Search Results (212)

Access to safe water remains a vital public health challenge, especially in low- and middle-income countries like Colombia, where untreated sources lead to severe diarrheal diseases in children under five. Escherichia coli (E. coli), a key indicator of fecal contamination, is often detected using culture-based methods that are time-consuming and rely on specialized infrastructure. To overcome these limitations, we developed an aptamer-based isolation system targeting environmental E. coli. Aptamers were obtained using a Cell-SELEX protocol, and after six enrichment rounds, two candidates—APT-EC-1 and its truncated version APT-EC-MUT—were synthesized and attached to carboxyl-functionalized magnetic nanoparticles (MNP-COOH). Both complexes demonstrated a strong binding affinity and high specificity, successfully isolating E. coli from environmental and ATCC reference strains in the laboratory. Sensitivity tests detected E. coli at dilutions up to 1:10,000, showing reliable performance. In early in-field testing with environmental water samples, APT-EC-1 consistently identified E. coli colonies, while APT-EC-MUT struggled with low bacterial levels, illustrating performance differences. These findings demonstrate the promise of aptamer-functionalized MNPs as the basis for quick, affordable, and portable biosensors for water quality testing, especially in resource-scarce areas. Future efforts will add colorimetric or electrochemical readouts to allow real-time, on-site detection of fecal contamination. Full article

The growing global demand for rapid, sensitive, and cost-effective food safety monitoring has driven the development of nanozyme-based biosensors as alternatives to natural enzyme-based methods. Among various nanozymes, bimetallic gold–platinum (AuPt) nanozymes show superior catalytic performance compared to monometallic and other Au-based bimetallic hybrids. This is due to their synergistic colorimetric, catalytic, geometric, and ensemble properties. This review systematically evaluates AuPt nanozymes in food safety applications, focusing on their synthesis, structural design, and practical uses. Various structural types are highlighted, including plain, magnetic, porous nanomaterial-labeled, and flexible nanomaterial-loaded AuPt hybrids. Key synthesis methods such as seed-mediated growth and one-pot procedures with different reducing agents are summarized. Detection modes covered include colorimetric, electrochemical, and multimodal sensing, demonstrating efficient detection of important food contaminants. Key innovations include core–shell designs for enhanced catalytic activity, new synthesis strategies for improved structural control, and combined detection modes to increase reliability and reduce false positives. Challenges and future opportunities are discussed, such as standardizing synthesis protocols, scaling up production, and integration with advanced sensing platforms. This review aims to accelerate the translation of AuPt nanozyme technology into practical food safety monitoring solutions that improve food security and public health. Full article

Matrix metalloproteinases (MMPs) are a class of extracellular Zn²⁺ peptidases involved in various physiological and pathological processes. These enzymes serve as excellent biomarkers for diagnosing various diseases, including cancer and periodontitis, to name a few. MMP levels also serve as a prognostic marker, which helps determine how much the disease has progressed. However, the current methods used to detect MMPs need a large sample volume, carry a high cost, and are not widely accessible to the public due to these challenges. Biosensing techniques tackle these problems by providing an efficient, cost-effective sensor with great sensitivity. This review provides a comprehensive overview of the latest developments and advancements in detecting MMPs using biosensors that employ various detection mechanisms such as electrochemical, colorimetric, and fluorescence methods. Furthermore, we have discussed the challenges and prospects of using MMPs as diagnostic tools. Full article

Optical biosensors have emerged as a transformative technology for food safety monitoring. These devices combine biorecognition molecules with advanced optical transducers, enabling the detection of a wide array of food contaminants, including pathogens, toxins, pesticides, and antibiotic residues. This review comprehensively explores the principles, advancements, applications, and future trends of optical biosensors in ensuring food safety. The key advantages of optical biosensors, such as high sensitivity to trace contaminants, fast response times, and portability, make them an attractive alternative to traditional analytical methods. Types of optical biosensors discussed include surface plasmon resonance (SPR), interferometric, fluorescence and chemiluminescence, and colorimetric biosensors. SPR biosensors stand out for their real-time, label-free analysis of foodborne pathogens and contaminants, while fluorescence and chemiluminescence biosensors offer exceptional sensitivity for detecting low levels of toxins. Interferometric and colorimetric biosensors, characterized by their portability and visual signal output, are well-suited for field-based applications. Biosensors have proven invaluable in monitoring heavy metals, pesticide residues, and antibiotic contaminants, ensuring compliance with stringent food safety standards. The integration of nanotechnology has further enhanced the performance of optical biosensors, with nanomaterials such as quantum dots and nanoparticles enabling ultra-sensitive detection and signal amplification. Optical biosensors represent a vital advancement in the field of food safety, addressing critical public health concerns through their rapid and reliable detection capabilities. Continued interdisciplinary efforts in nanotechnology, material science, and device engineering are poised to further expand their applications, making them indispensable tools for safeguarding global food supply chains. Full article

Considering the necessity of food safety testing, various biosensors have been developed based on biological elements (e.g., antibodies, aptamers), chemical elements (e.g., molecularly imprinted polymers), physical elements (e.g., nanopores) as recognition substances. According to the sensing patterns of signal transduction, the biosensors could be classified into optical and electrochemical biosensing, including fluorescence sensing, Raman sensing, colorimetric sensing, electrochemical sensing, etc. To enhance the sensing sensitivity, kinds of nanomaterials have been applied for signal amplification. With merits of high selectivity, sensitivity, and accuracy, the sensing strategies have been widely applied for food safety testing. This review highlights their signal output behavior, (e.g., fluorescence intensity shifts, Raman peak alterations, colorimetric changes, electrochemical current/voltage/impedance variations), nanostructure-mediated amplification mechanisms, and the fundamental recognition principles. Future efforts should prioritize multiplexed assay platforms, integration with microfluidics and smart devices, novel biorecognition elements, and sustainable manufacturing. Emerging synergies between biosensors and AI-driven data analytics promise intelligent monitoring systems for predictive food safety management, addressing challenges in food matrix compatibility and real-time hazard identification. Full article

Climate change increases microbial contamination risks in food, highlighting the need for real-time biosensors. However, food residues often interfere with detection signals, limiting the direct application. An integrated system of filter-assisted sample preparation (FASP) and an immunoassay-based colorimetric biosensor offers the rapid and simple on-site detection of foodborne pathogens in complex food matrices. The accuracy and stability of biosensor analysis were ensured via filter-assisted preprocessing, which separated food residues from bacteria. The system was applied to various food matrices, including vegetables, meats, and cheese brine, using samples spiked at contamination levels ranging from 10² to 10³ CFU per 25 g, thereby demonstrating broad applicability. Bacterial recovery varied by food matrix, with vegetables showing a 1-log reduction and meats, melon, and cheese brine showing a 2-log reduction relative to the initial inoculum. A detection limit of 10¹ CFU/mL was achieved for Escherichia coli O157:H7, Salmonella Typhimurium, and Listeria monocytogenes in the final preprocessed sample solutions. Sample preparation took under 3 min, and detection was completed within 2 h under stationary conditions. This approach enables rapid pathogen detection in various food matrices without the need for special reading devices, contributing to food safety as a real-time, rapid-response food biosensor. Full article

Rapid and accurate bacterial detection is essential in medicine, the food industry, and environmental monitoring. This work presents the development of an optical sensor based on color changes in the culture medium that leverages the optical interaction of bacterial metabolic products. The proposed prototype operates on the principle of optical transmittance through mannitol salt agar (ASM), a selective medium for Staphylococcus aureus. As bacterial growth progresses, the medium undergoes changes in thickness and, primarily, color, which is optically measurable at specific wavelengths depending on the type of illumination provided by the simplified light-emitting diodes (LEDs). The sensor demonstrated the ability to detect bacterial growth in approximately 90–120 min, offering a significant reduction in detection time compared to traditional incubation methods. The system is characterized by its simplicity, sensitivity, low reagent consumption (up to 140 fewer reagents per test), and potential for real-time monitoring. These findings support the viability of the proposed sensor as an efficient alternative for early pathogen detection in both clinical and industrial applications. Finally, a proposal for simplifying the sensor in a system composed of a light-emitting diode and a light-dependent resistor is presented. Full article

Metal–organic frameworks (MOFs) have emerged as highly versatile materials for the development of next-generation optical biosensors owing to their tunable porosity, large surface area, and customizable chemical functionality. Recently, MOF-based platforms have shown substantial potential in various optical transduction modalities, including fluorescence, luminescence, and colorimetric sensing, enabling the highly sensitive and selective detection of biological analytes. This review provides a comprehensive overview of recent advancements in MOF-based optical biosensors, focusing on their applications in pathogen detection and environmental monitoring. We highlight key design strategies, including MOF functionalization, hybridization with nanoparticles or dyes, and integration into microfluidic and wearable devices. Emerging methods, such as point-of-care diagnostics, label-free detection, and real-time monitoring, are also discussed. Finally, the current challenges and future directions for the practical deployment of MOF-based optical biosensors in clinical and field environments are discussed. Full article

Thanks to their low-cost, portability, and sustainability, microfluidic thread-based analytical devices (μTADs) are emerging as an attractive analytical platform for wearable biosensing. While several μTADs, mainly based on colorimetric and electrochemical detection methods, have been developed, achieving the needed sensitivity and accuracy for these biosensors continues to present a significant challenge. Prompted by this need we investigated for the first time the implementation of chemiluminescence (CL) as a detection technique for μTADs. Exploiting the lactate oxidase-catalyzed reaction coupled with the enhanced luminol/H₂O₂/horseradish peroxidase CL system, we developed a cotton-thread-based chemiluminescent device enabling the detection of lactate with a limit of detection of 0.25 mM in a 2 µL volume of artificial sweat at pH 6.5 within 3 min. The use of recycled grape skin as support made the device sustainable, while the smartphone detection allowed a simple and quantitative readout for the end-user. Using a smartphone as a detector, the analytical performance was evaluated in different conditions and in the presence of potential interferents, showing suitability for monitoring lactate levels in physiological conditions, such as for monitoring anaerobic thresholds in endurance training. Full article

Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this study introduces a proof-of-concept platform—using CA19-9 as a model biomarker—that integrates naturally derived melanin nanoparticles (MNPs) with machine learning-based image analysis to enable environmentally sustainable and analytically robust colorimetric quantification. Upon target binding, MNPs induce a concentration-dependent color transition from yellow to brown. This visual signal was quantified using a machine learning pipeline incorporating automated region segmentation and regression modeling. Sensor areas were segmented using three different algorithms, with the U-Net model achieving the highest accuracy (average IoU: 0.9025 ± 0.0392). Features extracted from segmented regions were used to train seven regression models, among which XGBoost performed best, yielding a Mean Absolute Percentage Error (MAPE) of 17%. Although reduced sensitivity was observed at higher analyte concentrations due to sensor saturation, the model showed strong predictive accuracy at lower concentrations, which are especially challenging for visual interpretation. This approach enables accurate, reproducible, and objective quantification of colorimetric signals, thereby offering a sustainable and scalable alternative for point-of-care diagnostic applications. Full article

Rapid screening of foodborne pathogens is critical for food safety, yet current detection techniques often suffer from low efficiency and complexity. In this study, we developed a sliding microfluidic colorimetric biosensor for the fast, sensitive, and multiplex detection of Salmonella. First, the target bacteria were specifically captured by antibody-functionalized magnetic nanoparticles in the microfluidic chip, forming magnetic bead–bacteria complexes. Then, through motor-assisted sliding of the chip, manganese dioxide (MnO₂) nanoflowers conjugated with secondary antibodies were introduced to bind the captured bacteria, generating a dual-antibody sandwich structure. Finally, a second sliding step brought the complexes into contact with a chromogenic substrate, where the MnO₂ nanoflowers catalyzed a colorimetric reaction, and the resulting signal was used to quantify the Salmonella concentration. Under optimized conditions, the biosensor achieved a detection limit of 10 CFU/mL within 20 min. In spiked pork samples, the average recovery rate of Salmonella ranged from 94.9% to 125.4%, with a coefficient of variation between 4.0% and 6.8%. By integrating mixing, separation, washing, catalysis, and detection into a single chip, this microfluidic biosensor offers a user-friendly, time-efficient, and highly sensitive platform, showing great potential for the on-site detection of foodborne pathogens. Full article

Salivary α-amylase, primarily encoded by the AMY1 gene, initiates the enzymatic digestion of dietary starch in the oral cavity and has recently emerged as a potential biomarker in metabolic research. Variability in salivary amylase activity (SAA), driven largely by copy number variation of AMY1, has been associated with postprandial glycemic responses, insulin secretion dynamics, and susceptibility to obesity. This review critically examines current analytical approaches for quantifying SAA, including enzymatic assays, colorimetric techniques, immunoassays, and emerging biosensor technologies. The methodological limitations related to sample handling, intra-individual variability, assay standardization, and specificity are highlighted in the context of metabolic and clinical studies. Furthermore, the review explores the physiological relevance of SAA in energy homeostasis and its associations with visceral adiposity and insulin resistance. We discuss the potential integration of SAA measurements into obesity risk stratification and personalized dietary interventions, particularly in individuals with altered starch metabolism. Finally, the review identifies key research gaps and future directions necessary to validate SAA as a reliable metabolic biomarker in clinical practice. Understanding the diagnostic and prognostic value of salivary amylase may offer new insights into the prevention and management of obesity and related metabolic disorders. Full article

Biosensors play a central role in the early detection of abnormal glucose levels in individuals with diabetes; therefore, the development of less invasive systems is essential. Herein, a 3D-printed colorimetric biosensor combining microneedles and chitosan nanoparticles was developed for glucose detection in sweat using machine learning. Briefly, hollow 3D-printed polylactic acid microneedles were constructed and loaded with chitosan nanoparticles encapsulating glucose oxidase, horseradish peroxidase, and the chromogenic substrate 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), resulting in the formation of the chitosan nanoparticle−microneedle patches. Glucose detection was performed colorimetrically by first incubating the chitosan nanoparticle−microneedle patches with glucose samples of varying concentrations and then by using photographs of the top side of each microneedle and a color recognition application on a smartphone. The Random Sample Consensus algorithm was used to train a simple linear regression model to predict glucose concentrations in unknown samples. The developed biosensor system exhibited a good linear response range toward glucose (0.025−0.375 mM), a low limit of detection (0.023 mM), a limit of quantification (0.078 mM), high specificity, and recovery rates ranging between 86–112%. Lastly, the biosensor was applied to glucose detection in spiked artificial sweat samples, confirming the potential of the proposed methodology for glucose detection in real samples. Full article

The rapid and accurate detection of pathogenic bacteria and viruses is critical for infectious disease control and public health protection. While conventional methods (e.g., culture, microscopy, serology, and PCR) are widely used, they are often limited by lengthy processing times, high costs, and specialized equipment requirements. In recent years, metal nanocluster (MNC)-based biosensors have emerged as powerful diagnostic platforms due to their unique optical, catalytic, and electrochemical properties. This systematic review comprehensively surveys advancements in MNC-based biosensors for bacterial and viral pathogen detection, focusing on optical (colorimetric and fluorescence) and electrochemical platforms. Three key aspects are emphasized: (1) detection mechanisms, (2) nanocluster types and properties, and (3) applications in clinical diagnostics, environmental monitoring, and food safety. The literature demonstrates that MNC-based biosensors provide high sensitivity, specificity, portability, and cost-efficiency. Moreover, the integration of nanotechnology with biosensing platforms enables real-time and point-of-care diagnostics. This review also discusses the limitations and future directions of the technology, emphasizing the need for enhanced stability, multiplex detection capability, and clinical validation. The findings offer valuable insights for developing next-generation biosensors with improved functionality and broader applicability in microbial diagnostics. Full article