combines ligand-bound regulator activity with a fluorescent reporter. This approach allowed to evaluate the ligand sensitivity by determining gene activation threshold AC_min and half-maximal effective concentration EC₅₀. Amongst studied hydroxybenzoic acids, 2-HBA and 4-HBA sensors from C. necator showed very low thresholds 4.8 and 2.4 μM and EC₅₀ values of 19.91 and 13.06 μM, indicating high sensitivity to these compounds and implicating a scavenging characteristic of associated catabolism. This study shows that the TF-based-biosensor approach applied for mapping functional sensing ranges of hydroxybenzoates combined with the research and informatics of catabolism can advance our understanding of how gene expression regulation systems have evolved to respond differentially to the availability and concentration of carbon sources. Furthermore, it can inform metabolic engineering strategies in the prevention of premature pathway activation or in predicting competitive substrate hierarchies in complex mixed environments. Full article

This comprehensive review synthesizes current advances and persistent challenges in integrating worker safety and food safety through human-robot collaboration (HRC) in poultry processing. Rapid industry expansion and rising consumer demand for ready-to-eat poultry products have heightened occupational risks and foodborne contamination concerns, necessitating holistic safety strategies. The review examines ergonomic, microbiological, and regulatory risks specific to poultry lines, and maps how state-of-the-art collaborative robots (“cobots”)—including power and force-limiting arms, adaptive soft grippers, machine vision, and biosensor integration—can support safer, more hygienic, and more productive operations. The authors analyze technical scientific literature (2018–2025) and real-world case studies, highlighting how automation (e.g., vision-guided deboning and intelligent sanitation) can reduce repetitive strain injuries, lower contamination rates, and improve production consistency. The review also addresses the psychological and sociocultural dimensions that affect workforce acceptance, as well as economic and regulatory barriers to adoption, particularly in small- and mid-sized plants. Key research gaps include gripper adaptability, validation of food safety outcomes in mixed human-cobot workflows, and the need for deeper workforce retraining and feedback mechanisms. The authors propose a multidisciplinary roadmap: harmonizing ergonomic, safety, and hygiene standards; developing adaptive food-grade robotic end-effectors; fostering explainable AI for process transparency; and advancing workforce education programs. Ultimately, successful HRC deployment in poultry processing will depend on continuous collaboration among industry, researchers, and regulatory authorities to ensure both safety and competitiveness in a rapidly evolving global food system. Full article

Aquaculture is a crucial global food production sector that faces challenges in water quality management, food safety, and stress-related health concerns in aquatic species. Cortisol, a key stress biomarker in fish, and Escherichia coli (E. coli) contamination in bivalve mollusks are critical indicators that require sensitive and real-time detection methods. Liquid crystal (LC)-based immunosensors have emerged as a promising solution for detecting biological analytes due to their high sensitivity, rapid response, and label-free optical detection capabilities. Therefore, this study explores the development and application of LC-based immunosensors for the detection of cortisol in artificial and real recirculating aquaculture system (RAS) samples, as well as E. coli in real contaminated water and clam samples during the depuration processes of bivalve mollusks. The biosensors exhibited the capacity to detect cortisol with a response time in seconds and a limit of detection (LOD) of 0.1 ng/mL. Furthermore, they demonstrated specificity to cortisol when tested against different interfering substances, including testosterone, glucose, and cholesterol. Furthermore, it was possible to correlate cortisol concentrations in different filtration stages and track E. coli contamination during depuration. The results confirm the feasibility of LC-based immunosensors as a user-friendly, portable, and efficient diagnostic tool for aquaculture applications. Full article

Foodborne pathogens remain a major global concern, demanding rapid, accessible, and determination technologies. Conventional methods, such as culture assays and polymerase chain reaction, offer high accuracy but are time-consuming for on-site testing. This study presents a portable, smartphone-assisted dual-mode biosensor that combines colorimetric and photothermal speckle imaging for improved sensitivity in lateral flow assays (LFAs). The prototype device, built using low-cost components ($500), uses a Raspberry Pi for illumination control, image acquisition, and machine learning-based signal analysis. Colorimetric features were derived from normalized RGB intensities, while photothermal responses were obtained from speckle fluctuation metrics during periodic plasmonic heating. Multivariate linear regression, with and without LASSO regularization, was used to predict Salmonella concentrations. The comparison revealed that regularization did not significantly improve predictive accuracy indicating that the unregularized linear model is sufficient and that the extracted features are robust without complex penalization. The fused model achieved the best performance (R² = 0.91) and consistently predicted concentrations down to a limit of detection (LOD) of 10⁴ CFU/mL, which is one order of magnitude improvement of visual and benchtop measurements from previous work. Blind testing confirmed robustness but also revealed difficulty distinguishing between negative and 10³ CFU/mL samples. This work demonstrates a low-cost, field-deployable biosensing platform capable of quantitative pathogen detection, establishing a foundation for the future deployment of smartphone-assisted, machine learning-enabled diagnostic tools for broader monitoring applications. Full article

Magnetotactic bacteria (MTB) have attracted interest in recent years, mainly due to their natural ability to form intracellular magnetic nanocrystals with potential for biomedical and environmental applications. In this study, we focused on the morphological analysis of the Paramagnetospirillum magnetotacticum MS-1 strain, trying to keep the bacteria as close to their natural state as possible. An important element of this work is the use of untreated bacterial cells, without conductive coating or chemical fixation, using a simple and low-cost support. This choice was made intentionally to avoid changes induced by metallization and to allow direct observation of characteristics that may be relevant in applications where the interaction of the bacteria with the environment plays an important role, such as biosensors. In addition, the analysis was performed on a bacterial suspension stored for approximately 10 months at 4 °C to assess whether the morphology specific to the MS-1 strain is maintained over time. The obtained results show that the general cell morphology and magnetosome organization can be clearly and reproducibly observed even after long-term storage. Without attempting to replace studies based on conventional sample preparation methods, this work provides a complementary perspective and suggests that magnetotactic bacteria may represent a natural and effective alternative to synthetic magnetic nanoparticles, with potential applications in the biomedical and environmental fields. Full article

Background: Greater trochanteric pain syndrome (GTPS) is associated with structural tendon alterations and functional impairment. Extracorporeal shockwave therapy (ESWT) is a common treatment, but objective monitoring of tendon remodeling and motor recovery remains limited. Objective: This study aimed to integrate shear-wave elastography (SWE) expressed in m/s and wearable inertial measurement unit (IMU) as biosensing tools for the quantitative assessment of tendon elasticity, morphology, and hip motion after ESWT in GTPS. Methods: In a prospective cohort of adults with chronic GTPS, shear wave elastography (SWE) quantified gluteus medius tendon (GMT) elasticity and thickness, while hip abduction range of motion (ROM) was measured using a triaxial inertial measurement unit. Clinical scores on the Visual Analogue Scale (VAS), Harris Hip Score (HHS), Low Extremity Functional Scale (LEFS), and Roles and Maudsley score (RM) were collected at baseline (T0) and at 6 months (T1). Results: Thirty-five patients completed follow-up. Pain and function improved significantly (VAS, HHS, LEFS, RM; all p < 0.05). SWE values of the affected GMT increased, while tendon thickness decreased yet remained greater than on the contralateral side. Hip abduction ROM increased significantly from T0 to T1 (p < 0.05). Correlation analysis showed a negative association between abduction and pain at T1 (r = −0.424; p = 0.011) and, at baseline, between abduction and VAS (r = −0.428; p = 0.010) and RM (r = −0.346; p = 0.042), and a positive association with LEFS (r = 0.366; p = 0.031). SWE correlated negatively with VAS at T1 (r = −0.600; p < 0.05) and positively with HHS at T1 (r = 0.400; p < 0.05). Conclusions: Integrating elastography with inertial sensor-based motion analysis provides complementary, quantitative insights into tendon remodeling and functional recovery after ESWT in GTPS. These findings support combined imaging and wearable motion measures to monitor treatment response over time. Full article

Staphylococcus aureus is a common opportunistic pathogen found in various environments, with the potential for rapid spread, especially in densely populated indoor settings. Integrating traditional microbiological monitoring with molecular techniques is critical for the timely detection and control of such pathogens. The aim of this study was (1) to monitor the presence and spread of S. aureus in a crowded occupational environment and (2) to optimize a PCR protocol with sequence specific primers (PCR-SSP) for precise identification and early detection of this microorganism and its antibiotic resistance genes. Sampling was conducted in two different places: a call center and a healthcare facility room. All samples were collected from indoor areas at two different time points (T0 and T1) in May 2025 (mean temperature: 22.5 °C; humidity: 59.5%). Microbiological techniques and molecular analysis using PCR-SSP were employed to confirm the presence of S. aureus and detect antibiotic resistance genes such as mecA. A total CFU (colony-forming unit) count of 587 was recorded at the dental clinic corridor, and a total CFU count of 2008 was recorded at the call center corridor. PCR-SSP successfully confirmed the identity of S. aureus with an amplicon size 267 bp and enabled the detection of antibiotic resistance markers, validating its use as a complementary method to traditional microbiological techniques. This study highlights the importance of combining environmental monitoring with molecular biology tools to enhance the early detection and accurate identification of microbial pathogens such as S. aureus and provide an insight for our future direction of producing biosensors for digital air monitoring in crowded workplaces. Full article