Metal-Nanoparticle-Supported Nanozyme-Based Colorimetric Sensor Array for Precise Identification of Proteins and Oral Bacteria

Multifunctional nanozymatic biosensors: Awareness, regulation and pathogenic bacteria detection

It is estimated that approximately 700,000 fatalities occur annually due to infections attributed to various pathogens, which are capable of dissemination via multiple environmental vectors, including air, water, and soil. Consequently, there is an urgent need to enhance and refine rapid detection technologies for pathogens to prevent and control the spread of associated diseases. This review focuses on applying nanozymes in constructing biosensors, particularly their advancement in detecting pathogenic bacteria. Nanozymes, which are nanomaterials exhibiting enzyme-like activity, combine unique magnetic, optical, and electronic properties with structural diversity. This blend of characteristics makes them highly appealing for use in biocatalytic applications. Moreover, their nanoscale dimensions facilitate effective contact with pathogenic bacteria, leading to efficient detection and antibacterial effects. This article briefly summarizes the development, classification, and strategies for regulating the catalytic activity of nanozymes. It primarily focuses on recent advancements in constructing biosensors that utilize nanozymes as probes for sensitively detecting pathogenic bacteria. The discussion covers the development of various optical and electrochemical biosensors, including colorimetric, fluorescence, surface-enhanced Raman scattering (SERS), and electrochemical methods. These approaches provide a reliable solution for the sensitive detection of pathogenic bacteria. Finally, the challenges and future development directions of nanozymes in pathogen detection are discussed.

Self-supplying of hydrogen peroxide nanozyme-based colorimetric sensing array as electronic tongue for biothiol detection and disease discrimination

Developing a highly reliably and sensitive nanozyme-based colorimetric sensor array for biothiols analysis is critical owing to they play an essential role in diagnosing disease. The required procedure of introducing hydrogen peroxide (H2O2) directly into the colorimetric reaction systems in traditional biothiols array analysis limits its applicability due to its poor stability and inhibition in biomolecular activity by using the high-concentration H2O2. Herein, we carried out a "green" and convenient approach to propose for the biothiol detection and disease discrimination through nanozymes-based colorimetric sensor technique without adding the high-concentration H2O2 for the first time. The copper peroxide nanodots (CPNs) and graphene oxide (GO) modified CPNs (GO@CPNs) are as sensing units to release H2O2 and Cu2+ under acidic conditions, which triggered a Fenton-like reaction, generating hydroxyl radical (•OH) to oxidize 3,3',5,5'-tetramethylbenzidine (TMB) accompanied by a change in TMB color from colorless to blue. Due to the synergistic effect of Cu2+ and GO, GO@CPNs showed increased the activity of peroxidase-like compared to CPNs. Therefore, the catalytic abilities of nanozymes-based colorimetric sensing array were inhibited to different degrees by different biothiols (i.e., glutathione (GSH), cysteine (Cys) and homocysteine (Hcy)) with a detection limit of 50 nM, which could be precisely distinguished by using pattern recognition method. Besides, the detection of a single biothiol at different concentrations and mixtures of biothiols has also been achieved. Moreover, the real biological samples (cells and human serum) can be accurately discriminated through array method, which demonstrated its potential application of medical diagnosis.

Identification and Quantification of Multiple Pathogenic Escherichia coli Strains Based on a Plasmonic Sensor Array

Pathogenic Escherichia coli (E. coli) is a widespread and clinically significant foodborne pathogen. Due to its high mutation rates and phenotypic diversity, rapid identification of its subtypes remains challenging and prone to false positives when detecting single strains. In this study, we developed a plasmonic sensor array with high-dimensional signal readouts (ζ-potential, dynamic light-scattering (DLS), surface-enhanced Raman scattering (SERS), and ultraviolet-visible (UV-vis) absorption spectra) for the selective discrimination of pathogenic E. coli, integrated with bacterial culture methods. The plasmonic sensor units demonstrated strong encoding capabilities, facilitating the differentiation of subtle variations among various E. coli strains and showing excellent anti-interference performance. The array realized different pathogenic E. coli strains, bacterial mixture identification, and even quantitative detection. Remarkably, the working concentration for the sensor array was notably low, at 104 CFU/mL. Finally, by incorporating bacterial isolation culture, the designed sensor array obtained 100% accuracy in detecting E. coli in real food samples. These findings highlight the sensor array's potential for applications in food safety monitoring and clinical diagnostics, offering a sensitive, rapid, and reliable tool for pathogen detection in complex samples.

Preparation of fluorescent sensor array based on internal self-polymerizing imprinted Janus nanosheets and its application in determination of multiple metal ions and bisphenol A

Internal self-imprinting is an ingenious way to achieve the combination of imprinting and Pickering emulsions. Herein, internal self-polymerizing imprinted Janus nanosheets for detecting bisphenol A (BPA) and multiple metal ions are reported. Janus composite hollow spheres were prepared by grafting an imprinted polymer onto vinyl groups after an emulsion interfacial self-organized sol-gel process. After crushing, the external surface is further modified with thiol groups (-SH) and fluorescence signal indicators. Janus silica nanosheets (J-MIPs/SH@QDs) with bispecific artificial receptors (-SH and molecularly imprinted sites) were designed to specifically identify BPA and 4 metal ions (Hg2+, Cu2+, Cr3+, Ag+). CdTe QDs with red and yellow emissions were incorporated into the J-MIPs/SH@QDs as fluorescent signal indicators. Due to the presence of molecularly imprinted sites, BPA can be recognized with high specificity, resulting in increased fluorescence intensity. The thiol groups and metal ions formed a chelated structure with the fluorescence intensity decreasing. Linear discriminant analysis (LDA) in SPSS software can be used to analyze these specific fluorescence responses to distinguish BPA and multiple metal ions. In conclusion, J-MIPs/SH@QDs exhibit sensitivity in multi-organic-inorganic analyte combinations. Its desegregation of innovative bispecific receptors creates a multitude of opportunities for the specific and effective detection of coexisting contaminants.

Nanozyme Colorimetric Sensor Array Based on Bimetal-Organic Framework MIL-53 (Fe, Mn) for the Discrimination of Biological Antioxidants

Biological antioxidants play a crucial role in numerous disease-related biological processes, which has led to significant interest in their identification. In this research, a colorimetric sensor array was created. It uses MIL-53 (Fe, Mn) nanozymes to simultaneously detect multiple antioxidants. The bimetallic nanozymes, MIL-53(Fe, Mn), were synthesized by using a solvent-thermal method. This framework demonstrated notable oxidase-like catalytic ability. In particular, it facilitates the conversion of colorless TMB to a blue compound under different pH conditions. When antioxidants were introduced into the reaction system, the sensor array exhibited cross-reactivity, resulting in distinct color signals. Linear discriminant analysis (LDA) was successfully utilized to precisely identify the different concentrations of cysteine (CYS), glutathione (GSH), dopamine (DA), and ascorbic acid (AA) and their combinations. This method enabled accurate determination of the amounts of these substances both individually and in mixtures, providing a reliable way to analyze their concentrations in an experimental context. Furthermore, the prepared sensor array combined with the smartphone successfully distinguished the target antioxidants in the serum. The proposed sensing method would be expected to have practical applications in diagnosis.

Fluorescent Fingerprint Identification of Protein Structural Changes and Disease-Specific Amyloid Beta Aggregates Based on a Single-Nanozyme Sensor Array

The misfolding of amyloid β (Aβ) peptides into an aggregation state is a central hallmark of the onset of Alzheimer's disease (AD). However, conventional methods are mainly focused on detecting a specific Aβ peptide, which makes it difficult to recognize multiple analytes with different topological features and unfolded states at the same time. Here, we propose a simple and universal sensing strategy to construct a fluorescence sensor array by using a single-nanozyme probe combined with three fluorescent substrates as three recognition units to probe the protein structural changes and identify between multiple Aβ assemblies. In this sensor system, the fingerprint-like patterns are produced from the nonspecific interactions between topological proteins and the sensing units. As a result, this sensor array can accurately identify 13 kinds of proteins and their mixtures at different ratios. Moreover, the sensor array can discriminate against proteins with unfolded states and diverse conformational forms. Most importantly, the sensor array successfully distinguishes between multiple Aβ species, even in artificial cerebrospinal fluid samples and human serum samples. This work provides an attractive and reliable strategy for predicting pathologically relevant proteins and clinical diagnosis of AD.

Machine Learning Assisted Nanofluidic Array for Multiprotein Detection

Solid-state nanopore and nanochannel biosensors have revolutionized protein detection by offering label-free, highly sensitive analyses. Traditional sensing systems (1st and 2nd stages) primarily focus on inner wall (IW) interactions, facing challenges such as complex preparation processes, variable protein entry angles, and conformational changes, leading to irregular detection events. To address these limitations, recent advancements (3rd stage) have shifted toward outer surface (OS) functionalization but are constrained by single-protein recognition models. Herein, we show a machine learning assisted nanofluidic array (MANY) sensing system (4th stage) that integrates a supervised dimensionality reduction strategy with photoresponsive MoS2 nanofluidic array functionalized with nonspecific functional elements (FEarray) at the OS. This approach serves as a proof-of-concept for label-free, probe-free detection of multiple proteins with 100% accuracy, highlighting its significant potential for rapid diagnostics in future disease detection applications.

Nanozyme-Enabled Biomedical Diagnosis: Advances, Trends, and Challenges

As nanoscale materials with the function of catalyzing substrates through enzymatic kinetics, nanozymes are regarded as potential alternatives to natural enzymes. Compared to protein-based enzymes, nanozymes exhibit attractive characteristics of low preparation cost, robust activity, flexible performance adjustment, and versatile functionalization. These advantages endow them with wide use from biochemical sensing and environmental remediation to medical theranostics. Especially in biomedical diagnosis, the feature of catalytic signal amplification provided by nanozymes makes them function as emerging labels for the detection of biomarkers and diseases, with rapid developments observed in recent years. To provide a comprehensive overview of recent progress made in this dynamic field, here an overview of biomedical diagnosis enabled by nanozymes is provided. This review first summarizes the synthesis of nanozyme materials and then discusses the main strategies applied to enhance their catalytic activity and specificity. Subsequently, representative utilization of nanozymes combined with biological elements in disease diagnosis is reviewed, including the detection of biomarkers related to metabolic, cardiovascular, nervous, and digestive diseases as well as cancers. Finally, some development trends in nanozyme-enabled biomedical diagnosis are highlighted, and corresponding challenges are also pointed out, aiming to inspire future efforts to further advance this promising field.

"Three-in-one" Analysis of Proteinuria for Disease Diagnosis through Multifunctional Nanoparticles and Machine Learning

Urinalysis is one of the predominant tools for clinical testing owing to the abundant composition, sufficient volume, and non-invasive acquisition of urine. As a critical component of routine urinalysis, urine protein testing measures the levels and types of proteins, enabling the early diagnosis of diseases. Traditional methods require three separate steps including strip testing, protein/creatinine ratio measurement, and electrophoresis respectively to achieve qualitative, quantitative, and classification analyses of proteins in urine with long time and cumbersome operations. Herein, this work demonstrates a "three-in-one" protocol to analyze the urine composition by combining multifunctional nanoparticles with machine learning. This work constructs a sensor array to analyze proteinuria by employing nanoparticles with unique optical properties, outstanding catalytic activity, diverse composition, and tunable structure as probes. Different proteins interacted with nanoprobes differently and are classified by this sensor array based on their physicochemical heterogeneities. With the aid of machine learning, the urine composition is precisely detected for the diagnosis of bladder cancer. This protocol enables quantification and classification of 5 proteinuria in 10 min without any tedious pretreatment, showing proimise for the comprehensive analysis of body fluid for early disease diagnosis.

Biomaterials for reliable wearable health monitoring: Applications in skin and eye integration

Recent advancements in biomaterials have significantly impacted wearable health monitoring, creating opportunities for personalized and non-invasive health assessments. These developments address the growing demand for customized healthcare solutions. Durability is a critical factor for biomaterials in wearable applications, as they must withstand diverse wearing conditions effectively. Therefore, there is a heightened focus on developing biomaterials that maintain robust and stable functionalities, essential for advancing wearable sensing technologies. This review examines the biomaterials used in wearable sensors, specifically those interfaced with human skin and eyes, highlighting essential strategies for achieving long-lasting and stable performance. We specifically discuss three main categories of biomaterials-hydrogels, fibers, and hybrid materials-each offering distinct properties ideal for use in durable wearable health monitoring systems. Moreover, we delve into the latest advancements in biomaterial-based sensors, which hold the potential to facilitate early disease detection, preventative interventions, and tailored healthcare approaches. We also address ongoing challenges and suggest future directions for research on material-based wearable sensors to encourage continuous innovation in this dynamic field.

An ultrasensitive colorimetric/fluorescent/photothermal sensing platform for the detection of D-amino acids based on carbon dots nanozymes with enhanced peroxidase-like activity

Based on the enhanced peroxidase-like activity of carbon dots nanozymes (CDszymes), with a specific oxidation reaction of D-amino acid oxidase catalysing the formation of H2O2 from D-amino acid, an ultrasensitive sensing platform, was constructed for the quantitative detection of D-amino acids in saliva. With the increase of D-amino acids concentration, the blue color of catalytic product gradually deepend, the fluorescence CDszymes gradually quenched, and the temperature gradually increased. Using D-alanine as D-amino acid models, the detection limits of D-alanine in colorimetric/photothermal/fluorescent mode were 0.3 μM, 1.8 μM, and 0.04 μM, respectively. The proposed detection platform exhibits promising application potential in clinical diagnostics. The exceptional sensing performance can be attributed to the utilization of CDszymes with outstanding POD activity. Revolving around the blind synthesis of CDszymes exhibiting high-efficiency POD activity, this study delved into the underlying mechanism governing the regulation of the POD activity of CDszymes by precursor functional groups. This work investigates the valence band theory to enhance the peroxidase-like of CDszymes, thereby offering a rational approach for designing CDszymes.

Smartphone-assisted colorimetric sensor arrays based on nanozymes for high throughput identification of heavy metal ions in salmon

The rapid, precise, and high-throughput identification of multiple heavy metals ions holds immense importance in ensuring food safety and promoting public health. This study presents a novel smartphone-assisted colorimetric sensor array for the rapid and precise detection of multiple heavy metals ions. The sensor array is based on three signal recognition elements (AuPt@Fe-N-C, AuPt@N-C, and Fe-N-C) and the presence of different heavy metal ions affects the nanozymes-chromogenic substrate (TMB) catalytic color production, enabling the differentiation and quantification of various heavy metal ions. Combined with a smartphone-based RGB mode, the colorimetric sensor array can successfully identify five different heavy metal ions (Hg2+, Pb2+, Co2+, Cr6+, and Fe3+) as low as 0.5 μM and different ratios of binary and ternary mixed heavy metal ions in just 5 min. The sensor array successfully tested seawater and salmon samples with a total heavy metal content of 10 μM in the South China Sea (Haikou and Wenchang). Overall, this study highlights the potential of smartphone-assisted colorimetric sensor arrays for the rapid and precise detection of multiple heavy metal ions, which could significantly contribute to food safety and public health monitoring.

Sensitive colorimetric detection of Escherichia coli in milk using Au@Ag core-shell nanoparticles

Escherichia coli (E. coli) is a prevalent pathogen that is frequently associated with the foodborne illness. It causes various infections and poses a significant threat to human health. A rapid and sensitive assay for detecting E. coli is essential for timely diagnosis. Herein, a simple and sensitive colorimetric analysis method for detecting E. coli was developed based on the formation of Au@Ag core-shell nanoparticles facilitated by p-benzoquinone (BQ). E. coli reduced p-benzoquinone to generate hydroquinone (HQ), which could reduce the added Tollens' reagent to silver elemental and grow on the surface of gold nanoparticles (AuNPs). As the E. coli concentration increased, the silver layer thickess on the AuNPs surface growed, resulting in a stronger silver absorption peak observed at 390 nm. The color of the solution changed from red to orange, which could be used to detect E. coli by the naked eye. As a result, E. coli was detected with a linear range from 1.0 × 101 to 1.0 × 107 CFU/mL based on the absorbance intensity. In addition, this method accurately detected E. coli in real milk sample, demonstrating promising applications in foodborne pathogen detection. With satisfactory accuracy, the proposed colorimetric method holds excellent prospects in detecting pathogens in actual food samples.

A rapid and accurate fluorescent sensor array based on lanthanide metal-organic framework for identification and determination of perfluorinated compounds

Perfluorinated compounds (PFCs), as an important class of environmental pollutants, have chemical and structural similarities that make their detection a great technical challenge. This study synthesized three species of metal-organic frameworks (MOFs) using different lanthanide metal ions or organic ligands, which were integrated into a fluorescent sensor array. This innovative approach offers a straightforward, rapid, and precise detection strategy for PFCs. Different ionization properties and fluorinated hydrophobic tails of PFCs lead to different electrostatic attraction and hydrophobic effects between PFCs and sensing elements, which become the basis for differential sensing. Furthermore, the fluorescence signal is more convenient to collect, making the sensor array simple to complete the identification. Combined with pattern recognition methods, the array successfully identified seven kinds of PFCs and mixtures with a classification accuracy of 100 % and a detection limit as low as 51 nM. Finally, the utility of the sensor array in river water sample analysis was verified. The strategy provides an effective method for identifying and determining PFCs and offers new opportunities for developing sensor arrays based on lanthanide MOFs.

Advances in machine learning-enhanced nanozymes

Nanozymes, synthetic nanomaterials that mimic the catalytic functions of natural enzymes, have emerged as transformative technologies for biosensing, diagnostics, and environmental monitoring. Since their introduction, nanozymes have rapidly evolved with significant advancements in their design and applications, particularly through the integration of machine learning (ML). Machine learning (ML) has optimized nanozyme efficiency by predicting ideal size, shape, and surface chemistry, reducing experimental time and resources. This review explores the rapid advancements in nanozyme technology, highlighting the role of ML in improving performance across various bioapplications, including real-time monitoring and the development of chemiluminescent, electrochemical and colorimetric sensors. We discuss the evolution of different types of nanozymes, their catalytic mechanisms, and the impact of ML on their property optimization. Furthermore, this review addresses challenges related to data quality, scalability, and standardization, while highlighting future directions for ML-driven nanozyme development. By examining recent innovations, this review highlights the potential of combining nanozymes with ML to drive the development of next-generation diagnostic and detection technologies.

Cu2O nanoparticles with morphology-dependent peroxidase mimic activity: a novel colorimetric biosensor for deoxynivalenol detection

Different morphological Cu2O nanoparticles including cube, truncated cube, and octahedron were successfully prepared by a selective surface stabilization strategy. The prepared cube Cu2O exhibited superior peroxidase-like activity over the other two morphological Cu2O nanoparticles, which can readily oxidize 3,3',5,5'-tetramethylbenzidine (TMB) to form visually recognizable color signals. Consequently, a sensitive and simple colorimetric biosensor was proposed for deoxynivalenol (DON) detection. In this biosensor, the uniform cube Cu2O was employed as the vehicle to label the antibody for the recognition of immunoreaction. The sensing strategy showed a detection limit as low as 0.01 ng/mL, and a wide linear range from 2 to 100 ng/mL. Concurrently, the approximate DON concentration can be immediately and conveniently observed by the vivid color changes. Benefiting from the high sensitivity and selectivity of the designed biosensor, the detection of DON in wheat, corn, and tap water samples was achieved, suggesting the bright prospect of the biosensor for the convenient and intuitive detection of DON in actual samples.

Accelerated and precise identification of antioxidants and pesticides using a smartphone-based colorimetric sensor array

The integration of smartphones with conventional analytical approaches plays a crucial role in enhancing on-site detection platforms for point-of-care testing. Here, we developed a simple, rapid, and efficient three-channel colorimetric sensor array, leveraging the peroxidase (POD)-like activity of polydopamine-decorated FeNi foam (PDFeNi foam), to identify antioxidants using both microplate readers and smartphones for signal readouts. The exceptional catalytic capacity of PDFeNi foam enabled the quick catalytic oxidation of three typical peroxidase substrates (TMB, OPD and 4-AT) within 3 min. Consequently, we constructed a colorimetric sensor array with cross-reactive responses, which was successfully applied to differentiate five antioxidants (i.e., glycine (GLY), glutathione (GSH), citric acid (CA), ascorbic acid (AA), and tannic acid (TAN)) within the concentration range of 0.1-10 μM, quantitatively analyze individual antioxidants (with AA and CA as model analytes), and assess binary mixtures of AA and GSH. The practical application was further validated by discriminating antioxidants in serum samples with a smartphone for signal readout. In addition, since pesticides could be absorbed on the surface of PDFeNi foam through π-π stacking and hydrogen bonding, the active sites were differentially masked, leading to featured modulation on POD-like activity of PDFeNi foam, thereby forming the basis for pesticides discrimination on the sensor array. The nanozyme-based sensor array provides a simple, rapid, visual and high-throughput strategy for precise identification of various analytes with a versatile platform, highlighting its potential application in point-care-of diagnostic, food safety and environmental surveillance.

Ionic liquid-enhanced silica aerogels for the specific extraction and detection of aflatoxin B1 coupled with a smartphone-based colorimetric biosensor

The polymer ionic liquid (1-allyl-3-butylimidazolium bromide) enhanced silica aerogel was modified onto the surface of stainless-steel mesh to immobilize aptamer-1 for the specific recognition of AFB1. The porous channels of silica aerogel could prevent the interference of macromolecules in food samples. Enzyme kinetic analysis showed that the MoS2/Au was an effective peroxidase mimic with a relatively low Michaelis constant (Km) value of 0.17 mM and a high catalytic rate of 3.87 × 10-8 mol (L·s)-1, which exhibited obvious superiority compared with horseradish peroxidase. The established "sandwich-structure" biosensor was coupled with the smartphone "Color Picker" application was used to detect AFB1 with a wide linear range (1-100 ng mL-1) and low detection limit (0.25 ng mL-1). The anti-interference ability of the established biosensor was evaluated by adding different concentrations of standards in corn, peanut, and wheat and matrix effects were 90.84-106.11 %. The results showed that this method demonstrated high specificity, sensitivity, rapidity and low interference in food samples.

Colorimetric sensing for translational applications: from colorants to mechanisms

Colorimetric sensing offers instant reporting via visible signals. Versus labor-intensive and instrument-dependent detection methods, colorimetric sensors present advantages including short acquisition time, high throughput screening, low cost, portability, and a user-friendly approach. These advantages have driven substantial growth in colorimetric sensors, particularly in point-of-care (POC) diagnostics. Rapid progress in nanotechnology, materials science, microfluidics technology, biomarker discovery, digital technology, and signal pattern analysis has led to a variety of colorimetric reagents and detection mechanisms, which are fundamental to advance colorimetric sensing applications. This review first summarizes the basic components (e.g., color reagents, recognition interactions, and sampling procedures) in the design of a colorimetric sensing system. It then presents the rationale design and typical examples of POC devices, e.g., lateral flow devices, microfluidic paper-based analytical devices, and wearable sensing devices. Two highlighted colorimetric formats are discussed: combinational and activatable systems based on the sensor-array and lock-and-key mechanisms, respectively. Case discussions in colorimetric assays are organized by the analyte identities. Finally, the review presents challenges and perspectives for the design and development of colorimetric detection schemes as well as applications. The goal of this review is to provide a foundational resource for developing colorimetric systems and underscoring the colorants and mechanisms that facilitate the continuing evolution of POC sensors.

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

Magnetic Immunoassay Based on Au Pt Bimetallic Nanoparticles/Carbon Nanotube Hybrids for Sensitive Detection of Tetracycline Antibiotics

Misusage of tetracycline (TC) antibiotics residue in animal food has posed a significant threat to human health. Therefore, there is an urgent need to develop highly sensitive and robust assays for detecting TC. In the current study, gold and platinum nanoparticles were deposited on carbon nanotubes (CNTs) through the superposition method (Au@Pt/CNTs-s) and one-pot method (Au@Pt/CNTs-o). Au@Pt/CNTs-s displayed higher enzyme-like activity than Au@Pt/CNTs-o, which were utilized for the development of sensitive magnetic immunoassays. Under the optimized conditions, the limits of detection (LODs) of magnetic immunoassays assisted by Au@Pt/CNTs-s and Au@Pt/CNTs-o against TCs could reach 0.74 ng/mL and 1.74 ng/m, respectively, which were improved 6-fold and 2.5-fold in comparison with conventional magnetic immunoassay. In addition, the measurement of TC-family antibiotics was implemented by this assay, and ascribed to the antibody used that could recognize TC, oxytetracycline, chlortetracycline, and doxycycline with high cross-reactivity. Furthermore, the method showed good accuracy (recoveries, 92.1-114.5% for milk; 88.6-92.4% for pork samples), which also were applied for determination of the targets in real samples. This study provides novel insights into the rapid detection of targets based on high-performance nanocatalysts.

Iridium nanoparticles supported on polyaniline nanotubes for peroxidase mimicking towards total antioxidant capacity assay of fruits and vegetables

In this study, a straightforward approach is presented for the first time to anchor Ir nanoparticles on the surface of uniform polyaniline (PANi) nanotubes (NTs), which can be used as an efficient peroxidase (POD)-like catalyst. The morphology and chemical structure of the PANi-Ir nanocomposite are characterized by scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), X-ray diffractometer (XRD), Raman and X-ray photoelectron spectroscopy (XPS) measurements. Owing to the strong interaction between Ir nanoparticles and PANi, a remarkable catalytic enhancement is achieved compared to the bare Ir black catalyst and individual PANi NTs, dominating withan electron transfer mechanism. Furthermore, an efficient colorimetric sensor for ascorbic acid (AA) is developed with a low detection limit of 1.0 μM (S/N = 3), and a total antioxidant capacity (TAC) sensing platform is also constructed for the rigorous detection and analysis of fruits and vegetables.

Nanotechnology improves the detection of bacteria: Recent advances and future perspectives

Nanotechnology has advanced significantly, particularly in biomedicine, showing promise for nanomaterial applications. Bacterial infections pose persistent public health challenges due to the lack of rapid pathogen detection methods, resulting in antibiotic overuse and bacterial resistance, threatening the human microbiome. Nanotechnology offers a solution through nanoparticle-based materials facilitating early bacterial detection and combating resistance. This study explores recent research on nanoparticle development for controlling microbial infections using various nanotechnology-driven detection methods. These approaches include Surface Plasmon Resonance (SPR) Sensors, Surface-Enhanced Raman Scattering (SERS) Sensors, Optoelectronic-based sensors, Bacteriophage-Based Sensors, and nanotechnology-based aptasensors. These technologies provide precise bacteria detection, enabling targeted treatment and infection prevention. Integrating nanoparticles into detection approaches holds promise for enhancing patient outcomes and mitigating harmful bacteria spread in healthcare settings.

Construction and Application of Nanozyme Sensor Arrays

Compared with traditional "lock-key mode" biosensors, a sensor array consists of a series of sensing elements based on intermolecular interactions (typically hydrogen bonds, van der Waals forces, and electrostatic interactions). At the same time, sensor arrays also have the advantages of fast response, high sensitivity, low energy consumption, low cost, rich output signals, and imageability, which have attracted widespread attention from researchers. Nanozymes are nanomaterials which own enzyme-like properties. Because of the adjustable activity, high stability, and cost effectiveness of nanozymes, they are potential candidates for construction of sensor arrays to output different signals from analytes through the chemoresponse of colorants, which solves the shortcomings of traditional sensors that they cannot support multiple detection and lack universality. Recently, a sensor array based on nanozymes as nonspecific recognition receptors has attracted much more attention from researchers and has been applied to precise recognition of proteins, bacteria, and heavy metals. In this perspective, attention is given to nanozymes and the regulation of their enzyme-like activity. Particularly, the building principles and methods for sensor arrays based on nanozymes are analyzed, and the applications are summarized. Finally, the approaches to overcome the challenges and perspectives are also presented and analyzed for facilitating further research and development of nanozyme sensor arrays. This perspective should be helpful for gaining insight into research ideas within the field of nanozyme sensor arrays.

Surface-Engineered Nanomaterials for Optical Array Based Sensing

Array based sensing governed by optical methods provides fast and economic way for detection of wide variety of analytes where the ideality of detection processes depends on the sensor element's versatile mode of interaction with multiple analytes in an unbiased manner. This can be achieved by either the receptor unit having multiple recognition moiety, or their surface property should possess tuning ability upon fabrication called surface engineering. Nanomaterials have a high surface to volume ratio, making them viable candidates for molecule recognition through surface adsorption phenomena, which makes it ideal to meet the above requirements. Most crucially, by engineering a nanomaterial's surface, one may produce cross-reactive responses for a variety of analytes while focusing solely on a single nanomaterial. Depending on the nature of receptor elements, in the last decade the array-based sensing has been considering as multimodal detection platform which operates through various pathway including single channel, multichannel, binding and indicator displacement assay, sequential ON-OFF sensing, enzyme amplified and nanozyme based sensing etc. In this review we will deliver the working principle for Array-based sensing by using various nanomaterials like nanoparticles, nanosheets, nanodots and self-assembled nanomaterials and their surface functionality for suitable molecular recognition.

MoS2-Based Sensor Array for Accurate Identification of Cancer Cells with Ensemble-Modified Aptamers

In this work, we present an array-based chemical nose sensor that utilizes a set of ensemble-modified aptamer (EMAmer) probes to sense subtle physicochemical changes on the cell surface for cancer cell identification. The EMAmer probes are engineered by domain-selective incorporation of different types and/or copies of positively charged functional groups into DNA scaffolds, and their differential interactions with cancer cells can be transduced through competitive adsorption of fluorophore-labeled EMAmer probes loaded on MoS2 nanosheets. We demonstrate that this MoS2-EMAmer-based sensor array enables rapid and effective discrimination among six types of cancer cells and their mixtures with a concentration of 104 cells within 60 min, achieving a 94.4% accuracy in identifying blinded unknown cell samples. The established MoS2-EMAmer sensing platform is anticipated to show significant promise in the advancement of cancer diagnostics.

Development of Optical Differential Sensing Based on Nanomaterials for Biological Analysis

The discrimination and recognition of biological targets, such as proteins, cells, and bacteria, are of utmost importance in various fields of biological research and production. These include areas like biological medicine, clinical diagnosis, and microbiology analysis. In order to efficiently and cost-effectively identify a specific target from a wide range of possibilities, researchers have developed a technique called differential sensing. Unlike traditional "lock-and-key" sensors that rely on specific interactions between receptors and analytes, differential sensing makes use of cross-reactive receptors. These sensors offer less specificity but can cross-react with a wide range of analytes to produce a large amount of data. Many pattern recognition strategies have been developed and have shown promising results in identifying complex analytes. To create advanced sensor arrays for higher analysis efficiency and larger recognizing range, various nanomaterials have been utilized as sensing probes. These nanomaterials possess distinct molecular affinities, optical/electrical properties, and biological compatibility, and are conveniently functionalized. In this review, our focus is on recently reported optical sensor arrays that utilize nanomaterials to discriminate bioanalytes, including proteins, cells, and bacteria.

Dual-Emission Single Sensing Element-Assembled Fluorescent Sensor Arrays for the Rapid Discrimination of Multiple Surfactants in Environments

Surfactants are considered as typical emerging pollutants, their extensive use of in disinfectants has hugely threatened the ecosystem and human health, particularly during the pandemic of coronavirus disease-19 (COVID-19), whereas the rapid discrimination of multiple surfactants in environments is still a great challenge. Herein, we designed a fluorescent sensor array based on luminescent metal-organic frameworks (UiO-66-NH2@Au NCs) for the specific discrimination of six surfactants (AOS, SDS, SDSO, MES, SDBS, and Tween-20). Wherein, UiO-66-NH2@Au NCs were fabricated by integrating UiO-66-NH2 (2-aminoterephthalic acid-anchored-MOFs based on zirconium ions) with gold nanoclusters (Au NCs), which exhibited a dual-emission features, showing good luminescence. Interestingly, due to the interactions of surfactants and UiO-66-NH2@Au NCs, the surfactants can differentially regulate the fluorescence property of UiO-66-NH2@Au NCs, producing diverse fluorescent "fingerprints", which were further identified by pattern recognition methods. The proposed fluorescence sensor array achieved 100% accuracy in identifying various surfactants and multicomponent mixtures, with the detection limit in the range of 0.0032 to 0.0315 mM for six pollutants, which was successfully employed in the discrimination of surfactants in real environmental waters. More importantly, our findings provided a new avenue in rapid detection of surfactants, rendering a promising technique for environmental monitoring against trace multicontaminants.

Enhanced "Electronic Tongue" for Dental Bacterial Discrimination and Elimination Based on a DNA-Encoded Nanozyme Sensor Array

Bacterial infections are the second leading cause of death around the world, especially those caused by delayed treatment and misdiagnosis. Therefore, rapid discrimination and effective elimination of multiple bacteria are of great importance for improving the survival rate in clinic. Herein, a novel colorimetric sensor array for bacterial discrimination and elimination is constructed using programmable DNA-encoded iron oxide nanoparticles (IONPs) as sensing elements. Utilizing differential interactions of bacteria on DNA-encoded IONPs, 11 kinds of dental bacteria and 6 kinds of proteins have been successfully identified by linear discriminant analysis (LDA). Moreover, the developed sensing system also performs well in the quantitative determination of individual bacteria and identification of bacterial mixtures. More importantly, the practicability of this sensing strategy is further verified by precise differentiation of blind and artificial saliva samples. Furthermore, the sensor array is used for efficiently killing multiple bacteria, demonstrating great potential in clinical prophylaxis and therapy.

Plasmon-Stimulated Colorimetry Biosensor Array for the Identification of Multiple Metabolites

Rationally designing highly catalytic and stable nanozymes for metabolite monitoring is of great importance because of their huge potential in early disease diagnosis. Herein, a novel nanozyme based on hierarchically structured CuS/ZnS with a highly efficient peroxidase (POD)-mimic capability was developed and synthesized for multiple metabolite determination and recognition via the plasmon-stimulated biosensor array strategy. The designed nanozyme can simultaneously harvest plasmon triggered hot electron-hole pairs and generate photothermal properties, leading to a sharply boosted POD-mimic capability under 808 nm laser irradiation. Interestingly, because of the interaction diversity of the metabolite with POD-like nanomaterials, the unique inhibitory effect of metabolites on the POD-mimic activity could be the signal response as the differentiation. Thus, utilizing TMB as a typical chromogenic substrate in the addition of H2O2, the designed colorimetric biosensor array can produce diverse fingerprints for the three vital metabolisms (cysteine (Cys), ascorbic acid (AA), and glutathione (GSH)), which can be precisely identified by principal component analysis (PCA). Notably, a distinct fingerprint of a single metabolite with different levels and metabolite mixtures is also achieved with a detection limit of 1 μM. Most importantly, cell lysis could be effectively discriminated by the biosensor assay, implying its great potential in clinical diagnosis.

Intelligent Identification of Cerebrospinal Fluid for the Diagnosis of Parkinson's Disease

Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson's disease (PD). Early detection of PD provides the best chance for successful implementation of disease-modifying treatments, making it crucial to effectively identify CSF extracted from PD patients or normal individuals. In this study, an intelligent sensor array was built by using three metal-organic frameworks (MOFs) that exhibited varying catalytic kinetics after reacting with potential protein markers. Machine learning algorithms were used to process fingerprint response patterns, allowing for qualitative and quantitative assessment of the proteins. The results were robust and capable of discriminating between PD and non-PD patients via CSF detection. The k-nearest neighbor regression algorithm was used to predict MDS scores with a minimum mean square error of 38.88. The intelligent MOF sensor array is expected to promote the detection of CSF biomarkers due to its ability to identify multiple targets and could be used in conjunction with MDS criteria and other techniques to diagnose PD more sensitively and selectively.

A colorimetric sensor array based on nanoceria crosslinked and heteroatom-doped graphene oxide nanoribbons for the detection and discrimination of multiple pesticides

Nanozymes have demonstrated high potential in constructing colorimetric sensor array for pesticides. However, rarely array for pesticides constructed without bio-enzyme were reported. Herein, nanoceria crosslinked graphene oxide nanoribbons (Ce-GONRs) and heteroatom-doped graphene oxide nanoribbons (Ce-BGONRs and Ce-NGONRs) were prepared, demonstrating excellent peroxidase-like activities. A colorimetric sensor array was developed based on directly inhibiting the peroxidase-like activities of the above three nanozymes, which realized the discrimination and quantitative analysis of six pesticides. In the presence of pesticides including carbaryl (Car), fluroxypyr-mepthyl (Flu), thiophanate-methyl (Thio), thiram (Thir), diafenthiuron (Dia) and fomesafen (Fom), the peroxidase-like activities of three nanozymes were inhibited to different degrees, resulting in different fingerprint responses. The six pesticides in the concentration range of 0.1-50 μg/mL and two pesticides mixtures at varied ratios could be detected and discriminated, and minimum detection limit for pesticides was 0.022 μg/mL. In addition, this sensor array has been successfully applied for pesticides discrimination in lake water and apple samples. This work provided a new strategy of constructing simple and sensitive colorimetric sensor array for pesticides based on directly inhibiting the catalytic activities of nanozymes.

Advances in terahertz metasurface graphene for biosensing and application

Based on the extraordinary electromagnetic properties of terahertz waves, such as broadband, low energy, high permeability, and biometric fingerprint spectra, terahertz sensors show great application prospects in the biochemical field. However, the sensitivity of terahertz sensing technology is increasingly required by modern sensing demands. With the development of terahertz technology and functional materials, graphene-based terahertz metasurface sensors with the advantages of high sensitivity, fingerprint identification, nondestructive and anti-interference are gradually gaining attention. In addition to providing ideas for terahertz biosensors, these devices have attracted in-depth research and development by scientists. An overview of graphene-based terahertz metasurfaces and their applications in the detection of biochemical molecules is presented. This includes sensor mechanism research, graphene metasurface index evaluation, protein and nucleic acid sensors, and other chemical molecule sensing. A comparative analysis of graphene, nanomaterials, silicon, and metals to develop material-integrated metasurfaces. Furthermore, a brief summary of the main performance results of this class of devices is presented, along with suggestions for improvements to the existing shortcoming.

Signal Amplification Strategy Design in Nanozyme-Based Biosensors for Highly Sensitive Detection of Trace Biomarkers

Nanozymes show great promise in enhancing disease biomarker sensing by leveraging their physicochemical properties and enzymatic activities. These qualities facilitate signal amplification and matrix effects reduction, thus boosting biomarker sensing performance. In this review, recent studies from the last five years, concentrating on disease biomarker detection improvement through nanozyme-based biosensing are examined. This enhancement primarily involves the modulations of the size, morphology, doping, modification, electromagnetic mechanisms, electron conduction efficiency, and surface plasmon resonance effects of nanozymes for increased sensitivity. In addition, a comprehensive description of the synthesis and tuning strategies employed for nanozymes has been provided. This includes a detailed elucidation of their catalytic mechanisms in alignment with the fundamental principles of enhanced sensing technology, accompanied by the presentation of quantitatively analyzed results. Moreover, the diverse applications of nanozymes in strip sensing, colorimetric sensing, electrochemical sensing, and surface-enhanced Raman scattering have been outlined. Additionally, the limitations, challenges, and corresponding recommendations concerning the application of nanozymes in biosensing have been summarized. Furthermore, insights have been offered into the future development and outlook of nanozymes for biosensing. This review aims to serve not only as a reference for enhancing the sensitivity of nanozyme-based biosensors but also as a catalyst for exploring nanozyme properties and their broader applications in biosensing.

A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification

Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.

Phase Separation of DNA-Encoded Artificial Cells Boosts Signal Amplification for Biosensing

Life-like hierarchical architecture shows great potential for advancing intelligent biosensing, but modular expansion of its sensitivity and functionality remains a challenge. Drawing inspiration from intracellular liquid-liquid phase separation, we discovered that a DNA-encoded artificial cell with a liquid core (LAC) can enhance peroxidase-like activity of Hemin and its DNA G-quadruplex aptamer complex (DGAH) without substrate-selectivity, unlike its gelled core (GAC) counterpart. The LAC is easily engineered as an ultrasensitive biosensing system, benefiting from DNA's high programmability and unique signal amplification capability mediated by liquid-liquid phase separation. As proof of concept, its versatility was successfully demonstrated by coupling with two molecular recognition elements to monitor tumor-related microRNA and profile cancer cell phenotypes. This scalable design philosophy offers new insights into the design of next generation of artificial cells-based biosensors.

Battery-free and AI-enabled multiplexed sensor patches for wound monitoring

Wound healing is a dynamic process with multiple phases. Rapid profiling and quantitative characterization of inflammation and infection remain challenging. We report a paper-like battery-free in situ AI-enabled multiplexed (PETAL) sensor for holistic wound assessment by leveraging deep learning algorithms. This sensor consists of a wax-printed paper panel with five colorimetric sensors for temperature, pH, trimethylamine, uric acid, and moisture. Sensor images captured by a mobile phone were analyzed by neural network-based machine learning algorithms to determine healing status. For ex situ detection via exudates collected from rat perturbed wounds and burn wounds, the PETAL sensor can classify healing versus nonhealing status with an accuracy as high as 97%. With the sensor patches attached on rat burn wound models, in situ monitoring of wound progression or severity is demonstrated. This PETAL sensor allows early warning of adverse events, which could trigger immediate clinical intervention to facilitate wound care management.

Ag+-gated peroxidase activity of gold nanoparticles for sensitive detection of Escherichia coli

Escherichia coli (E. coli) is one of the most ubiquitous foodborne pathogens that can cause infections and threaten human health. Herein, a colorimetric method for sensitive detection of E. coli was established by using enzyme-nanozyme cascade reaction for signal amplification. Gold nanoparticles (AuNPs) are well-known nanozymes due to their high peroxidase-like activity. When the dense cetyltrimethylammonium bromide (CTAB) membrane on the surfaces of AuNPs kept the substrate away from AuNPs, the peroxidase activity of AuNPs was inhibited. However, the CTAB membrane could be disrupted by Ag⁺, resulting in enhanced peroxidase activity of AuNPs. When E. coli was present, the enzyme-nanozyme cascade reaction was initiated. The substrate p-aminophenyl β-D-galactopyranoside (PAPG) was hydrolyzed to the reductive p-aminophenol (PAP) by beta-galactosidase (β-gal) in E. coli, reducing Ag⁺ to Ag. Consequently, CTAB-AuNPs remained weak peroxidase activity and could not catalyze the H₂O₂-mediated oxidation of TMB. As the amount of E. coli increased, the absorbance of TMB decreased along with a color change from deep blue to pink. The absorbance intensity displayed a linear dependence on E. coli from 1.0 × 10² to 1.0 × 10⁹ CFU mL^-1. Therefore, the proposed method holds good prospects in foodborne pathogenic bacteria detection.

Nanozyme colorimetric sensor array based on monatomic cobalt for the discrimination of sulfur-containing metal salts

The identification of sulfur-containing metal salts (SCMs) is of great interest because they play an important role in many biological processes and diseases. Here, we constructed a ternary channel colorimetric sensor array to detect multiple SCMs simultaneously, relying on monatomic Co embedded in nitrogen-doped graphene nanozyme (CoN₄-G). Due to the unique structure, CoN₄-G exhibits activity similar to native oxidases, capable of catalysing directly the oxidization of 3,3',5,5'-tetramethylbenzidine (TMB) by O₂ molecules independent of H₂O₂. Density functional theory (DFT) calculations suggest that CoN₄-G has no potential barrier in the whole reaction route, thus presenting higher oxidase-like catalytic activity. Based on different degrees of TMB oxidation, different colorimetric response changes are obtained as "fingerprints" on the sensor array. The sensor array can discriminate different concentrations of unitary, binary, ternary, and quaternary SCMs and has been successfully applied to detect six real samples (soil, milk, red wine and egg white). To advance the field detection of the above four types of SCMs, we creatively propose a smartphone-based autonomous detection platform with a linear range of 1.6-320 μM and a limit of detection of 0.0778-0.218 μM, which demonstrates the potential use of sensor arrays in the application of disease diagnosis and food and environment monitoring.

Carbocyanine-Based Optical Sensor Array for the Discrimination of Proteins and Rennet Samples Using Hypochlorite Oxidation

Optical sensor arrays are widely used in obtaining fingerprints of samples, allowing for solutions of recognition and identification problems. An approach to extending the functionality of the sensor arrays is using a kinetic factor by conducting indicator reactions that proceed at measurable rates. In this study, we propose a method for the discrimination of proteins based on their oxidation by sodium hypochlorite with the formation of the products, which, in turn, feature oxidation properties. As reducing agents to visualize these products, carbocyanine dyes IR-783 and Cy5.5-COOH are added to the reaction mixture at pH 5.3, and different spectral characteristics are registered every several minutes (absorbance in the visible region and fluorescence under excitation by UV (254 and 365 nm) and red light). The intensities of the photographic images of the 96-well plate are processed by principal component analysis (PCA) and linear discriminant analysis (LDA). Six model proteins (bovine and human serum albumins, γ-globulin, lysozyme, pepsin, and proteinase K) and 10 rennet samples (mixtures of chymosin and pepsin from different manufacturers) are recognized by the proposed method. The method is rapid and simple and uses only commercially available reagents.

Recent advances in colorimetric sensors based on nanozymes with peroxidase-like activity

Nanozymes have been widely used to construct colorimetric sensors due to their advantages of cost-effectiveness, high stability, good biocompatibility, and ease of modification. The emergence of nanozymes greatly enhanced the detection sensitivity and stability of the colorimetric sensing platform. Recent significant research has focused on designing various sensors based on nanozymes with peroxidase-like activity for colorimetric analysis. However, with the deepening of research, nanozymes with peroxidase-like activity has also exposed some problems, such as weak affinity and low catalytic activity. In view of the above issues, existing investigations have shown that the catalytic properties of nanozymes can be improved by adding surface modification and changing the structure of nanomaterials. In this review, we summarize the recent trends and advances of colorimetric sensors based on several typical nanozymes with peroxidase-like activities, including noble metals, metal oxides, metal sulfides/metal selenides, and carbon and metal-organic frameworks (MOF). Finally, the current challenges and prospects of colorimetric sensors based on nanozymes with peroxidase-like activity are summarized and discussed to provide a reference for researchers in related fields.

Colorimetric Sensor Array for Identification of Proteins and Classification of Metabolic Profiles under Various Osmolyte Conditions

Rapid and efficient detection and identification of proteins hold great promise in medical diagnostics, treatment of different diseases, and proteomics. Here, we present a simple colorimetric sensor array for the differentiation of proteins in various osmolyte solutions. Osmolytes have different influences on the conformation of proteins, which have differential binding to silver nanoparticles, resulting in color changes. The sensor array shows unique color change patterns for each of the 19 proteins, allowing unambiguous identification. Very interestingly, the differentiation of 19 proteins is related to their molecular weight. Moreover, the sensor array can be used to identify protein mixtures, thermal denaturized proteins, and unknown protein samples. Finally, the sensor array can also analyze the plasma or liver samples of the four groups of salt-sensitive rats fed with different diets, indicating that it has the potential for the classification of metabolic profiles.

Colorimetric and photothermal dual-mode immunoassay of aflatoxin B1 based on peroxidase-like activity of Pt supported on nitrogen-doped carbon

In this work, a split-type dual-mode (colorimetric/photothermal) immunoassay method was designed for point-of-care testing (POCT) detection of mycotoxins (aflatoxin B₁, AFB₁ as the model analyte) in foodstuffs based on Pt supported on nitrogen-doped carbon amorphous (Pt-CN). The as-synthesized Pt-CN exhibits excellent peroxidase-mimicking activity, which can catalyze the oxidization of 3,3',5,5'-tetramethylbenzidine (TMB) into TMB_ox with sensitive colorimetric readout in the presence of hydrogen peroxide (H₂O₂). Moreover, the TMB_ox also serves as a near-infrared (NIR) photothermal agent to convert the colorimetric readout into heat under the irradiation of an 808 nm laser. A competitive-type immunoreaction is carried out between AFB₁ and glucose oxidase (GOx)-labeled AFB₁-bovine serum albumin (AFB₁-BSA-GOx) conjugates. With the formation of immune complexes, the entrained GOx catalyzes the hydrolysis of glucose to generate H₂O_2, which further involves the Pt-CN catalyzed production of TMB_ox to increase colorimetric/photothermal readouts. Depending on the degree of TMB oxidation, the dual-mode immunoassay provides a linear range of 1.0 pg/mL to 10 ng/mL, with a limit of detection (LOD) of 0.22 pg/mL for the colorimetric assay and 0.76 pg/mL for the photothermal assay. Moreover, the developed method is successfully used to detect AFB₁ in peanuts with acceptable accuracy compared with commercially enzyme-linked immunosorbent assay (ELISA) kits. Significantly, the photothermal readout in this method is recorded on a mobile phone device without any expensive instruments, providing an affordable and convenient tool for food safety testing.

The Link between Stroke Risk and Orodental Status-A Comprehensive Review

One of the primary causes of disability and mortality in the adult population worldwide is stroke. A person's general health is significantly impacted by their oral and dental health. People who have poor oral health are more susceptible to conditions such as stroke. Stroke risk has long been linked to oral and dental conditions. The risk of stroke and its cost impact on the healthcare systems appear to be significantly reduced as a result of the decline in the incidence and prevalence of oral and dental illnesses. Hypothetically, better management of oral hygiene and dental health lead to reduced stroke risk. To the authors' best knowledge, for the first time, the potential link between dental health and stroke were cross-examined. The most typical stroke symptoms, oral and dental illnesses linked to stroke, and the role of oral healthcare professionals in stroke prevention are revealed. The potential mediating processes and subsequent long-term cognitive and functional neurological outcomes are based on the available literature. It must be noted that periodontal diseases and tooth loss are two common oral health measures. Lack of knowledge on the effects of poor oral health on systemic health together with limited access to primary medical or dental care are considered to be partially responsible for the elevated risk of stroke. Concrete evidence confirming the associations between oral inflammatory conditions and stroke in large cohort prospective studies, stratifying association between oral disease severity and stroke risk and disease effects on stroke survival will be desirable. In terms of clinical pathology, a predictive model of stroke as a function of oral health status, and biomarkers of systemic inflammation could be useful for both cardiologists and dentists.

Continuously Quantifying Oral Chemicals Based on Flexible Hybrid Electronics for Clinical Diagnosis and Pathogenetic Study

Simultaneous monitoring of diverse salivary parameters can reveal underlying mechanisms of intraoral biological processes and offer profound insights into the evolution of oral diseases. However, conventional analytical devices with bulky volumes, rigid formats, and discrete sensing mechanisms deviate from the requirements of continuous biophysiological quantification, resulting in huge difficulty in precise clinical diagnosis and pathogenetic study. Here, we present a flexible hybrid electronic system integrated with functional nanomaterials to continuously sense Ca2+, pH, and temperature for wireless real-time oral health monitoring. The miniaturized system with an island-bridge structure that is designed specifically to fit the teeth is only 0.4 g in weight and 31.5×8.5×1.35 mm3 in dimension, allowing effective integration with customized dental braces and comfort attachment on teeth. Characterization results indicate high sensitivities of 30.3 and 60.6 mV/decade for Ca2+ and pH with low potential drifts. The system has been applied in clinical studies to conduct Ca2+ and pH mappings on carious teeth, biophysiological monitoring for up to 12 h, and outcome evaluation of dental restoration, providing quantitative data to assist in the diagnosis and understanding of oral diseases. Notably, caries risk assessment of 10 human subjects using the flexible system validates the important role of saliva buffering capacity in caries pathogenesis. The proposed flexible system may offer an open platform to carry diverse components to support both clinical diagnosis and treatment as well as fundamental research for oral diseases and induced systemic diseases.

A multifunctional colorimetric sensor array for bacterial identification and real-time bacterial elimination to prevent bacterial contamination

The constructed sensor array has simple operation and successfully integrates bacterial identification and inactivation.