Disposable all-printed electronic biosensor for instantaneous detection and classification of pathogens

Advancing Microfluidic Immunity Testing Systems: New Trends for Microbial Pathogen Detection

Pathogenic microorganisms play a crucial role in the global disease burden due to their ability to cause various diseases and spread through multiple transmission routes. Immunity tests identify antigens related to these pathogens, thereby confirming past infections and monitoring the host's immune response. Traditional pathogen detection methods, including enzyme-linked immunosorbent assays (ELISAs) and chemiluminescent immunoassays (CLIAs), are often labor-intensive, slow, and reliant on sophisticated equipment and skilled personnel, which can be limiting in resource-poor settings. In contrast, the development of microfluidic technologies presents a promising alternative, offering automation, miniaturization, and cost efficiency. These advanced methods are poised to replace traditional assays by streamlining processes and enabling rapid, high-throughput immunity testing for pathogens. This review highlights the latest advancements in microfluidic systems designed for rapid and high-throughput immunity testing, incorporating immunosensors, single molecule arrays (Simoas), a lateral flow assay (LFA), and smartphone integration. It focuses on key pathogenic microorganisms such as SARS-CoV-2, influenza, and the ZIKA virus (ZIKV). Additionally, the review discusses the challenges, commercialization prospects, and future directions to advance microfluidic systems for infectious disease detection.

Disposable electrochemical biosensors for the detection of bacteria in the light of antimicrobial resistance

Persistent and inappropriate use of antibiotics is causing rife antimicrobial resistance (AMR) worldwide. Common bacterial infections are thus becoming increasingly difficult to treat without the use of last resort antibiotics. This has necessitated a situation where it is imperative to confirm the infection to be bacterial, before treating it with antimicrobial speculatively. Conventional methods of bacteria detection are either culture based which take anywhere between 24 and 96 hor require sophisticated molecular analysis equipment with libraries and trained operators. These are difficult propositions for resource limited community healthcare setups of developing or less developed countries. Customized, inexpensive, point-of-care (PoC) biosensors are thus being researched and developed for rapid detection of bacterial pathogens. The development and optimization of disposable sensor substrates is the first and crucial step in development of such PoC systems. The substrates should facilitate easy charge transfer, a high surface to volume ratio, be tailorable by the various bio-conjugation chemistries, preserve the integrity of the biorecognition element, yet be inexpensive. Such sensor substrates thus need to be thoroughly investigated. Further, if such systems were made disposable, they would attain immunity to biofouling. This article discusses a few potential disposable electrochemical sensor substrates deployed for detection of bacteria for environmental and healthcare applications. The technologies have significant potential in helping reduce bacterial infections and checking AMR. This could help save lives of people succumbing to bacterial infections, as well as improve the overall quality of lives of people in low- and middle-income countries.

Machine Learning in Bioelectrocatalysis

At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning

Here, we provide a new methodology for reducing false results and time delay of biosensors, which are barriers to industrial, healthcare, military, and consumer applications. We show that integrating machine learning with domain knowledge in biosensing can complement and improve the biosensor accuracy and speed relative to the performance achieved by traditional regression analysis of a standard curve based on the biosensor steady-state response. The methodology was validated by rapid and accurate quantification of microRNA across the nanomolar to femtomolar range using the dynamic response of cantilever biosensors. Theory-guided feature engineering improved the performance and efficiency of several classification models relative to the performance achieved using traditional feature engineering methods (TSFRESH). In addition to the entire dynamic response, the technique enabled rapid and accurate quantification of the target analyte concentration and false-positive and false-negative results using the initial transient response, thereby reducing the required data acquisition time (i.e., time delay). We show that model explainability can be achieved by combining theory-guided feature engineering and feature importance analysis. The performance of multiple classifiers using both TSFRESH- and theory-based features from the biosensor's initial transient response was similar to that achieved using the entire dynamic response with data augmentation. We also show that the methodology can guide design of experiments for high-performance biosensing applications, specifically, the selection of data acquisition parameters (e.g., time) based on potential application-dependent performance thresholds. This work provides an example of the opportunities for improving biosensor performance, such as reducing biosensor false results and time delay, using explainable machine learning models supervised by domain knowledge in biosensing.

Highly-accurate solvent identification using dynamic evaporation reflection spectra from an inverse opal sensor combined with a deep learning model

Developing a low-cost, rapid, and highly accurate method for detecting solvents with similar structures and properties is highly demanded. In recent years, methods based on dynamic reflection spectroscopy have been developed to distinguish isomers and homologues. However, these methods heavily rely on responsive photonic crystals that can interact intricately with the solvent. In this work, we propose a deep learning approach for direct solvent identification from dynamic evaporative reflection spectra (DERS) obtained on a simple inverse opal (IO) sensor. The sensor was prepared using co-assembly and sacrificial template methods. Then, a dataset was constructed with 985 DERS obtained from 14 different solvents. Different classical machine learning and deep learning algorithms were employed for classifying these DERS. The results showed that ResNet18-CBAM, an improved convolutional neural network, outperformed all other algorithms, achieving 97.7 ± 0.9% on the 5-fold cross-validation set and 100% accuracy on the test set. This strategy presents not only a simple, efficient, and repeatable method for solvent detection but also, more importantly, by integrating the deep learning model, it allows an automatic, rapid, and accurate analysis of DERS data without the need for human intervention. It holds great application prospects in the field of solvent detection.

Recent Advances of Biosensors for Detection of Multiple Antibiotics

The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics.

Artificial intelligence-driven electrochemical immunosensing biochips in multi-component detection

Electrochemical Immunosensing (EI) combines electrochemical analysis and immunology principles and is characterized by its simplicity, rapid detection, high sensitivity, and specificity. EI has become an important approach in various fields, such as clinical diagnosis, disease prevention and treatment, environmental monitoring, and food safety. However, EI multi-component detection still faces two major bottlenecks: first, the lack of cost-effective and portable detection platforms; second, the difficulty in eliminating batch differences and accurately decoupling signals from multiple analytes. With the gradual maturation of biochip technology, high-throughput analysis and portable detection utilizing the advantages of miniaturized chips, high sensitivity, and low cost have become possible. Meanwhile, Artificial Intelligence (AI) enables accurate decoupling of signals and enhances the sensitivity and specificity of multi-component detection. We believe that by evaluating and analyzing the characteristics, benefits, and linkages of EI, biochip, and AI technologies, we may considerably accelerate the development of EI multi-component detection. Therefore, we propose three specific prospects: first, AI can enhance and optimize the performance of the EI biochips, addressing the issue of multi-component detection for portable platforms. Second, the AI-enhanced EI biochips can be widely applied in home care, medical healthcare, and other areas. Third, the cross-fusion and innovation of EI, biochip, and AI technologies will effectively solve key bottlenecks in biochip detection, promoting interdisciplinary development. However, challenges may arise from AI algorithms that are difficult to explain and limited data access. Nevertheless, we believe that with technological advances and further research, there will be more methods and technologies to overcome these challenges.

Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.

A Short Review on Miniaturized Biosensors for the Detection of Nucleic Acid Biomarkers

Even today, most biomarker testing is executed in centralized, dedicated laboratories using bulky instruments, automated analyzers, and increased analysis time and expenses. The development of miniaturized, faster, low-cost microdevices is immensely anticipated for substituting for these conventional laboratory-oriented assays and transferring diagnostic results directly onto the patient's smartphone using a cloud server. Pioneering biosensor-based approaches might make it possible to test biomarkers with reliability in a decentralized setting, but there are still a number of issues and restrictions that must be resolved before the development and use of several biosensors for the proper understanding of the measured biomarkers of numerous bioanalytes such as DNA, RNA, urine, and blood. One of the most promising processes to address some of the issues relating to the growing demand for susceptible, quick, and affordable analysis techniques in medical diagnostics is the creation of biosensors. This article critically discusses a short review of biosensors used for detecting nucleic acid biomarkers, and their use in biomedical prognostics will be addressed while considering several essential characteristics.

Machine learning toward high-performance electrochemical sensors

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.

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.

Biofilms: Formation, Research Models, Potential Targets, and Methods for Prevention and Treatment

Due to the continuous rise in biofilm-related infections, biofilms seriously threaten human health. The formation of biofilms makes conventional antibiotics ineffective and dampens immune clearance. Therefore, it is important to understand the mechanisms of biofilm formation and develop novel strategies to treat biofilms more effectively. This review article begins with an introduction to biofilm formation in various clinical scenarios and their corresponding therapy. Established biofilm models used in research are then summarized. The potential targets which may assist in the development of new strategies for combating biofilms are further discussed. The novel technologies developed recently for the prevention and treatment of biofilms including antimicrobial surface coatings, physical removal of biofilms, development of new antimicrobial molecules, and delivery of antimicrobial agents are subsequently presented. Finally, directions for future studies are pointed out.

Biosensors and Microfluidic Biosensors: From Fabrication to Application

Biosensors are ubiquitous in a variety of disciplines, such as biochemical, electrochemical, agricultural, and biomedical areas. They can integrate various point-of-care applications, such as in the food, healthcare, environmental monitoring, water quality, forensics, drug development, and biological domains. Multiple strategies have been employed to develop and fabricate miniaturized biosensors, including design, optimization, characterization, and testing. In view of their interactions with high-affinity biomolecules, they find application in the sensitive detection of analytes, even in small sample volumes. Among the many developed techniques, microfluidics have been widely explored; these use fluid mechanics to operate miniaturized biosensors. The currently used commercial devices are bulky, slow in operation, expensive, and require human intervention; thus, it is difficult to automate, integrate, and miniaturize the existing conventional devices for multi-faceted applications. Microfluidic biosensors have the advantages of mobility, operational transparency, controllability, and stability with a small reaction volume for sensing. This review addresses biosensor technologies, including the design, classification, advances, and challenges in microfluidic-based biosensors. The value chain for developing miniaturized microfluidic-based biosensor devices is critically discussed, including fabrication and other associated protocols for application in various point-of-care testing applications.

Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors

We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH4+ ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH4+ ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH4+ concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH4+ ion levels. The proposed NH4+ sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.

Machine Learning-Reinforced Noninvasive Biosensors for Healthcare

The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health-related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning-reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning-reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning-reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real-time monitoring, out-of-clinic diagnosis, and onsite food safety detection are proposed.

Flexible electrochemical biosensors for healthcare monitoring

As the interest in wearable devices has increased recently, increasing biosensor flexibility has begun to attract considerable attention. Among the various types of biosensors, electrochemical biosensors are uniquely suited for the development of such flexible biosensors due to their many advantages, including their fast response, inherent miniaturization, convenient operation, and portability. Therefore, many studies on flexible electrochemical biosensors have been conducted in recent years to achieve non-invasive and real-time monitoring of body fluids such as tears, sweat, and saliva. To achieve this, various substrates, novel nanomaterials, and detection techniques have been utilized to develop conductive flexible platforms that can be applied to create flexible electrochemical biosensors. In this review, we discussed recently reported flexible electrochemical biosensors and divided them into specific categories including materials for flexible substrate, fabrication techniques for flexible biosensor development, and recently developed flexible electrochemical biosensors to externally monitor target molecules, thereby providing a means to noninvasively examine cells and body fluid samples. In conclusion, this review will discuss the materials, methods, recent studies, and perspectives on flexible electrochemical biosensors for healthcare monitoring and wearable biosensing systems.

Diversity and functional landscapes in the microbiota of animals in the wild

Animals in the wild are able to subsist on pathogen-infected and poisonous food and show immunity to various diseases. These may be due to their microbiota, yet we have a poor understanding of animal microbial diversity and function. We used metagenomics to analyze the gut microbiota of more than 180 species in the wild, covering diverse classes, feeding behaviors, geographies, and traits. Using de novo metagenome assembly, we constructed and functionally annotated a database of more than 5000 genomes, comprising 1209 bacterial species of which 75% are unknown. The microbial composition, diversity, and functional content exhibit associations with animal taxonomy, diet, activity, social structure, and life span. We identify the gut microbiota of wild animals as a largely untapped resource for the discovery of therapeutics and biotechnology applications.

Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors

Biosensors are essential tools which have been traditionally used to monitor environmental pollution and detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication-enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to, potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization-induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors, as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology-enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications. The article aims to provide the reader a holistic overview of different nanostructures which have been used for biosensing purposes along with some specific applications in the field of cancer detection and the Internet of things (IoT), as well as a brief overview of machine-learning-based biosensing.

Advancing Biosensors with Machine Learning

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

Newly Developed System for the Robust Detection of Listeria monocytogenes Based on a Bioelectric Cell Biosensor

Human food-borne diseases caused by pathogenic bacteria have been significantly increased in the last few decades causing numerous deaths worldwide. The standard analyses used for their detection have significant limitations regarding cost, special facilities and equipment, highly trained staff, and a long procedural time that can be crucial for foodborne pathogens with high hospitalization and mortality rates, such as Listeria monocytogenes. This study aimed to develop a biosensor that could detect L. monocytogenes rapidly and robustly. For this purpose, a cell-based biosensor technology based on the Bioelectric Recognition Assay (BERA) and a portable device developed by EMBIO Diagnostics, called B.EL.D (Bio Electric Diagnostics), were used. Membrane engineering was performed by electroinsertion of Listeria monocytogenes homologous antibodies into the membrane of African green monkey kidney (Vero) cells. The newly developed biosensor was able to detect the pathogen's presence rapidly (3 min) at concentrations as low as 102 CFU mL-1, demonstrating a higher sensitivity than most existing biosensor-based methods. In addition, lack of cross-reactivity with other Listeria species, as well as with Escherichia coli, was shown, thus, indicating biosensor's significant specificity against L. monocytogenes.

Nano-enabled sensing approaches for pathogenic bacterial detection

Infectious diseases caused by pathogenic bacteria, especially antibiotic-resistant bacteria, are one of the biggest threats to global health. To date, bacterial contamination is detected using conventional culturing techniques, which are highly dependent on expert users, limited by the processing time and on-site availability. Hence, real-time and continuous monitoring of pathogen levels is required to obtain valuable information that could assist health agencies in guiding prevention and containment of pathogen-related outbreaks. Nanotechnology-based smart sensors are opening new avenues for early and rapid detection of such pathogens at the patient's point-of-care. Nanomaterials can play an essential role in bacterial sensing owing to their unique optical, magnetic, and electrical properties. Carbon nanoparticles, metallic nanoparticles, metal oxide nanoparticles, and various types of nanocomposites are examples of smart nanomaterials that have drawn intense attention in the field of microbial detection. These approaches, together with the advent of modern technologies and coupled with machine learning and wireless communication, represent the future trend in the diagnosis of infectious diseases. This review provides an overview of the recent advancements in the successful harnessing of different nanoparticles for bacterial detection. In the beginning, we have introduced the fundamental concepts and mechanisms behind the design and strategies of the nanoparticles-based diagnostic platform. Representative research efforts are highlighted for in vitro and in vivo detection of bacteria. A comprehensive discussion is then presented to cover the most commonly adopted techniques for bacterial identification, including some seminal studies to detect bacteria at the single-cell level. Finally, we discuss the current challenges and a prospective outlook on the field, together with the recommended solutions.

Recent development of advanced biotechnology for wastewater treatment

The importance of highly efficient wastewater treatment is evident from aggravated water crises. With the development of green technology, wastewater treatment is required in an eco-friendly manner. Biotechnology is a promising solution to address this problem, including treatment and monitoring processes. The main directions and differences in biotreatment process are related to the surrounding environmental conditions, biological processes, and the type of microorganisms. It is significant to find suitable biotreatment methods to meet the specific requirements for practical situations. In this review, we first provide a comprehensive overview of optimized biotreatment processes for treating wastewater during different conditions. Both the advantages and disadvantages of these biotechnologies are discussed at length, along with their application scope. Then, we elaborated on recent developments of advanced biosensors (i.e. optical, electrochemical, and other biosensors) for monitoring processes. Finally, we discuss the limitations and perspectives of biological methods and biosensors applied in wastewater treatment. Overall, this review aims to project a rapid developmental path showing a broad vision of recent biotechnologies, applications, challenges, and opportunities for scholars in biotechnological fields for "green" wastewater treatment.

Disposable Sensors in Diagnostics, Food, and Environmental Monitoring

Disposable sensors are low-cost and easy-to-use sensing devices intended for short-term or rapid single-point measurements. The growing demand for fast, accessible, and reliable information in a vastly connected world makes disposable sensors increasingly important. The areas of application for such devices are numerous, ranging from pharmaceutical, agricultural, environmental, forensic, and food sciences to wearables and clinical diagnostics, especially in resource-limited settings. The capabilities of disposable sensors can extend beyond measuring traditional physical quantities (for example, temperature or pressure); they can provide critical chemical and biological information (chemo- and biosensors) that can be digitized and made available to users and centralized/decentralized facilities for data storage, remotely. These features could pave the way for new classes of low-cost systems for health, food, and environmental monitoring that can democratize sensing across the globe. Here, a brief insight into the materials and basics of sensors (methods of transduction, molecular recognition, and amplification) is provided followed by a comprehensive and critical overview of the disposable sensors currently used for medical diagnostics, food, and environmental analysis. Finally, views on how the field of disposable sensing devices will continue its evolution are discussed, including the future trends, challenges, and opportunities.

Emerging Analytical Techniques for Rapid Pathogen Identification and Susceptibility Testing

In the face of looming threats from multi-drug resistant microorganisms, there is a growing need for technologies that will enable rapid identification and drug susceptibility profiling of these pathogens in health care settings. In particular, recent progress in microfluidics and nucleic acid amplification is pushing the boundaries of timescale for diagnosing bacterial infections. With a diverse range of techniques and parallel developments in the field of analytical chemistry, an integrative perspective is needed to understand the significance of these developments. This review examines the scope of new developments in assay technologies grouped by key enabling domains of research. First, we examine recent development in nucleic acid amplification assays for rapid identification and drug susceptibility testing in bacterial infections. Next, we examine advances in microfluidics that facilitate acceleration of diagnostic assays via integration and scale. Lastly, recentdevelopments in biosensor technologies are reviewed. We conclude this review with perspectives on the use of emerging concepts to develop paradigm-changing assays.

Controlling parameters and characteristics of electrochemical biosensors for enhanced detection of 8-hydroxy-2'-deoxyguanosine

This work discusses the parameters and characteristics required on the development of a scalable and reliable electrochemical sensor board for detecting 8-hydroxy-2′-deoxyguanosine (8-OHdG), an oxidative stress biomarker for diabetic nephropathy, cancer and Parkinson’s disease. We used Printed Circuit Board (PCB) technology to make a precise, low-cost bare sensor board. ZnO nanorods (NRs) and ZnO NRs: reduced graphene oxide (RGO) composites were used as a pathway for antibody immobilization on the working electrode (WE). The parameters and characteristics of the WE were controlled for enhancing the quality of the electrochemical sensor board. Thickness of the gold and the presence of ZnO NRs or their composite on the WE have influence on charge transference process and reproducibility of the sensor board. The amount of the antibody, and its incubation period are crucial to avoid saturation of the sites during immobilization step and reduce the cost of the sensor. Our ZnO NRs-based electrochemical sensor board showed high sensitivity and selectivity to 8-OHdG with detection capacity in the range of 0.001–5.00 ng.mL⁻¹. The successful application of our immunosensor to detect 8-OHdG in urine was evidenced.