The ubiquitous nature of bacteria enables them to survive in a wide variety of environments. Hence, the rise of various pathogenic species that are harmful to human health raises the need for the development of accurate sensing systems. Sensing systems are necessary for diagnosis and epidemiological control of pathogenic organism, especially in the food-borne pathogen and sanitary water treatment facility' bacterial populations. Bacterial sensing for the purpose of diagnosis can function in three ways: bacterial morphological visualization, specific detection of bacterial component and whole cell detection. This paper provides an overview of the currently available bacterial detection systems that ranges from microscopic observation to state-of-the-art smartphone-based detection.
Animal senses cover a broad range of signal types and signal bandwidths and have inspired various sensors and bioinstrumentation devices for biological and medical applications. Insects, such as desert ants and honeybees, for example, utilize polarized skylight pattern-based information in their navigation activities. They reliably return to their nests and hives from places many kilometers away. The insect navigation system involves the dorsal rim area in their compound eyes and the corresponding polarization sensitive neurons in the brain. The dorsal rim area is equipped with photoreceptors, which have orthogonally arranged small hair-like structures termed microvilli. These are the specialized sensors for the detection of polarized skylight patterns (e-vector orientation). Various research groups have been working on the development of novel navigation systems inspired by polarized skylight-based navigation in animals. Their major contributions are critically reviewed. One focus of current research activities is on imitating the integration path mechanism in desert ants. The potential for simple, high performance miniaturized bioinstrumentation that can assist people in navigation will be explored.
For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.
The diagnosis of hepatitis B virus (HBV) and monitoring of the vaccination efficiency against HBV require real-time analysis. The presence of antibody against hepatitis B virus surface antigen (anti-HBsAg) as a result of HBV infection and/or immunization may indicate individual immune status towards HBV. This study investigated the ability of a bio-nanogate-based displacement immunosensing strategy in detecting anti-HBsAg antibody, via nonspecific-binding between polyamidoamine dendrimers encapsulated gold nanoparticles (PAMAM-Au) and the 'antigenic determinant' region (aD) of HBsAg. For this purpose, maltose binding protein harbouring the aD region (MBP-aD) was synthesized as a bioreceptor and immobilized on the screen-printed carbon electrode (SPCE). Following that, PAMAM-Au was deposited on MBP-aD, forming the 'gate' and was used as a monitoring agent. Under optimal conditions, the high specificity of anti-HBsAg antibody towards MBP-aD displaced PAMAM-Au causing the decrement of anodic peak in differential pulse voltammetry (DPV) analysis. The signal changes were proportionally related to the concentration of anti-HBsAg antibody, in a range of 1 - 1000 mIU/mL with a limit of detection (LOD) of 2.5 mIU/mL. The results also showed high specificity and selectivity of the immunosensor platform in detecting anti-HBsAg antibody both in spiked buffer and human serum samples.
Cardiovascular disease is a major global health issue. In particular, acute myocardial infarction (AMI) requires urgent attention and early diagnosis. The use of point-of-care diagnostics has resulted in the improved management of cardiovascular disease, but a major drawback is that the performance of POC devices does not rival that of central laboratory tests. Recently, many studies and advances have been made in the field of surface-enhanced Raman scattering (SERS), including the development of POC biosensors that utilize this detection method. Here, we present a review of the strengths and limitations of these emerging SERS-based biosensors for AMI diagnosis. The ability of SERS to multiplex sensing against existing POC detection methods are compared and discussed. Furthermore, SERS calibration-free methods that have recently been explored to minimize the inconvenience and eliminate the limitations caused by the limited linear range and interassay differences found in the calibration curves are outlined. In addition, the incorporation of artificial intelligence (AI) in SERS techniques to promote multivariate analysis and enhance diagnostic accuracy are discussed. The future prospects for SERS-based POC devices that include wearable POC SERS devices toward predictive, personalized medicine following the Fourth Industrial Revolution are proposed.
Dengue is the current leading cause of death among children in several Latin American and Asian countries. Due to poverty in areas where the disease is prevalent and the high cost of conventional diagnostic systems, low cost devices are needed to reduce the burden caused by dengue infection. Centrifugal microfluidic platforms are an alternative solution to reduce costs and increase the availability of a rapid diagnostic system. The rate of chemical reactions in such devices often depends on the efficiency of the mixing techniques employed in their microfluidic networks. This paper introduces a micromixer that operates by the expansion and contraction of a microballoon to produce a consistent periodical 3D reciprocating flow. We established that microballoons reduced mixing time of 12 μl liquids from 170 min, for diffusional mixing, to less than 23 s. We have also tested the effect of the microballoon mixers on the detection of the dengue virus. The results indicate that employing a microballoon mixer enhances the detection sensitivity of the dengue virus by nearly one order of magnitude compared to the conventional ELISA method.
An optical biosensor based on glutamate dehydrogenase (GLDH) immobilized in a chitosan film for the determination of ammonium in water samples is described. The biosensor film was deposited on a glass slide via a spin-coating method. The ammonium was measured based on beta-nicotinamide adenine dinucleotide (NADH) oxidation in the presence of alpha-ketoglutaric acid at a wavelength of 340 nm. The biosensor showed optimum activity at pH 8. The optimum chitosan concentrations and enzyme loading were found to be at 2% (w/v) and 0.08 mg, respectively. Optimum concentrations of NADH and alpha-ketoglutaric acid both were obtained at 0.15 mM. A linear response of the biosensor was obtained in the ammonium concentration range of 0.005 to 0.5 mM with a detection limit of 0.005 mM. The reproducibility of the biosensor was good, with an observed relative standard deviation of 5.9% (n=8). The biosensor was found to be stable for at least 1 month when stored dry at 4 degrees C.
The burden of food contamination and food wastage has significantly contributed to the increased prevalence of foodborne disease and food insecurity all over the world. Due to this, there is an urgent need to develop a smarter food traceability system. Recent advancements in biosensors that are easy-to-use, rapid yet selective, sensitive, and cost-effective have shown great promise to meet the critical demand for onsite and immediate diagnosis and treatment of food safety and quality control (i.e. point-of-care technology). This review article focuses on the recent development of different biosensors for food safety and quality monitoring. In general, the application of biosensors in agriculture (i.e. pre-harvest stage) for early detection and routine control of plant infections or stress is discussed. Afterward, a more detailed advancement of biosensors in the past five years within the food supply chain (i.e. post-harvest stage) to detect different types of food contaminants and smart food packaging is highlighted. A section that discusses perspectives for the development of biosensors in the future is also mentioned.
Electrochemical biosensors for phenolic compound determination were developed by immobilization of tyrosinase enzyme in a series of methacrylic-acrylic based biosensor membranes deposited directly using a photocuring method. By modifying the hydrophilicity of the membranes using different proportions of 2-hydroxyethyl methacrylate (HEMA) and butyl acrylate (nBA), we developed biosensor membranes of different hydrophilic characters. The differences in hydrophilicity of these membranes led to changes in the sensitivity of the biosensors towards different phenolic compounds. In general biosensors constructed from the methacrylic-acrylic based membranes showed the poorest response to catechol relative to other phenolic compounds, which is in contrast to many other biosensors based on tyrosinase. The decrease in hydrophilicity of the membrane also allowed better selectivity towards chlorophenols. However, phenol biosensors constructed from the more hydrophilic membrane materials demonstrated better analytical performance towards phenol compared with those made from less hydrophilic ones. For the detection of phenols, these biosensors with different membranes gave detection limits of 0.13-0.25 microM and linear response range from 6.2-54.2 microM phenol. The phenol biosensors also showed good phenol recovery from landfill leachate samples (82-117%).
Graphene and its hybrids are being employed as potential materials in light-sensing devices due to their high optical and electronic properties. However, the absence of a bandgap in graphene limits the realization of devices with high performance. In this work, a boron-doped reduced graphene oxide (B-rGO) is proposed to overcome the above problems. Boron doping enhances the conductivity of graphene oxide and creates several defect sites during the reduction process, which can play a vital role in achieving high-sensing performance of light-sensing devices. Initially, the B-rGO is synthesized using a modified microwave-assisted hydrothermal method and later analyzed using standard FESEM, FTIR, XPS, Raman, and XRD techniques. The content of boron in doped rGO was found to be 6.51 at.%. The B-rGO showed a tunable optical bandgap from 2.91 to 3.05 eV in the visible spectrum with an electrical conductivity of 0.816 S/cm. The optical constants obtained from UV-Vis absorption spectra suggested an enhanced surface plasmon resonance (SPR) response for B-rGO in the theoretical study, which was further verified by experimental investigations. The B-rGO with tunable bandgap and enhanced SPR could open up the solution for future high-performance optoelectronic and sensing applications.
The development of easy to use, rapid and sensitive methods for direct detection of foodborne bacterial pathogens has become significantly important due to their impact on human health. In recent years, carbon nanomaterials have been adapted in the fabrication of electrochemical biosensors due to their exceptional combination of intrinsic properties such as high conductivity, stability and biocompatibility that render them as a promising candidate for bio-sensing material. The scope of this review is to provide a brief history of the current methods and different types of electrochemical biosensors used for the detection of bacterial pathogens. We primarily focus on the recent progress and applications of graphene, carbon nanotubes and their derivatives in electrochemical biosensors for foodborne bacterial pathogens detection. Finally, the status and future prospects of carbon-based electrochemical biosensors are also reviewed and discussed.
In this study, an amino-modified aptasensor using multi-walled carbon nanotubes (MWCNTs)-deposited ITO electrode was prepared and evaluated for the detection of pathogenic Salmonella bacteria. An amino-modified aptamer (ssDNA) which binds selectively to whole-cell Salmonella was immobilised on the COOH-rich MWCNTs to produce the ssDNA/MWCNT/ITO electrode. The morphology of the MWCNT before and after interaction with the aptamers were observed using scanning electron microscopy (SEM). Cyclic voltammetry and electrochemical impedance spectroscopy techniques were used to investigate the electrochemical properties and conductivity of the aptasensor. The results showed that the impedance measured at the ssDNA/MWCNT/ITO electrode surface increased after exposure to Salmonella cells, which indicated successful binding of Salmonella on the aptamer-functionalised surface. The developed ssDNA/MWCNT/ITO aptasensor was stable and maintained linearity when the scan rate was increased from 10 mV s-1 to 90 mV s-1. The detection limit of the ssDNA/MWCNT/ITO aptasensor, determined from the sensitivity analysis, was found to be 5.5 × 101 cfu mL-1 and 6.7 × 101 cfu mL-1 for S. Enteritidis and S. Typhimurium, respectively. The specificity test demonstrated that Salmonella bound specifically to the ssDNA/MWCNT/ITO aptasensor surface, when compared with non-Salmonella spp. The prepared aptasensor was successfully applied for the detection of Salmonella in food samples.
Matched MeSH terms: Biosensing Techniques/methods*; Biosensing Techniques/statistics & numerical data
In this work, the direct electrochemistry of hemoglobin (Hb), which was immobilized on carbonyl functionalized single walled carbon nanotube (SWCNT) and deposited onto a gold (Au) electrode has been described. The synthesis of the network of crosslinked SWCNT/Hb was done with the help of crosslinking agent EDC (1-ethyl-3-(3-dimethylaminopropyl) carbodiimide). The UV-Vis and FTIR spectroscopy of SWCNT/Hb networks showed that Hb maintained its natural structure and kept good stability. In addition with this, scanning electron microscopy (SEM) illustrated that SWCNT/Hb networks had a featured layered structure and Hb being strongly liked with SWCNT surface. Cyclic voltammetry (CV) was used to study and to optimize the performance of the resulting modified electrode. The cyclic voltammetric (CV) responses of SWCNT/Hb networks in pH 7.0 exhibit prominent redox couple for the FeIII/II redox process with a midpoint potential of -0.46 V and -0.34, cathodic and anodic respectively. Furthermore, SWCNT/Hb networks are utilized for the detection of hydrogen peroxide (H2O2). Electrochemical measurements reveal that the resulting SWCNT/Hb electrodes display high electrocatalytic activity to H2O2 with high sensitivity, wide linear range, and low detection limit. Overall, the electrochemical results are due to excellent biocompatibility and excellent electron transport efficiency of CNT as well as high Hb loading and synergistic catalytic effect of the modified electrode toward H2O2.
CeO2 nanoparticles (NPs) have shown promising approaches as therapeutic agents in biology and medical sciences. The physicochemical properties of CeO2-NPs, such as size, agglomeration status in liquid, and surface charge, play important roles in the ultimate interactions of the NP with target cells. Recently, CeO2-NPs have been synthesized through several bio-directed methods applying natural and organic matrices as stabilizing agents in order to prepare biocompatible CeO2-NPs, thereby solving the challenges regarding safety, and providing the appropriate situation for their effective use in biomedicine. This review discusses the different green strategies for CeO2-NPs synthesis, their advantages and challenges that are to be overcome. In addition, this review focuses on recent progress in the potential application of CeO2-NPs in biological and medical fields. Exploiting biocompatible CeO2-NPs may improve outcomes profoundly with the promise of effective neurodegenerative therapy and multiple applications in nanobiotechnology.
Capacitive biosensors are an emerging technology revolutionizing wearable sensing systems and personal healthcare devices. They are capable of continuously measuring bioelectrical signals from the human body while utilizing textiles as an insulator. Different textile types have their own unique properties that alter skin-electrode capacitance and the performance of capacitive biosensors. This paper aims to identify the best textile insulator to be used with capacitive biosensors by analysing the characteristics of 6 types of common textile materials (cotton, linen, rayon, nylon, polyester, and PVC-textile) while evaluating their impact on the performance of a capacitive biosensor. A textile-insulated capacitive (TEX-C) biosensor was developed and validated on 3 subjects. Experimental results revealed that higher skin-electrode capacitance of a TEX-C biosensor yields a lower noise floor and better signal quality. Natural fabric such as cotton and linen were the two best insulating materials to integrate with a capacitive biosensor. They yielded the lowest noise floor of 2 mV and achieved consistent electromyography (EMG) signals measurements throughout the performance test.
A capacitive electromyography (cEMG) biomedical sensor measures the EMG signal from human body through capacitive coupling methodology. It has the flexibility to be insulated by different types of materials. Each type of insulator will yield a unique skin-electrode capacitance which determine the performance of a cEMG biomedical sensor. Most of the insulator being explored are solid and non-breathable which cause perspiration in a long-term EMG measurement process. This research aims to explore the porous medical bandages such as micropore, gauze, and crepe bandage to be used as an insulator of a cEMG biomedical sensor. These materials are breathable and hypoallergenic. Their unique properties and characteristics have been reviewed respectively. A 50 Hz digital notch filter was developed and implemented in the EMG measurement system design to further enhance the performance of these porous medical bandage insulated cEMG biomedical sensors. A series of experimental verifications such as noise floor characterization, EMG signals measurement, and performance correlation were done on all these sensors. The micropore insulated cEMG biomedical sensor yielded the lowest noise floor amplitude of 2.44 mV and achieved the highest correlation coefficient result in comparison with the EMG signals captured by the conventional wet contact electrode.
We report the production and degradation of quorum sensing N-acyl-homoserine lactones by bacteria isolated from Malaysian montane forest soil. Phylogenetic analysis indicated that these isolates clustered closely to the genera of Arthrobacter, Bacillus and Pseudomonas. Quorum quenching activity was detected in six isolates of these three genera by using a series of bioassays and rapid resolution liquid chromatography analysis. Biosensor screening and high resolution liquid chromatography-mass spectrometry analysis revealed the production of N-dodecanoyl-L-homoserine lactone (C12-HSL) by Pseudomonas frederiksbergensis (isolate BT9). In addition to degradation of a wide range of N-acyl-homoserine lactones, Arthrobacter and Pseudomonas spp. also degraded p-coumaroyl-homoserine lactone. To the best of our knowledge, this is the first documentation of Arthrobacter and Pseudomonas spp. capable of degrading p-coumaroyl-homoserine lactone and the production of C12-HSL by P. frederiksbergensis.
The development of an optical biosensor based on immobilization of 3-methyl-2-benzothiazolinone hydrazone (MBTH) in hybrid nafion/sol-gel silicate film and tyrosinase in chitosan film for the detection of phenolic compounds has been described. Tyrosinase was immobilized in chitosan film deposited on the hybrid nafion/sol-gel silicate film containing MBTH. The enzymatic oxidation product of phenolic compounds were stabilized through formation of adduct with MBTH to produce a maroon color adduct. The color intensity of adduct was found to increase proportionally with the increase of the substrate concentrations after 5min exposure. The linearity of the biosensor towards phenol, catechol and m-cresol were in the respective concentration range of 0.5-7.0, 0.5-10.0 and 1.0-13.0mg/L with detection limit of 0.18, 0.23 and 0.43mg/L, respectively. The biosensor shows a good stability for at least 3 months.
Heavy metals with high chemical activity from sludge and waste release, agriculture, and
mining activity are a major concern. They should be carefully managed before reaching the
main water bodies. Excessive exposure to heavy metal may cause toxic effect to any types of
organism from the biomolecular to the physiological level, and ultimately cause death. Monitoring is the best technique to ensure the safety of our environment before a rehabilitation is
needed. Nowadays, enzyme-based biosensors are utilised in biomonitoring programmes as
this technique allows for a real-time detection and rapid result. It is also inexpensive and easy
to handle. Enzyme-based biosensors are an alternative for the preliminary screening of
contamination before a secondary screening is performed using high-performance technology.
This review highlights the current knowledge on enzyme-based biosensors, focusing on
cholinesterase for toxic metal detection in the environment.
Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.