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Feature Papers in Biosensors Section 2022
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Feature Papers in Biosensors Section 2022

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 45196

Special Issue Editors

Prof. Dr. Huangxian Ju

E-Mail Website
Guest Editor
State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
Interests: immunosensors; electrochemical sensors; chemically modified electrodes; biosensors; electroanalysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicole Jaffrezic-Renault

E-Mail Website
Guest Editor
Institute of Analytical Sciences, UMR CNRS 5280, Department LSA, 5 Rue de La Doua, 69100 Villeurbanne, France
Interests: biosensors; impedance; immunosensors; conductometric sensors; enzymatic sensors; affinity sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the journal section Biosensors is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our journal and outstanding scholars in this research field. We welcome contributions, as well as recommendations, from the EBMs.

The purpose of this Special Issue is to publish a set of papers that typify the most exceptional, insightful, influential and original research articles or reviews, where our section’s EBMs discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collated into a printed edition book after the deadline and will be well promoted.

We would also like to take this opportunity to call on more scholars to join the journal section Biosensors so that we can work together to further develop this exciting field of research. Potential topics include but are not limited to the following:

  • biosensors;
  • lab-on-a-chip technology;
  • optical biosensors;
  • plasmonic biosensors;
  • biosensors for cell analysis;
  • electrochemical biosensor;
  • enzymatic biosensors;
  • graphene-based biosensors;
  • carbon nanotube biosensors;
  • aptamer biosensors;
  • DNA/RNA sensors;
  • glucose biosensor;
  • capacitive biosensors;
  • biosensor for toxin detection;
  • implantable biosensors;
  • microwave biosensors;
  • biosensor and bioelectronic devices;
  • nucleic acid sensors;
  • protein-based biosensors;
  • immunosensors;
  • biological electrode;
  • magnetic-based sensors;
  • biosensors of bacterial cells.

Prof. Dr. Huangxian Ju
Prof. Dr. Nicole Jaffrezic-Renault
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biosensors
  • lab-on-a-chip technology
  • optical biosensors
  • plasmonic biosensors
  • biosensors for cell analysis
  • electrochemical biosensor
  • enzymatic biosensors
  • graphene-based biosensors
  • carbon nanotube biosensors
  • aptamer biosensors
  • DNA/RNA sensors
  • glucose biosensor
  • capacitive biosensors
  • biosensor for toxin detection
  • implantable biosensors
  • microwave biosensors
  • biosensor and bioelectronic devices
  • nucleic acid sensors
  • protein-based biosensors
  • immunosensors
  • biological electrode
  • magnetic-based sensors
  • biosensors of bacterial cells.

Related Special Issue

Published Papers (17 papers)

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Editorial

Jump to: Research, Review

2 pages, 167 KiB  
Editorial
Special Issue “Feature Papers in Biosensors Section 2022”
by Huangxian Ju and Nicole Jaffrezic-Renault
Sensors 2023, 23(7), 3704; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073704 - 03 Apr 2023
Viewed by 849
Abstract
Biosensors are devices composed of a biorecognition part and of a transduction part [...] Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)

Research

Jump to: Editorial, Review

18 pages, 6153 KiB  
Article
An Electrochemical Sensor for Sulfadiazine Determination Based on a Copper Nanoparticles/Molecularly Imprinted Overoxidized Polypyrrole Composite
by Manahil Babiker Elamin, Shazalia Mahmoud Ahmed Ali, Houda Essousi, Amani Chrouda, Laila M. Alhaidari, Nicole Jaffrezic-Renault and Houcine Barhoumi
Sensors 2023, 23(3), 1270; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031270 - 22 Jan 2023
Cited by 3 | Viewed by 2304
Abstract
To protect consumers from risks related to overexposure to sulfadiazine, total residues of this antibacterial agent in animal-origin foodstuffs not exceed international regulations. To this end, a new electrochemical sensor based on a molecularly imprinted polymer nanocomposite using overoxidized polypyrrole and copper nanoparticles [...] Read more.
To protect consumers from risks related to overexposure to sulfadiazine, total residues of this antibacterial agent in animal-origin foodstuffs not exceed international regulations. To this end, a new electrochemical sensor based on a molecularly imprinted polymer nanocomposite using overoxidized polypyrrole and copper nanoparticles for the detection of sulfadiazine is elaborated. After optimization of the preparation of the electrochemical sensors, their differential pulse voltammetric signal exhibits an excellent stability and reproducibility at 1.05 V, with a large linear range between 10−9 and 10−5 mol L−1 and a low detection limit of 3.1 × 10−10 mol L−1. The produced sulfadiazine sensor was successfully tested in real milk samples. The combination of the properties of the electrical conduction of copper nanoparticles with the properties of the preconcentration of the molecularly imprinted overoxidized polypyrrole allows for the highly sensitive detection of sulfadiazine, even in real milk samples. This strategy is new and leads to the lowest detection limit yet achieved, compared to those of the previously published sulfadiazine electrochemical sensors. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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11 pages, 3144 KiB  
Article
Investigation of GOx Stability in a Chitosan Matrix: Applications for Enzymatic Electrodes
by Ayman Chmayssem, Ibrahim Shalayel, Stéphane Marinesco and Abdelkader Zebda
Sensors 2023, 23(1), 465; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010465 - 01 Jan 2023
Cited by 4 | Viewed by 2314
Abstract
In this study, we designed a new biosensing membrane for the development of an electrochemical glucose biosensor. To proceed, we used a chitosan-based hydrogel that entraps glucose oxidase enzyme (GOx), and we crosslinked the whole matrix using glutaraldehyde, which is known for its [...] Read more.
In this study, we designed a new biosensing membrane for the development of an electrochemical glucose biosensor. To proceed, we used a chitosan-based hydrogel that entraps glucose oxidase enzyme (GOx), and we crosslinked the whole matrix using glutaraldehyde, which is known for its quick and reactive crosslinking behavior. Then, the stability of the designed biosensors was investigated over time, according to different storage conditions (in PBS solution at temperatures of 4 °C and 37 °C and in the presence or absence of glucose). In some specific conditions, we found that our biosensor is capable of maintaining its stability for more than six months of storage. We also included catalase to protect the biosensing membranes from the enzymatic reaction by-products (e.g., hydrogen peroxide). This design protects the biocatalytic activity of GOx and enhances the lifetime of the biosensor. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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17 pages, 1767 KiB  
Article
Mediator Microbial Biosensor Analyzers for Rapid Determination of Surface Water Toxicity
by Anna Kharkova, Vyacheslav Arlyapov, Anastasia Medvedeva, Roman Lepikash, Pavel Melnikov and Anatoly Reshetilov
Sensors 2022, 22(21), 8522; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218522 - 05 Nov 2022
Cited by 3 | Viewed by 1611
Abstract
Microbial mediator biosensors for surface water toxicity determination make it possible to carry out an early assessment of the environmental object’s quality without time-consuming standard procedures based on standard test-organisms, and provide broad opportunities for receptor element modifying depending on the required operational [...] Read more.
Microbial mediator biosensors for surface water toxicity determination make it possible to carry out an early assessment of the environmental object’s quality without time-consuming standard procedures based on standard test-organisms, and provide broad opportunities for receptor element modifying depending on the required operational parameters analyzer. Four microorganisms with broad substrate specificity and nine electron acceptors were used to form a receptor system for toxicity assessment. Ferrocene was the most effective mediator according to its high rate constant of interaction with the microorganisms (0.33 ± 0.01 dm3/(g × s) for yeast Saccharomyces cerevisiae). Biosensors were tested on samples containing four heavy metal ions (Cu2+, Zn2+, Pb2+, Cd2+), two phenols (phenol and p-nitrophenol), and three natural water samples. The «ferrocene- Escherichia coli» and «ferrocene-Paracoccus yeei, E. coli association» systems showed good operational stability with a relative standard deviation of 6.9 and 7.3% (14 measurements) and a reproducibility of 7 and 5.2% using copper (II) ions as a reference toxicant. Biosensor analysis with these systems was shown to highly correlate with the results of the standard method using Chlorella algae as a test object. Developed biosensors allow for a valuation of the polluted natural water’s impact on the ecosystem via an assessment of the influence on bacteria and yeast in the receptor system. The systems could be used in toxicological monitoring of natural waters. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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Graphical abstract

24 pages, 7622 KiB  
Article
Classification of Drivers’ Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals
by Daniela Cardone, David Perpetuini, Chiara Filippini, Lorenza Mancini, Sergio Nocco, Michele Tritto, Sergio Rinella, Alberto Giacobbe, Giorgio Fallica, Fabrizio Ricci, Sabina Gallina and Arcangelo Merla
Sensors 2022, 22(19), 7300; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197300 - 26 Sep 2022
Cited by 14 | Viewed by 2033
Abstract
Mental workload (MW) represents the amount of brain resources required to perform concurrent tasks. The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test—DST [...] Read more.
Mental workload (MW) represents the amount of brain resources required to perform concurrent tasks. The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test—DST and Ray Auditory Verbal Learning Test—RAVLT) were administered to participants while driving in a simulated environment. The tests were chosen to investigate the drivers’ response to predefined levels of cognitive load to categorize the classes of MW. Infrared (IR) thermal imaging concurrently with heart rate variability (HRV) were used to obtain features related to the psychophysiology of the subjects, in order to feed machine learning (ML) classifiers. Six categories of models have been compared basing on unimodal IR/unimodal HRV/multimodal IR + HRV features. The best classifier performances were reached by the multimodal IR + HRV features-based classifiers (DST: accuracy = 73.1%, sensitivity = 0.71, specificity = 0.69; RAVLT: accuracy = 75.0%, average sensitivity = 0.75, average specificity = 0.87). The unimodal IR features based classifiers revealed high performances as well (DST: accuracy = 73.1%, sensitivity = 0.73, specificity = 0.73; RAVLT: accuracy = 71.1%, average sensitivity = 0.71, average specificity = 0.85). These results demonstrated the possibility to assess drivers’ MW levels with high accuracy, also using a completely non-contact and non-invasive technique alone, representing a key advancement with respect to the state of the art in traffic accident prevention. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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12 pages, 2178 KiB  
Article
Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics
by Clement Ogugua Asogwa, Hanatsu Nagano, Kai Wang and Rezaul Begg
Sensors 2022, 22(18), 6960; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186960 - 14 Sep 2022
Cited by 1 | Viewed by 2105
Abstract
Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and [...] Read more.
Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories using precise, high-speed, camera-based 3D motion capture systems. For practical intervention strategies designed to predict, and possibly assist, swing foot trajectory to prevent tripping, identification of the MFC event is essential; however, no technique is currently available to determine MFC timing in real-life settings outside the laboratory. One strategy has been to use wearable sensors, such as Inertial Measurement Units (IMUs), but these data are limited to primarily providing only tri-axial linear acceleration and angular velocity. The aim of this study was to develop Machine Learning (ML) algorithms to predict MFC timing based on the preceding toe-off gait event. The ML algorithms were trained using 13 young adults’ foot trajectory data recorded from an Optotrak 3D motion capture system. A Deep Learning configuration was developed based on a Recurrent Neural Network with a Long Short-Term Memory (LSTM) architecture and Huber loss-functions to minimise MFC-timing prediction error. We succeeded in predicting MFC timing from toe-off characteristics with a mean absolute error of 0.07 s. Although further algorithm training using population-specific inputs are needed. The ML algorithms designed here can be used for real-time actuation of wearable active devices to increase foot clearance at critical MFC and reduce devastating tripping falls. Further developments in ML-guided actuation for active exoskeletons could prove highly effective in developing technologies to reduce tripping-related falls across a range of gait impaired populations. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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20 pages, 11596 KiB  
Article
Electroactive Biofilms of Activated Sludge Microorganisms on a Nanostructured Surface as the Basis for a Highly Sensitive Biochemical Oxygen Demand Biosensor
by Saniyat Kurbanalieva, Vyacheslav Arlyapov, Anna Kharkova, Roman Perchikov, Olga Kamanina, Pavel Melnikov, Nadezhda Popova, Andrey Machulin, Sergey Tarasov, Evgeniya Saverina, Anatoly Vereshchagin and Anatoly Reshetilov
Sensors 2022, 22(16), 6049; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166049 - 12 Aug 2022
Cited by 10 | Viewed by 2537
Abstract
The possibility of the developing a biochemical oxygen demand (BOD) biosensor based on electroactive biofilms of activated sludge grown on the surface of a graphite-paste electrode modified with carbon nanotubes was studied. A complex of microscopic methods controlled biofilm formation: optical microscopy with [...] Read more.
The possibility of the developing a biochemical oxygen demand (BOD) biosensor based on electroactive biofilms of activated sludge grown on the surface of a graphite-paste electrode modified with carbon nanotubes was studied. A complex of microscopic methods controlled biofilm formation: optical microscopy with phase contrast, scanning electron microscopy, and laser confocal microscopy. The features of charge transfer in the obtained electroactive biofilms were studied using the methods of cyclic voltammetry and electrochemical impedance spectroscopy. The rate constant of the interaction of microorganisms with the extracellular electron carrier (0.79 ± 0.03 dm3(g s)−1) and the heterogeneous rate constant of electron transfer (0.34 ± 0.02 cm s−1) were determined using the cyclic voltammetry method. These results revealed that the modification of the carbon nanotubes’ (CNT) electrode surface makes it possible to create electroactive biofilms. An analysis of the metrological and analytical characteristics of the created biosensors showed that the lower limit of the biosensor based on an electroactive biofilm of activated sludge is 0.41 mgO2/dm3, which makes it possible to analyze almost any water sample. Analysis of 12 surface water samples showed a high correlation (R2 = 0.99) with the results of the standard method for determining biochemical oxygen demand. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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15 pages, 5172 KiB  
Article
Distinct Binding Properties of Neutravidin and Streptavidin Proteins to Biotinylated Supported Lipid Bilayers: Implications for Sensor Functionalization
by Tun Naw Sut, Hyeonjin Park, Dong Jun Koo, Bo Kyeong Yoon and Joshua A. Jackman
Sensors 2022, 22(14), 5185; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145185 - 11 Jul 2022
Cited by 7 | Viewed by 3491
Abstract
The exceptional strength and stability of noncovalent avidin-biotin binding is widely utilized as an effective bioconjugation strategy in various biosensing applications, and neutravidin and streptavidin proteins are two commonly used avidin analogues. It is often regarded that the biotin-binding abilities of neutravidin and [...] Read more.
The exceptional strength and stability of noncovalent avidin-biotin binding is widely utilized as an effective bioconjugation strategy in various biosensing applications, and neutravidin and streptavidin proteins are two commonly used avidin analogues. It is often regarded that the biotin-binding abilities of neutravidin and streptavidin are similar, and hence their use is interchangeable; however, a deeper examination of how these two proteins attach to sensor surfaces is needed to develop reliable surface functionalization options. Herein, we conducted quartz crystal microbalance-dissipation (QCM-D) biosensing experiments to investigate neutravidin and streptavidin binding to biotinylated supported lipid bilayers (SLBs) in different pH conditions. While streptavidin binding to biotinylated lipid receptors was stable and robust across the tested pH conditions, neutravidin binding strongly depended on the solution pH and was greater with increasingly acidic pH conditions. These findings led us to propose a two-step mechanistic model, whereby streptavidin and neutravidin binding to biotinylated sensing interfaces first involves nonspecific protein adsorption that is mainly influenced by electrostatic interactions, followed by structural rearrangement of adsorbed proteins to specifically bind to biotin functional groups. Practically, our findings demonstrate that streptavidin is preferable to neutravidin for constructing SLB-based sensing platforms and can improve sensing performance for detecting antibody–antigen interactions. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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26 pages, 18634 KiB  
Article
Dynamic Mode Decomposition of Fluorescence Loss in Photobleaching Microscopy Data for Model-Free Analysis of Protein Transport and Aggregation in Living Cells
by Daniel Wüstner
Sensors 2022, 22(13), 4731; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134731 - 23 Jun 2022
Cited by 4 | Viewed by 1852
Abstract
The phase separation and aggregation of proteins are hallmarks of many neurodegenerative diseases. These processes can be studied in living cells using fluorescent protein constructs and quantitative live-cell imaging techniques, such as fluorescence recovery after photobleaching (FRAP) or the related fluorescence loss in [...] Read more.
The phase separation and aggregation of proteins are hallmarks of many neurodegenerative diseases. These processes can be studied in living cells using fluorescent protein constructs and quantitative live-cell imaging techniques, such as fluorescence recovery after photobleaching (FRAP) or the related fluorescence loss in photobleaching (FLIP). While the acquisition of FLIP images is straightforward on most commercial confocal microscope systems, the analysis and computational modeling of such data is challenging. Here, a novel model-free method is presented, which resolves complex spatiotemporal fluorescence-loss kinetics based on dynamic-mode decomposition (DMD) of FLIP live-cell image sequences. It is shown that the DMD of synthetic and experimental FLIP image series (DMD-FLIP) allows for the unequivocal discrimination of subcellular compartments, such as nuclei, cytoplasm, and protein condensates based on their differing transport and therefore fluorescence loss kinetics. By decomposing fluorescence-loss kinetics into distinct dynamic modes, DMD-FLIP will enable researchers to study protein dynamics at each time scale individually. Furthermore, it is shown that DMD-FLIP is very efficient in denoising confocal time series data. Thus, DMD-FLIP is an easy-to-use method for the model-free detection of barriers to protein diffusion, of phase-separated protein assemblies, and of insoluble protein aggregates. It should, therefore, find wide application in the analysis of protein transport and aggregation, in particular in relation to neurodegenerative diseases and the formation of protein condensates in living cells. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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14 pages, 2671 KiB  
Article
Bioluminescence Sensing in 3D Spherical Microtissues for Multiple Bioactivity Analysis of Environmental Samples
by Maria Maddalena Calabretta, Denise Gregucci, Tiziana Guarnieri, Marina Bonini, Elisa Neri, Martina Zangheri and Elisa Michelini
Sensors 2022, 22(12), 4568; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124568 - 17 Jun 2022
Cited by 4 | Viewed by 2172
Abstract
The development of predictive in vitro sensing tools able to provide rapid information on the different bioactivities of a sample is of pivotal importance, not only to monitor environmental toxicants, but also to understand their mechanisms of action on diverse molecular pathways. This [...] Read more.
The development of predictive in vitro sensing tools able to provide rapid information on the different bioactivities of a sample is of pivotal importance, not only to monitor environmental toxicants, but also to understand their mechanisms of action on diverse molecular pathways. This mechanistic understanding is highly important for the characterization of toxicological hazards, and for the risk assessment of chemicals and environmental samples such as surface waters and effluents. Prompted by this need, we developed and optimized a straightforward bioluminescent multiplexed assay which enables the measurement of four bioactivities, selected for their relevance from a toxicological perspective, in bioluminescent microtissues. The assay was developed to monitor inflammatory, antioxidant, and toxic activity, and the presence of heavy metals, and was successfully applied to the analysis of river water samples, showing potential applicability for environmental analyses. The assay, which does not require advanced equipment, can be easily implemented in general laboratories equipped with basic cell culture facilities and a luminometer. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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10 pages, 2061 KiB  
Article
A Low-Cost, 3D-Printed Biosensor for Rapid Detection of Escherichia coli
by Samir Malhotra, Dang Song Pham, Michael P. H. Lau, Anh H. Nguyen and Hung Cao
Sensors 2022, 22(6), 2382; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062382 - 19 Mar 2022
Cited by 8 | Viewed by 3533
Abstract
Detection of bacterial pathogens is significant in the fields of food safety, medicine, and public health, just to name a few. If bacterial pathogens are not properly identified and treated promptly, they can lead to morbidity and mortality, also possibly contribute to antimicrobial [...] Read more.
Detection of bacterial pathogens is significant in the fields of food safety, medicine, and public health, just to name a few. If bacterial pathogens are not properly identified and treated promptly, they can lead to morbidity and mortality, also possibly contribute to antimicrobial resistance. Current bacterial detection methodologies rely solely on laboratory-based techniques, which are limited by long turnaround detection times, expensive costs, and risks of inadequate accuracy; also, the work requires trained specialists. Here, we describe a cost-effective and portable 3D-printed electrochemical biosensor that facilitates rapid detection of certain Escherichia coli (E. coli) strains (DH5α, BL21, TOP10, and JM109) within 15 min using 500 μL of sample, and costs only USD 2.50 per test. The sensor displayed an excellent limit of detection (LOD) of 53 cfu, limit of quantification (LOQ) of 270 cfu, and showed cross-reactivity with strains BL21 and JM109 due to shared epitopes. This advantageous diagnostic device is a strong candidate for frequent testing at point of care; it also has application in various fields and industries where pathogen detection is of interest. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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16 pages, 16185 KiB  
Article
Digital Assessment and Classification of Wine Faults Using a Low-Cost Electronic Nose, Near-Infrared Spectroscopy and Machine Learning Modelling
by Claudia Gonzalez Viejo and Sigfredo Fuentes
Sensors 2022, 22(6), 2303; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062303 - 16 Mar 2022
Cited by 13 | Viewed by 3164
Abstract
The winemaking industry can benefit greatly by implementing digital technologies to avoid guesswork and the development of off-flavors and aromas in the final wines. This research presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with [...] Read more.
The winemaking industry can benefit greatly by implementing digital technologies to avoid guesswork and the development of off-flavors and aromas in the final wines. This research presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning to detect and assess wine faults. For this purpose, red and white base wines were used, and treatments consisted of spiked samples with 12 faults that are traditionally formed in wines. Results showed high accuracy in the classification models using NIR and e-nose for red wines (94–96%; 92–97%, respectively) and white wines (96–97%; 90–97%, respectively). Implementing new and emerging digital technologies could be a turning point for the winemaking industry to become more predictive in terms of decision-making and maintaining and increasing wine quality traits in a changing and challenging climate. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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19 pages, 3421 KiB  
Article
Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach
by Chiara Filippini, Adolfo Di Crosta, Rocco Palumbo, David Perpetuini, Daniela Cardone, Irene Ceccato, Alberto Di Domenico and Arcangelo Merla
Sensors 2022, 22(5), 1789; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051789 - 24 Feb 2022
Cited by 22 | Viewed by 3199
Abstract
Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while [...] Read more.
Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect’s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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14 pages, 888 KiB  
Article
Non-Invasive Blood Flow Speed Measurement Using Optics
by Alex Ce Zhang and Yu-Hwa Lo
Sensors 2022, 22(3), 897; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030897 - 25 Jan 2022
Cited by 2 | Viewed by 5031
Abstract
Non-invasive measurement of the arterial blood speed gives important health information such as cardio output and blood supplies to vital organs. The magnitude and change in arterial blood speed are key indicators of the health conditions and development and progression of diseases. We [...] Read more.
Non-invasive measurement of the arterial blood speed gives important health information such as cardio output and blood supplies to vital organs. The magnitude and change in arterial blood speed are key indicators of the health conditions and development and progression of diseases. We demonstrated a simple technique to directly measure the blood flow speed in main arteries based on the diffused light model. The concept is demonstrated with a phantom that uses intralipid hydrogel to model the biological tissue and an embedded glass tube with flowing human blood to model the blood vessel. The correlation function of the measured photocurrent was used to find the electrical field correlation function via the Siegert relation. We have shown that the characteristic decorrelation rate (i.e., the inverse of the decoherent time) is linearly proportional to the blood speed and independent of the tube diameter. This striking property can be explained by an approximate analytic solution for the diffused light equation in the regime where the convective flow is the dominating factor for decorrelation. As a result, we have demonstrated a non-invasive method of measuring arterial blood speed without any prior knowledge or assumption about the geometric or mechanic properties of the blood vessels. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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Review

Jump to: Editorial, Research

26 pages, 3487 KiB  
Review
Programmable Nanostructures Based on Framework-DNA for Applications in Biosensing
by Bing Liu, Fan Wang and Jie Chao
Sensors 2023, 23(6), 3313; https://0-doi-org.brum.beds.ac.uk/10.3390/s23063313 - 21 Mar 2023
Cited by 3 | Viewed by 2097
Abstract
DNA has been actively utilized as bricks to construct exquisite nanostructures due to their unparalleled programmability. Particularly, nanostructures based on framework DNA (F-DNA) with controllable size, tailorable functionality, and precise addressability hold excellent promise for molecular biology studies and versatile tools for biosensor [...] Read more.
DNA has been actively utilized as bricks to construct exquisite nanostructures due to their unparalleled programmability. Particularly, nanostructures based on framework DNA (F-DNA) with controllable size, tailorable functionality, and precise addressability hold excellent promise for molecular biology studies and versatile tools for biosensor applications. In this review, we provide an overview of the current development of F-DNA-enabled biosensors. Firstly, we summarize the design and working principle of F-DNA-based nanodevices. Then, recent advances in their use in different kinds of target sensing with effectiveness have been exhibited. Finally, we envision potential perspectives on the future opportunities and challenges of biosensing platforms. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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25 pages, 2541 KiB  
Review
Healthcare Monitoring Using Low-Cost Sensors to Supplement and Replace Human Sensation: Does It Have Potential to Increase Independent Living and Prevent Disease?
by Zhuofu Liu, Vincenzo Cascioli and Peter W. McCarthy
Sensors 2023, 23(4), 2139; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042139 - 14 Feb 2023
Cited by 3 | Viewed by 2839
Abstract
Continuous monitoring of health status has the potential to enhance the quality of life and life expectancy of people suffering from chronic illness and of the elderly. However, such systems can only come into widespread use if the cost of manufacturing is low. [...] Read more.
Continuous monitoring of health status has the potential to enhance the quality of life and life expectancy of people suffering from chronic illness and of the elderly. However, such systems can only come into widespread use if the cost of manufacturing is low. Advancements in material science and engineering technology have led to a significant decrease in the expense of developing healthcare monitoring devices. This review aims to investigate the progress of the use of low-cost sensors in healthcare monitoring and discusses the challenges faced when accomplishing continuous and real-time monitoring tasks. The major findings include (1) only a small number of publications (N = 50) have addressed the issue of healthcare monitoring applications using low-cost sensors over the past two decades; (2) the top three algorithms used to process sensor data include SA (Statistical Analysis, 30%), SVM (Support Vector Machine, 18%), and KNN (K-Nearest Neighbour, 12%); and (3) wireless communication techniques (Zigbee, Bluetooth, Wi-Fi, and RF) serve as the major data transmission tools (77%) followed by cable connection (13%) and SD card data storage (10%). Due to the small fraction (N = 50) of low-cost sensor-based studies among thousands of published articles about healthcare monitoring, this review not only summarises the progress of related research but calls for researchers to devote more effort to the consideration of cost reduction as well as the size of these components. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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17 pages, 1904 KiB  
Review
Biomarkers and Corresponding Biosensors for Childhood Cancer Diagnostics
by Azadeh Gharehzadehshirazi, Mashaalah Zarejousheghani, Sedigheh Falahi, Yvonne Joseph and Parvaneh Rahimi
Sensors 2023, 23(3), 1482; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031482 - 28 Jan 2023
Cited by 6 | Viewed by 2720
Abstract
Although tremendous progress has been made in treating childhood cancer, it is still one of the leading causes of death in children worldwide. Because cancer symptoms overlap with those of other diseases, it is difficult to predict a tumor early enough, which causes [...] Read more.
Although tremendous progress has been made in treating childhood cancer, it is still one of the leading causes of death in children worldwide. Because cancer symptoms overlap with those of other diseases, it is difficult to predict a tumor early enough, which causes cancers in children to be more aggressive and progress more rapidly than in adults. Therefore, early and accurate detection methods are urgently needed to effectively treat children with cancer therapy. Identification and detection of cancer biomarkers serve as non-invasive tools for early cancer screening, prevention, and treatment. Biosensors have emerged as a potential technology for rapid, sensitive, and cost-effective biomarker detection and monitoring. In this review, we provide an overview of important biomarkers for several common childhood cancers. Accordingly, we have enumerated the developed biosensors for early detection of pediatric cancer or related biomarkers. This review offers a restructured platform for ongoing research in pediatric cancer diagnostics that can contribute to the development of rapid biosensing techniques for early-stage diagnosis, monitoring, and treatment of children with cancer and reduce the mortality rate. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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