Advances in Electrochemical Nano-Biosensors for Biomedical and Environmental Applications: From Current Work to Future Perspectives

Esterase-2 mutant-based nanostructured amperometric biosensors for the selective determination of paraoxon (Neurotoxin)

Organophosphate pesticides (OPs) are causing non-selective inhibition in enzymatic bioreceptors, thus the enzymatic-inhibition-based traditional assays are not suitable for their specific detection in food and environmental samples. Accordingly, a selective nanostructured electrochemical biosensing system was designed using six mutants of the esterase-2 (EST2 protein) enzymes from A. acidocaldarius to be exploited as targeting bio-receptors for the specific detection of OPs. Each of the EST2 mutant enzymes was immobilized on disposable screen-printed electrodes modified with Aluminum oxide (Al2O3)/Copper (Cu) nanocomposite. Consequently, chronoamperometric assay was fully optimized, and cross-reactivity study was carried out using paraoxon, malathion and chlorpyrifos. The comparative cross-reactivity study was performed on the six mutant proteins in terms of inhibitory percentage over a wide range of pesticide concentrations. Eventually, a wide dynamic inhibition range was achieved while the limit of detection for the paraoxon toxicity was 0.01 nM and the limit of quantification was 0.05 nM. Finally, paraoxon was selectively determined using the newly developed EST-based biosensor in different spiked food samples.

Biosensors for the detection of celiac disease

Celiac disease (CeD) is an autoimmune disorder triggered by sensitivity to gluten, a protein complex found in wheat, barley, and rye. Gliadins, a component of gluten, are proteins that trigger an immune response in individuals with CeD, primarily affecting the small intestine's inner lining. Despite a 1-1.5% prevalence, only 24% of cases are diagnosed due to non-specific symptoms. Screening is advised for high-risk groups, including first-degree relatives and type 1 diabetes patients. The accurate diagnosis of this condition and the assessment of the patient's response to the current treatment - a lifelong gluten-free diet - necessitate using dependable, swift, sensitive, specific, uncomplicated, and affordable analytical methods. Detecting CeD biomarkers in whole blood, serum, or plasma provides a non-invasive approach that serves as an ideal initial diagnostic step. Biosensors offer a novel and alternative way for CeD detection, began emerging in 2007, and hold promise for clinical and point-of-care applications. This review explores the use of biomarker-based diagnostic approaches for CeD, with a focus on biosensors. It delves into the progress of biosensors for CeD diagnosis, identifying trends and challenges in this evolving field. Key biomarkers are highlighted, offering insights into the evolving landscape of biosensors in CeD detection.

Machine Learning-Driven Innovations in Microfluidics

Microfluidic devices have revolutionized biosensing by enabling precise manipulation of minute fluid volumes across diverse applications. This review investigates the incorporation of machine learning (ML) into the design, fabrication, and application of microfluidic biosensors, emphasizing how ML algorithms enhance performance by improving design accuracy, operational efficiency, and the management of complex diagnostic datasets. Integrating microfluidics with ML has fostered intelligent systems capable of automating experimental workflows, enabling real-time data analysis, and supporting informed decision-making. Recent advances in health diagnostics, environmental monitoring, and synthetic biology driven by ML are critically examined. This review highlights the transformative potential of ML-enhanced microfluidic systems, offering insights into the future trajectory of this rapidly evolving field.

Wearable biosensors for pediatric hospitals: a scoping review

As wearable biosensors are increasingly used in healthcare settings, this review aimed to identify the types of wearable biosensors used for neonate and pediatric patients and how these biosensors were clinically evaluated. A literature search was conducted using PubMed, CINAHL, Embase, Web of Science, and Cochrane. The studies published between January 2010 and February 2024 were included. Descriptive statistics were used to present counts and percentages of types, locations, clinical evaluation methods, and their results. Seventy-nine studies were included. 104 wearable sensors and 40 devices were identified. The most common type of biosensor was optoelectrical sensors (n = 40, 38.5%), and used to measure heart rate (n = 22, 19.0%). The clinical evaluation was tested by a combination of validity (n = 68, 86.1%) and reliability (n = 14, 17.7%). Only two-thirds of the wearable devices were validated or reported acceptable reliability. The majority of the biosensor studies (n = 51, 64.5%) did not report any complications related to wearable biosensors. The current literature has gaps regarding clinical evaluation and safety of wearable biosensor devices with interchangeable use of validity and reliability terms. There is a lack of comprehensive reporting on complications, highlighting the need for standardized guidelines in the clinical evaluation of biosensor medical devices. IMPACT: The most common types of biosensors in pediatric settings were optoelectrical sensors and electrical sensors. Only two-thirds of the wearable devices were validated or reported acceptable reliability, and more than half of the biosensor studies did not report whether they assessed any complications related to wearable biosensors. This review discovered significant gaps in safety and clinical validation reporting, emphasizing the need for standardized guidelines. The findings advocate for improved reporting clinical validation processes to enhance the safety of wearable biosensors in pediatric care.

A Machine Learning Assisted Non-Enzymatic Electrochemical Biosensor to Detect Urea Based on Multi-Walled Carbon Nanotube Functionalized with Copper Oxide Micro-Flowers

Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations. For the detection of urea, the biosensor was modified with a multi-walled carbon nanotube-zinc oxide (MWCNT-ZnO) nanocomposite functionalized with copper oxide (CuO) micro-flowers (MFs). Further, the CuO-MFs were synthesized using a standard sol-gel approach, and the obtained particles were subjected to various characterization techniques, including X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR) spectroscopy. The sensor's performance for urea detection was evaluated by assessing the dependence of peak currents on analyte concentration using cyclic voltammetry (CV) at different scan rates of 50, 75, and 100 mV/s. The designed non-enzymatic biosensor showed an acceptable linear range of operation of 0.5-8 mM, and the limit of detection (LoD) observed was 78.479 nM, which is well aligned with the urea concentration found in human blood and exhibits a good sensitivity of 117.98 mA mM-1 cm-2. Additionally, different regression-based ML models were applied to determine CV parameters to predict urea concentrations experimentally. ML significantly improves the accuracy and reliability of screen-printed biosensors, enabling accurate predictions of urea levels. Finally, the combination of ML and biosensor design emphasizes not only the high sensitivity and accuracy of the sensor but also its potential for complex non-enzymatic urea detection applications. Future advancements in accurate biochemical sensing technologies are made possible by this strong and dependable methodology.

Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns. As the Internet of Things (IoT), big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. Still, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights the critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology.

Nanobiosensors based on on-site detection approaches for rapid pesticide sensing in the agricultural arena: A systematic review of the current status and perspectives

The extensive use of chemical pesticides has significantly boosted agricultural food crop yields. Nevertheless, their excessive and unregulated application has resulted in food contamination and pollution in environmental, aquatic, and agricultural ecosystems. Consequently, the on-site monitoring of pesticide residues in agricultural practices is paramount to safeguard global food and conservational safety. Traditional pesticide detection methods are cumbersome and ill-suited for on-site pesticide finding. The systematic review provides an in-depth analysis of the current status and perspectives of nanobiosensors (NBS) for pesticide detection in the agricultural arena. Furthermore, the study encompasses the fundamental principles of NBS, the various transduction mechanisms employed, and their incorporation into on-site detection platforms. Conversely, the assortment of transduction mechanisms, including optical, electrochemical, and piezoelectric tactics, is deliberated in detail, emphasizing its advantages and limitations in pesticide perception. Incorporating NBS into on-site detection platforms confirms a vital feature of their pertinence. The evaluation reflects the integration of NBS into lab-on-a-chip systems, handheld devices, and wireless sensor networks, permitting real-time monitoring and data-driven decision-making in agronomic settings. The potential for robotics and automation in pesticide detection is also scrutinized, highlighting their role in improving competence and accuracy. Finally, this systematic review provides a complete understanding of the current landscape of NBS for on-site pesticide sensing. Consequently, we anticipate that this review offers valuable insights that could form the foundation for creating innovative NBS applicable in various fields such as materials science, nanoscience, food technology and environmental science.

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Biomedical applications of graphene-based nanomaterials: recent progress, challenges, and prospects in highly sensitive biosensors

Graphene-based nanomaterials (graphene, graphene oxide, reduced graphene oxide, graphene quantum dots, graphene-based nanocomposites, etc.) are emerging as an extremely important class of nanomaterials primarily because of their unique and advantageous physical, chemical, biological, and optoelectronic aspects. These features have resulted in uses across diverse areas of scientific research. Among all other applications, they are found to be particularly useful in designing highly sensitive biosensors. Numerous studies have established their efficacy in sensing pathogens and other biomolecules allowing for the rapid diagnosis of various diseases. Considering the growing importance and popularity of graphene-based materials for biosensing applications, this review aims to provide the readers with a summary of the recent progress in the concerned domain and highlights the challenges associated with the synthesis and application of these multifunctional materials.

Structure of plasmonic multi spectral Apta sensor and analyzing of bulk and surface sensitivity

In this work, a multispectral aptasensor structure, including a sub-layer and two side walls, was presented. The cells are positioned at the down and top of the structure, with the down cells oriented perpendicular to the walls and the top cells aligned parallel to the walls. The validity of the findings was verified by the utilization of a numerical simulation technique known as 3D Finite Difference Time Domain (FDTD). The biosensor under consideration exhibits sensitivities of 1093.7 nm/RIU, 754 nm/RIU, and 707.43 nm/RIU in mode III, mode II, and mode I, respectively. In the majority of instances, the quantity of analyte available is insufficient to coat the surface of the sensor thoroughly. Consequently, in this study, the evaluation of surface sensitivity was undertaken alongside bulk sensitivity. The surface sensitivity of the suggested structure for mode II in the sensor layer, with thicknesses of 10, 20, 30, and 70 nm, is measured to be 25, 78, 344, and 717.636 nm/RIU, respectively. Our design incorporates a unique arrangement of sub-layer and side walls, with cells positioned to maximize interaction with the target analyte. This innovative configuration, combined with Ag for its superior plasmonic properties, enables the detection of E. coli O157 with remarkable sensitivity.

Recent advances in smart wearable sensors for continuous human health monitoring

In recent years, the biochemical and biological research areas have shown great interest in a smart wearable sensor because of its increasing prevalence and high potential to monitor human health in a non-invasive manner by continuous screening of biomarkers dispersed throughout the biological analytes, as well as real-time diagnostic tools and time-sensitive information compared to conventional hospital-centered system. These smart wearable sensors offer an innovative option for evaluating and investigating human health by incorporating a portion of recent advances in technology and engineering that can enhance real-time point-of-care-testing capabilities. Smart wearable sensors have emerged progressively with a mixture of multiplexed biosensing, microfluidic sampling, and data acquisition systems incorporated with flexible substrate and bodily attachments for enhanced wearability, portability, and reliability. There is a good chance that smart wearable sensors will be relevant to the early detection and diagnosis of disease management and control. Therefore, pioneering smart wearable sensors into reality seems extremely promising despite possible challenges in this cutting-edge technology for a better future in the healthcare domain. This review presents critical viewpoints on recent developments in wearable sensors in the upcoming smart digital health monitoring in real-time scenarios. In addition, there have been proactive discussions in recent years on materials selection, design optimization, efficient fabrication tools, and data processing units, as well as their continuous monitoring and tracking strategy with system-level integration such as internet-of-things, cyber-physical systems, and machine learning algorithms.

Measurement of microRNA-106b as a gastric cancer biomarker based on Zn-BTC MOF label-free genosensor

Due to the late detection of stomach cancer, this cancer usually causes high mortality. The development of an electrochemical genosensor to measure microRNA 106b (miR-106b), as a gastric cancer biomarker, is the aim of this effort. In this regard, first, 1,3,5-benzenetricarboxylate (BTC) metal-organic frameworks (Zn-BTC MOF) were self-assembled on the glassy carbon electrode and then the probe (ssDNA) was immobilized on it. The morphology Zn-BTC MOF was characterized by SEM, FT-IR, Raman and X-Ray techniques. Zn-BTC MOF as a biosensor substrate has strong interaction with ssDNA. Quantitative measurement of miR-106b was performed by electrochemical impedance spectroscopy (EIS). To perform this measurement, the difference of the charge transfer resistances (ΔRct) of Nyquist plots of the ssDNA probe modified electrode before and after hybridization with miR-106b was obtained and used as an analytical signal. Using the suggested genosensor, it is possible to measure miR-106b in the concentration range of 1.0 fM to 1.0 μM with a detection limit of 0.65 fM under optimal conditions. Moreover, at the genosensor surface, miR-106b can be detected from a non-complementary and a single base mismatch sequence. Also, the genosensor was used to assess miR-106b in a human serum sample and obtained satisfactory results.

Nanoarchitectonics-based electrochemical aptasensors for highly efficient exosome detection

Exosomes, a type of extracellular vesicles, have attracted considerable attention due to their ability to provide valuable insights into the pathophysiological microenvironment of the cells from which they originate. This characteristic implicates their potential use as diagnostic disease biomarkers clinically, including cancer, infectious diseases, neurodegenerative disorders, and cardiovascular diseases. Aptasensors, which are electrochemical aptamers based biosensing devices, have emerged as a new class of powerful detection technology to conventional methods like ELISA and Western analysis, primarily because of their capability for high-performance bioanalysis. This review covers the current research landscape on the detection of exosomes utilizing nanoarchitectonics strategy for the development of electrochemical aptasensors. Strategies involving signal amplification and biofouling prevention are discussed, with an emphasis on nanoarchitectonics-based bio-interfaces, showcasing their potential to enhance sensitivity and selectivity through optimal conduction and mass transport properties. The ongoing challenges to broaden the clinical applications of these biosensors are also highlighted.

Biosensors for the detection of pathogenic bacteria: current status and future perspectives

Pathogenic microorganisms pose significant threats to human health, food safety and environmental integrity. Rapid and accurate detection of these pathogens is essential to mitigate their impact. Fast, sensitive detection methods such as biosensors also play a critical role in preventing outbreaks and controlling their spread. In recent years, biosensors have emerged as a revolutionary technology for pathogen detection. This review aims to present the current developments in biosensor technology, investigate the methods by which these developments are used in the detection of pathogenic bacteria and highlight future perspectives on the subject.

Nanomaterial-based biosensors for avian influenza virus: A new way forward

Avian influenza virus (AIV) is a zoonotic virus that can be transmitted from animals to humans. Although human infections are rare, the virus has a high mortality rate when contracted. Appropriate detection methods are thus crucial for combatting this pathogen. There is a growing demand for rapid, selective, and accurate methods of identifying the virus. Numerous biosensors have been designed and commercialized to detect AIV. However, they all have considerable shortcomings. Nanotechnology offers a new way forward. Nanomaterials produce more eco-friendly, rapid, and portable diagnostic systems. They also exhibit high sensitivity and selectivity while achieving a low detection limit (LOD). This paper reviews state-of-the-art nanomaterial-based biosensors for AIV detection, such as those composed of quantum dots, gold, silver, carbon, silica, nanodiamond, and other nanoparticles. It also offers insight into potential trial protocols for creating more effective methods of identifying AIV and discusses key issues associated with developing nanomaterial-based biosensors.

Generation of a helper phage for the fluorescent detection of peptide-target interactions by dual-display phages

Phage display is a molecular biology technique that allows the presentation of foreign peptides on the surface of bacteriophages. It is widely utilized for applications such as the discovery of biomarkers, the development of therapeutic antibodies, and the investigation of protein-protein interactions. When employing phages in diagnostic and therapeutic monitoring assays, it is essential to couple them with a detection system capable of revealing and quantifying the interaction between the peptide displayed on the phage capsid and the target of interest. This process is often technically challenging and costly. Here, we generated a fluorescent helper phage vector displaying sfGFP in-frame to the pIII of the capsid proteins. Further, we developed an exchangeable dual-display phage system by combining our newly developed fluorescent helper phage vector with a phagemid vector harboring the engineered pVIII with a peptide-probe. By doing so, the sfGFP and a peptide-probe are displayed on the same phage particle. Notably, our dual-display approach is highly flexible as it allows for easy exchange of the displayed peptide-probe on the pVIII to gain the desired selectivity, while maintaining the sfGFP gene, which allows easy visualization and quantification of the interaction peptide-probe. We anticipate that this system will reduce time and costs compared to the current phage-based detection systems.

Nanomaterials and Their Recent Applications in Impedimetric Biosensing

Impedimetric biosensors measure changes in the electrical impedance due to a biochemical process, typically the binding of a biomolecule to a bioreceptor on the sensor surface. Nanomaterials can be employed to modify the biosensor's surface to increase the surface area available for biorecognition events, thereby improving the sensitivity and detection limits of the biosensor. Various nanomaterials, such as carbon nanotubes, carbon nanofibers, quantum dots, metal nanoparticles, and graphene oxide nanoparticles, have been investigated for impedimetric biosensors. These nanomaterials have yielded promising results in improving sensitivity, selectivity, and overall biosensor performance. Hence, they offer a wide range of possibilities for developing advanced biosensing platforms that can be employed in various fields, including healthcare, environmental monitoring, and food safety. This review focuses on the recent developments in nanoparticle-functionalized electrochemical-impedimetric biosensors.

A dual-targeting nanobiosensor for Gender Determination applying Signal Amplification Methods and integrating Fluorometric Gold and Silver Nanoclusters

A dual-targeting nanobiosensor has been developed for the simultaneous detection of AMELX and AMELY genes based on the different fluorescence signals emitted from gold and silver nanoclusters, AuNCs and AgNCs respectively. In our design, both catalytic hairpin assembly (CHA) and hybridization chain reaction (HCR) have been used as isothermal, enzyme-free and simple methods for signal's amplification. The working principle is based on the initiation of a cascade of CHA-HCR reactions when AMELX is present, in which AuNCs, synthesized on the third hairpin, are aggregated on the surface of the dsDNA product, performing the phenomenon of aggregation induced emission (AIE) and enhancing their fluorescence signal. On the other hand, the presence of the second target, AMELY, is responsible for the enhancement of the fluorescence signal corresponding to AgNCs by the same phenomenon, via hybridizing to the free end of the dsDNA formed and at the same time to the probe of silver nanoclusters fixing it closer to the surface of the dsDNA product. Such a unique design has the merits of being simple, inexpensive, specific and stable and presents rapid results. The detection limits of this assay for AMELX and AMELY are as low as 3.16 fM and 23.6 fM respectively. Moreover, this platform showed great performance in real samples. The design has great promise for the application of dual-targeting nanobiosensors to other biomarkers.

Double-antibody-based nano-biosensing system for the onsite monitoring of SARS-CoV-2 variants

The fast and reliable diagnosis of COVID-19 is the foremost priority for promoting public health interventions. Therefore, double-antibody-based immunobiosensor chips were designed, constructed, and exploited for clinical diagnosis. Gold nanoparticles/tungsten oxide/carbon nanotubes (AuNPs/WO3/CNTs) were used as the active working sensor surface to support the chemical immobilization of a mixture of SARS-CoV-2 antibodies (anti-RBD-S and anti-RBD-S-anti-Llama monoclonal antibodies). The morphology and chemical functionalization of the fabricated disposable immunochips was characterized using scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). After full assay optimization, the immunobiosensor showed a high sensitivity to detect SARS-CoV-2-S protein with limits of detection and quantification of 1.8 and 5.6 pg/mL, respectively. On the other hand, for the SARS-CoV-2 whole virus particle analysis, the detection and quantification limits were determined to be 5.7 and 17 pg/mL, respectively. The biosensor showed a highly selective response toward SARS-CoV-2, even in the presence of influenza, nontargeting human coronaviruses, and Middle East respiratory syndrome coronavirus (MERS-CoV). The immunochips exhibited distinct responses toward the variants of concern: B.1>C.36.3>Omicron> Delta> Alpha coronavirus variants. For biosensor validation, twenty-nine clinical specimens were analyzed, and the impedimetric responses were positively detected for two Delta samples, eighteen Omicron samples, and six B.1-type samples in addition to three negative samples. Eventually, the immunobiosensor was fabricated in the form of ready-to-use chips capable of sensitive detection of virus variants, especially variants of concern (VOC) and interest, in a specimen within 15 min. The chips provided instantaneous detection with the direct application of clinical samples and are considered a point-of-care device that could be used in public places and hot spots.

Selective determination of nitrite in water and food samples using zirconium oxide (ZrO2)@MWCNTs modified screen printed electrode

Nitrite ions are being used in different forms as food preservatives acting as flavor enhancers or coloring agents for food products. However, continuous ingestion of nitrite may have severe health implications due to its mutagenic and carcinogenic effects. Thus, this study constructed an electrochemical assay using disposable nano-sensor chip ZrO₂@MWCNTs screen printed electrodes (SPE) for the rapid, selective, and sensitive determination of nitrite in food and water samples. As a sensing platform, the use of nanomaterials, including metal oxide nanostructures and carbon nanotubes, exhibited a superior electrocatalytic activity and conductivity. Morphological, structural, and electrochemical analyses were performed using electron microscopy (SEM and TEM), Fourier-transform infrared (FTIR) spectroscopy, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and chronoamperometry (CA). Accordingly, a wide dynamic linear range (5.0 μM to 100 μM) was obtained with a limit of detection of 0.94 μM by the chronoamperometric technique. In addition, the sensor's selectivity was tested when several non-target species were exposed to the sensor chips while no obvious electrochemical signals were generated when the nitrite ions were not present. Eventually, real food and water sample analysis was conducted, and a high recovery was achieved.

Fe3O4@Au Core-Shell Magnetic Nanoparticles for the Rapid Analysis of E. coli O157:H7 in an Electrochemical Immunoassay

Escherichia coli (E. coli) O157:H7 is a pathogenic bacterium that causes serious toxic effects in the human gastrointestinal tract. In this paper, a method for its effective analytical control in a milk sample was developed. To perform rapid (1 h) and accurate analysis, monodisperse Fe₃O₄@Au magnetic nanoparticles were synthesized and used in an electrochemical sandwich-type magnetic immunoassay. Screen-printed carbon electrodes (SPCE) were used as transducers, and electrochemical detection was performed by chronoamperometry using a secondary horseradish peroxidase-labeled antibody and 3,3',5,5'-tetramethylbenzidine. This magnetic assay was used to determine the E. coli O157:H7 strain in the linear range from 20 to 2 × 10⁶ CFU/mL, with a limit of detection of 20 CFU/mL. The selectivity of the assay was tested using Listeria monocytogenes p60 protein, and the applicability of the assay was assessed by analyzing a commercial milk sample, demonstrating the usefulness of the synthesized nanoparticles in the developed magnetic immunoassay.

An update on pathogenesis and clinical scenario for Parkinson's disease: diagnosis and treatment

In severe cases, Parkinson's disease causes uncontrolled movements known as motor symptoms such as dystonia, rigidity, bradykinesia, and tremors. Parkinson's disease also causes non-motor symptoms such as insomnia, constipation, depression and hysteria. Disruption of dopaminergic and non-dopaminergic neural networks in the substantia nigra pars compacta is a major cause of motor symptoms in Parkinson's disease. Furthermore, due to the difficulty of clinical diagnosis of Parkinson's disease, it is often misdiagnosed, highlighting the need for better methods of detection. Treatment of Parkinson's disease is also complicated due to the difficulties of medications passing across the blood-brain barrier. Moreover, the conventional methods fail to solve the aforementioned issues. As a result, new methods are needed to detect and treat Parkinson's disease. Improved diagnosis and treatment of Parkinson's disease can help avoid some of its devastating symptoms. This review explores how nanotechnology platforms, such as nanobiosensors and nanomedicine, have improved Parkinson's disease detection and treatment. Nanobiosensors integrate science and engineering principles to detect Parkinson's disease. The main advantages are their low cost, portability, and quick and precise analysis. Moreover, nanotechnology can transport medications in the form of nanoparticles across the blood-brain barrier. However, because nanobiosensors are a novel technology, their use in biological systems is limited. Nanobiosensors have the potential to disrupt cell metabolism and homeostasis, changing cellular molecular profiles and making it difficult to distinguish sensor-induced artifacts from fundamental biological phenomena. In the treatment of Parkinson's disease, nanoparticles, on the other hand, produce neurotoxicity, which is a challenge in the treatment of Parkinson's disease. Techniques must be developed to distinguish sensor-induced artifacts from fundamental biological phenomena and to reduce the neurotoxicity caused by nanoparticles.

Designing and fabrication of electrochemical nano-biosensor for the fast detection of SARS-CoV-2-RNA

SARS-CoV-2 caused a global panic among populations. Rapid diagnostic procedures for the virus are crucial for disease control. Thus, the designed signature probe from a highly conserved region of the virus was chemically immobilized onto the nanostructured-AuNPs/WO₃-screen printed electrodes. Different concentrations of the matched oligonucleotides were spiked to test the specificity of the hybridization affinity whereas the electrochemical impedance spectroscopy was used for tracking the electrochemical performance. After a full assay optimization, limits of detection and quantification were calculated based on linear regression and were valued at 298 and 994 fM, respectively. Further, the high performance of the fabricated RNA-sensor chips was confirmed after testing the interference status in the presence of the mismatched oligos in one nucleotide and completely one. Worthy to mention that the single-stranded matched oligos can be hybridized to the immobilized probe in 5 min at room temperature. The designed disposable sensor chips are capable of detecting the virus genome directly. Therefore, the chips are a rapid tool for SARS-CoV-2 detection.

Bacteriophage-based nano-biosensors for the fast impedimetric determination of pathogens in food samples

The early and rapid detection of pathogenic microorganisms is of critical importance in addressing serious public health issues. Here, a new bacteriophage-based nano-biosensor was constructed and the electrochemical impedimetric method was fully optimized and applied for the quantitative detection of Escherichia coli O157:H7 in food samples. The impact of using a nanocomposite consisting of gold nanoparticles (AuNPs), multi-walled carbon nanotubes (MWCNTs), and tungsten oxide nanostructures (WO₃) on the electrochemical performance of disposable screen printed electrodes was identified using the cyclic voltammetry and electrochemical impedance spectroscopy. The use nanomaterials enabled high capturing sensitivity against the targeting bacterial host cells with the limit of detection of 3.0 CFU/ml. Moreover, selectivity of the covalently immobilized active phage was tested against several non-targeting bacterial strains, where a high specificity was achieved. Thus, the targeting foodborne pathogen was successfully detected in food samples with high specificity, and the sensor provided an excellent recovery rate ranging from 90.0 to 108%. Accordingly, the newly developed phage-biosensor is recommended as a disposable label-free impedimetric biosensor for the quick and real-time monitoring of food quality.

Label-Free Electrochemical Biosensor Platforms for Cancer Diagnosis: Recent Achievements and Challenges

With its fatal effects, cancer is still one of the most important diseases of today's world. The underlying fact behind this scenario is most probably due to its late diagnosis. That is why the necessity for the detection of different cancer types is obvious. Cancer studies including cancer diagnosis and therapy have been one of the most laborious tasks. Since its early detection significantly affects the following therapy steps, cancer diagnosis is very important. Despite researchers' best efforts, the accurate and rapid diagnosis of cancer is still challenging and difficult to investigate. It is known that electrochemical techniques have been successfully adapted into the cancer diagnosis field. Electrochemical sensor platforms that are brought together with the excellent selectivity of biosensing elements, such as nucleic acids, aptamers or antibodies, have put forth very successful outputs. One of the remarkable achievements of these biomolecule-attached sensors is their lack of need for additional labeling steps, which bring extra burdens such as interference effects or demanding modification protocols. In this review, we aim to outline label-free cancer diagnosis platforms that use electrochemical methods to acquire signals. The classification of the sensing platforms is generally presented according to their recognition element, and the most recent achievements by using these attractive sensing substrates are described in detail. In addition, the current challenges are discussed.

Non-enzymatic disposable electrochemical sensors based on CuO/Co3O4@MWCNTs nanocomposite modified screen-printed electrode for the direct determination of urea

A new electrochemical impedimetric sensor for direct detection of urea was designed and fabricated using nanostructured screen-printed electrodes (SPEs) modified with CuO/Co3O4 @MWCNTs. A facile and simple hydrothermal method was achieved for the chemical synthesis of the CuO/Co3O4 nanocomposite followed by the integration of MWCNTs to be the final platform of the urea sensor. A full physical and chemical characterization for the prepared nanomaterials were performed including Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), contact angle, scanning electron microscope (SEM) and transmission electron microscopy (TEM). Additionally, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were used to study the electrochemical properties the modified electrodes with the nanomaterials at different composition ratios of the CuO/Co3O4 or MWCNTs. The impedimetric measurements were optimized to reach a picomolar sensitivity and high selectivity for urea detection. From the calibration curve, the linear concentration range of 10-12-10-2 M was obtained with the regression coefficient (R2) of 0.9961 and lower detection limit of 0.223 pM (S/N = 5). The proposed sensor has been used for urea analysis in real samples. Thus, the newly developed non-enzymatic sensor represents a considerable advancement in the field for urea detection, owing to the simplicity, portability, and low cost-sensor fabrication.

A novel gallium oxide nanoparticles-based sensor for the simultaneous electrochemical detection of Pb2+, Cd2+ and Hg2+ ions in real water samples

Differential pulse voltammetry (DPV) using gallium oxide nanoparticles/carbon paste electrode (Ga₂O₃/CPE) was utilized for the simultaneous detection of Pb²⁺, Cd²⁺ and Hg²⁺ ions. Ga₂O₃NPs were chemically synthesized and fully characterized by Fourier-transform infrared (FTIR), X-ray diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Through the assay optimization, electrochemical screening of different nanomaterials was carried out using the cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) in order to determine the best electrode modifier that will be implemented for the present assay. Consequently, various parameters such as electrode matrix composition, electrolyte, deposition potential, and deposition time were optimized and discussed. Accordingly, the newly developed sensing platform showed a wide dynamic linear range of 0.3-80 µM with detection limits (LODs) of 84, 88 and 130 nM for Pb²⁺, Cd²⁺ and Hg²⁺ ions, respectively. While the corresponding limit of quantification (LOQ) values were 280, 320 and 450 nM. Sensors selectivity was investigated towards different non-targeting metal ions, whereas no obvious cross-reactivity was obtained. Eventually, applications on real samples were performed, while excellent recoveries for the multiple metal ions were successfully achieved.

Recent Advancements in Nanobiosensors: Current Trends, Challenges, Applications, and Future Scope

In recent years, there has been immense advancement in the development of nanobiosensors as these are a fundamental need of the hour that act as a potential candidate integrated with point-of-care-testing for several applications, such as healthcare, the environment, energy harvesting, electronics, and the food industry. Nanomaterials have an important part in efficiently sensing bioreceptors such as cells, enzymes, and antibodies to develop biosensors with high selectivity, peculiarity, and sensibility. It is virtually impossible in science and technology to perform any application without nanomaterials. Nanomaterials are distinguished from fine particles used for numerous applications as a result of being unique in properties such as electrical, thermal, chemical, optical, mechanical, and physical. The combination of nanostructured materials and biosensors is generally known as nanobiosensor technology. These miniaturized nanobiosensors are revolutionizing the healthcare domain for sensing, monitoring, and diagnosing pathogens, viruses, and bacteria. However, the conventional approach is time-consuming, expensive, laborious, and requires sophisticated instruments with skilled operators. Further, automating and integrating is quite a challenging process. Thus, there is a considerable demand for the development of nanobiosensors that can be used along with the POCT module for testing real samples. Additionally, with the advent of nano/biotechnology and the impact on designing portable ultrasensitive devices, it can be stated that it is probably one of the most capable ways of overcoming the aforementioned problems concerning the cumulative requirement for the development of a rapid, economical, and highly sensible device for analyzing applications within biomedical diagnostics, energy harvesting, the environment, food and water, agriculture, and the pharmaceutical industry.