Hierarchical Cluster Analysis of Medical Chemicals Detected by a Bacteriophage-Based Colorimetric Sensor Array

Recent Trends of Bioanalytical Sensors with Smart Health Monitoring Systems: From Materials to Applications

Smart biosensors attract significant interest due to real-time monitoring of user health status, where bioanalytical electronic devices designed to detect various activities and biomarkers in the human body have potential applications in physical sign monitoring and health care. Bioelectronics can be well integrated by output signals with wireless communication modules for transferring data to portable devices used as smart biosensors in performing real-time diagnosis and analysis. In this review, the scientific keys of biosensing devices and the current trends in the field of smart biosensors, (functional materials, technological approaches, sensing mechanisms, main roles, potential applications and challenges in health monitoring) will be summarized. Recent advances in the design and manufacturing of bioanalytical sensors with smarter capabilities and enhanced reliability indicate a forthcoming expansion of these smart devices from laboratory to clinical analysis. Therefore, a general description of functional materials and technological approaches used in bioelectronics will be presented after the sections of scientific keys to bioanalytical sensors. A careful introduction to the established systems of smart monitoring and prediction analysis using bioelectronics, regarding the integration of machine-learning-based basic algorithms, will be discussed. Afterward, applications and challenges in development using these smart bioelectronics in biological, clinical, and medical diagnostics will also be analyzed. Finally, the review will conclude with outlooks of smart biosensing devices assisted by machine learning algorithms, wireless communications, or smartphone-based systems on current trends and challenges for future works in wearable health monitoring.

Sensor development for multiple simultaneous classifications using genetically engineered M13 bacteriophages

Sensors for detecting infinitesimal amounts of chemicals in air have been widely developed because they can identify the origin of chemicals. These sensing technologies are also used to determine the variety and freshness of fresh food and detect explosives, hazardous chemicals, environmental hormones, and diseases using exhaled gases. However, there is still a need to rapidly develop portable and highly sensitive sensors that respond to complex environments. Here, we show an efficient method for optimising an M13 bacteriophage-based multi-array colourimetric sensor for multiple simultaneous classifications. Apples, which are difficult to classify due to many varieties in distribution, were selected for classifying targets. M13 was adopted to fabricate a multi-array colourimetric sensor using the self-templating process since a chemical property of major coat protein p8 consisting of the M13 body can be manipulated by genetic engineering to respond to various target substances. The twenty sensor units, which consisted of different types of manipulated M13, exhibited colour changes because of the change of photonic crystal-like nanostructure when they were exposed to target substances associated with apples. The classification success rate of the optimal sensor combinations was achieved with high accuracy for the apple variety (100%), four standard fragrances (100%), and aging (84.5%) simultaneously. We expect that this optimisation technique can be used for rapid sensor development capable of multiple simultaneous classifications in various fields, such as medical diagnosis, hazardous environment monitoring, and the food industry, where sensors need to be developed in response to complex environments consisting of various targets.

Calligraphy of Nanoplasmonic Bioink-Based Multiplex Immunosensor for Precision Immune Monitoring and Modulation

Immunomodulation therapies have attracted immense interest recently for the treatment of immune-related diseases, such as cancer and viral infections. This new wave of enthusiasm for immunomodulators, predominantly revolving around cytokines, has spurred emerging needs and opportunities for novel immune monitoring and diagnostic tools. Considering the highly dynamic immune status and limited window for therapeutic intervention, precise real-time detection of cytokines is critical to effectively monitor and manage the immune system and optimize the therapeutic outcome. The clinical success of such a rapid, sensitive, multiplex immunoanalytical platform further requires the system to have ease of integration and fabrication for sample sparing and large-scale production toward massive parallel analysis. In this article, we developed a nanoplasmonic bioink-based, label-free, multiplex immunosensor that can be readily "written" onto a glass substrate via one-step calligraphy patterning. This facile nanolithography technique allows programmable patterning of a minimum of 3 μL of nanoplasmonic bioink in 1 min and thus enables fabrication of a nanoplasmonic microarray immunosensor with 2 h simple incubation. The developed immunosensor was successfully applied for real-time, parallel detection of multiple cytokines (e.g., interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and transforming growth factor-beta (TGF-β)) in immunomodulated macrophage samples. This integrated platform synergistically incorporates the concepts of nanosynthesis, nanofabrication, and nanobiosensing, showing great potential in the scalable production of label-free multiplex immunosensing devices with superior analytical performance for clinical applications in immunodiagnostics and immunotherapy.

Virus-Based Pyroelectricity

The first observation of heat-induced electrical potential generation on a virus and its detection through pyroelectricity are presented. Specifically, the authors investigate the pyroelectric properties of the M13 phage, which possesses inherent dipole structures derived from the noncentrosymmetric arrangement of the major coat protein (pVIII) with an α-helical conformation. Unidirectional polarization of the phage is achieved through genetic engineering of the tail protein (pIII) and template-assisted self-assembly techniques. By modifying the pVIII proteins with varying numbers of glutamate residues, the structure-dependent tunable pyroelectric properties of the phage are explored. The most polarized phage exhibits a pyroelectric coefficient of 0.13 µC m-2 °C-1 . Computational modeling and circular dichroism (CD) spectroscopy analysis confirm that the unfolding of α-helices within the pVIII proteins leads to changes in phage polarization upon heating. Moreover, the phage is genetically modified to enable its pyroelectric function in diverse chemical environments. This phage-based approach not only provides valuable insights into bio-pyroelectricity but also opens up new opportunities for the detection of various viral particles. Furthermore, it holds great potential for the development of novel biomaterials for future applications in biosensors and bioelectric materials.

Artificial Neural Processing-Driven Bioelectronic Nose for the Diagnosis of Diabetes and Its Complications

Diabetes and its complications affect the younger population and are associated with a high mortality rate; however, early diagnosis can contribute to the selection of appropriate treatment regimens that can reduce mortality. Although diabetes diagnosis via exhaled breath has great potential for early diagnosis, research on such diagnosis is restricted to disease detection, requiring in-depth examination to diagnose and classify diseases and their complications. This study demonstrates the use of an artificial neural processing-based bioelectronic nose to accurately diagnose diabetes and classify diabetic types (type I and II) and their complications, such as heart disease. Specifically, an M13 phage-based electronic nose (e-nose) is used to explore the features of subjects with diabetes at various levels of cellular and organismal organization (cells, liver organoids, and mice). Exhaled breath samples are collected during culturing and exposed to the phage-based e-nose. Compared with cells, liver organoids cultured under conditions mimicking a diabetic environment display properties that closely resemble the characteristics of diabetic mice. Using neural pattern separation, the M13 phage-based e-nose achieves a classification success rate of over 86% for four conditions in mice, namely, type 1 diabetes, type 2 diabetes, diabetic cardiomyopathy, and cardiomyopathy.

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.

The development progress of multi-array colourimetric sensors based on the M13 bacteriophage

Techniques for detecting chemicals dispersed at low concentrations in air continue to evolve. These techniques can be applied not only to manage the quality of agricultural products using a post-ripening process but also to establish a safety prevention system by detecting harmful gases and diagnosing diseases. Recently, techniques for rapid response to various chemicals and detection in complex and noisy environments have been developed using M13 bacteriophage-based sensors. In this review, M13 bacteriophage-based multi-array colourimetric sensors for the development of an electronic nose is discussed. The self-templating process was adapted to fabricate a colour band structure consisting of an M13 bacteriophage. To detect diverse target chemicals, the colour band was utilised with wild and genetically engineered M13 bacteriophages to enhance their sensing abilities. Multi-array colourimetric sensors were optimised for application in complex and noisy environments based on simulation and deep learning analysis. The development of a multi-array colourimetric sensor platform based on the M13 bacteriophage is likely to result in significant advances in the detection of various harmful gases and the diagnosis of various diseases based on exhaled gas in the future.

Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review

Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.

Neural mechanism mimetic selective electronic nose based on programmed M13 bacteriophage

The electronic nose is a reliable practical sensor device that mimics olfactory organs. Although numerous studies have demonstrated excellence in detecting various target substances with the help of ideal models, biomimetic approaches still suffer in practical realization because of the inability to mimic the signal processing performed by olfactory neural systems. Herein, we propose an electronic nose based on the programable surface chemistry of M13 bacteriophage, inspired by the neural mechanism of the mammalian olfactory system. The neural pattern separation (NPS) was devised to apply the pattern separation that operates in the memory and learning process of the brain to the electronic nose. We demonstrate an electronic nose in a portable device form, distinguishing polycyclic aromatic compounds (harmful in living environment) in an atomic-level resolution (97.5% selectivity rate) for the first time. Our results provide practical methodology and inspiration for the second-generation electronic nose development toward the performance of detection dogs (K9).

Recent Trends in Exhaled Breath Diagnosis Using an Artificial Olfactory System

Artificial olfactory systems are needed in various fields that require real-time monitoring, such as healthcare. This review introduces cases of detection of specific volatile organic compounds (VOCs) in a patient's exhaled breath and discusses trends in disease diagnosis technology development using artificial olfactory technology that analyzes exhaled human breath. We briefly introduce algorithms that classify patterns of odors (VOC profiles) and describe artificial olfactory systems based on nanosensors. On the basis of recently published research results, we describe the development trend of artificial olfactory systems based on the pattern-recognition gas sensor array technology and the prospects of application of this technology to disease diagnostic devices. Medical technologies that enable early monitoring of health conditions and early diagnosis of diseases are crucial in modern healthcare. By regularly monitoring health status, diseases can be prevented or treated at an early stage, thus increasing the human survival rate and reducing the overall treatment costs. This review introduces several promising technical fields with the aim of developing technologies that can monitor health conditions and diagnose diseases early by analyzing exhaled human breath in real time.

Application of Bacteriophages in Nanotechnology

Bacteriophages (phages for short) are viruses, which have bacteria as hosts. The single phage body virion, is a colloidal particle, often possessing a dipole moment. As such, phages were used as perfectly monodisperse systems to study various physicochemical phenomena (e.g., transport or sedimentation in complex fluids), or in the material science (e.g., as scaffolds). Nevertheless, phages also execute the life cycle to multiply and produce progeny virions. Upon completion of the life cycle of phages, the host cells are usually destroyed. Natural abilities to bind to and kill bacteria were a starting point for utilizing phages in phage therapies (i.e., medical treatments that use phages to fight bacterial infections) and for bacteria detection. Numerous applications of phages became possible thanks to phage display-a method connecting the phenotype and genotype, which allows for selecting specific peptides or proteins with affinity to a given target. Here, we review the application of bacteriophages in nanoscience, emphasizing bio-related applications, material science, soft matter research, and physical chemistry.

Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks

Internet of Things (IoT) projects are increasing in size over time, and some of them are growing to reach the whole world. Sensor arrays are deployed world-wide and their data is sent to the cloud, making use of the Internet. These huge networks can be used to improve the quality of life of the humanity by continuously monitoring many useful indicators, like the health of the users, the air quality or the population movements. Nevertheless, in this scalable context, a percentage of the sensor data readings can fail due to several reasons like sensor reliabilities, network quality of service or extreme weather conditions, among others. Moreover, sensors are not homogeneously replaced and readings from some areas can be more precise than others. In order to address this problem, in this paper we propose to use collaborative filtering techniques to predict missing readings, by making use of the whole set of collected data from the IoT network. State of the art recommender systems methods have been chosen to accomplish this task, and two real sensor array datasets and a synthetic dataset have been used to test this idea. Experiments have been carried out varying the percentage of failed sensors. Results show a good level of prediction accuracy which, as expected, decreases as the failure rate increases. Results also point out a failure rate threshold below which is better to make use of memory-based approaches, and above which is better to choose model-based methods.

Phage-Based Sensors in Medicine: A Review

Bacteriophages are interesting entities on the border of biology and chemistry. In nature, they are bacteria parasites, while, after genetic manipulation, they gain new properties, e.g., selectively binding proteins. Owing to this, they may be applied as recognition elements in biosensors. Combining bacteriophages with different transducers can then result in the development of innovative sensor designs that may revolutionize bioanalytics and improve the quality of medical services. Therefore, here, we review the use of bacteriophages, or peptides from bacteriophages, as new sensing elements for the recognition of biomarkers and the construction of the highly effective diagnostics tools.

Washable Colorimetric Nanofiber Nonwoven for Ammonia Gas Detection

The colorimetric sensor is a facile, cost-effective, and non-power-operated green energy material for gas detection. In this study, the colorimetric sensing property of a meta-aramid/dye 3 nanofiber sensor for ammonia (NH₃) gas detection was investigated. This colorimetric sensor was prepared using various dye 3 concentrations via electrospinning. Morphological, thermal, structural, and mechanical analyses of the sensor were carried out by field-emission scanning electron microscopy, thermogravimetric analysis, Fourier-transform infrared spectroscopy, and a universal testing machine, respectively. A homemade computer color matching machine connected with a gas flow device characterized the response of the meta-aramid/dye 3 nanofiber colorimetric sensor to various exposure levels of NH₃ gas. From the results, we confirmed that this colorimetric green energy sensor could detect ammonia gas in the concentration of 1-10 ppm with a sensing response time of 10 s at room temperature. After washing with laundry detergent for 30 min, the colorimetric sensors still exhibited sensing property and reversibility.

Virus-Based Nanomaterials and Nanostructures

This Special Issue highlights the recent developments and future directions of virus-based nanomaterials and nanostructures in energy and biomedical applications. The virus-based biomimetic materials formulated using innovative ideas presented herein are characterized for the applications of biosensors and nanocarriers. The research contributions and trends based on virus-based materials, covering energy-harvesting devices to tissue regeneration over the last two decades, are described and discussed.