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For early detection and diagnosis of cancer, it is essential to develop an electrochemical biosensor that is quick, accurate, and sensitive.

The prevalence of the inclusion or substitution of porcine and its derivatives in foods by imprudent food manufacturers accentuated the need for a sensitive and specific method to detect porcine DNA. This work aimed to develop and validate an EvaGreen dye-based quantitative real-time (qPCR) assay for porcine DNA analysis. The primer set used in this work targets a 95-bp fragment of the porcine cytochrome b gene. Application of the assay to dilutions of porcine genomic DNA showed that the assay is sensitive down to 10 pg/μL of porcine DNA and the detection limit, under binary admixture conditions, was 0.001%. A correlation coefficient (R2) of 0.998 and PCR efficiency of 101.6% over the dynamic range from 10 to 10000 pg/μL indicated that the assay has high linearity and efficiency. The method’s specificity towards porcine was revealed by the amplification profile, which only displayed an amplification signal for porcine DNA. These findings suggest that the developed assay is sensitive, specific, and rapid. In conclusion, the assay can be used as an alternative method to detect and quantify porcine DNA in raw and processed food products.

A label-free electrochemiluminescence biosensor using carbon nano-horns and nafion nanocomposite-modified carbon nanofiber printed sensor for the detection of porcine gelatin.

The rapid development of accurate and quick diagnostic tools for infectious diseases has made a massive impact in global health. POC devices for pathogen detection have primarily contributed to clinical management in various applications such as pathology, drug discovery and food safety. Diagnostic tests in isolated or remote areas were previously time-consuming, costly, and the methods were extensive and laborious. However, the new generation of miniaturized biosensor technologies aimed at POC testing resulted in a more sensitive, reliable, rapid and cost-effective detection process without needing sophisticated instruments. Among them, a few were developed as ready-to-use devices and are currently commercially available. In this review, we presented the most recent developments in diagnostic methods in pathogen detection and the improvements included in the detection steps in recent POCs for pathogen detection. We also discussed incorporating nanomaterials, microfluidics, lateral flow tests, screen-printed electrodes and smartphones into POC devices used in this area. The challenges and prospects of developing sample-to-results POC devices for pathogen detection were also discussed.

In recent years, rapid detection methods such as polymerase chain reaction (PCR) and quantitative real-time PCR (qPCR) have been continuously developed to improve the detection of food-borne pathogens in food samples. The recent developments of PCR and qPCR in the detection and identification of these food-borne pathogens are described and elaborated throughout this review. Specifically, further developments and improvements of qPCR are discussed in detecting Salmonella and norovirus. Promising advances in these molecular detection methods have been widely used to prevent human food-borne illnesses and death caused by the food-borne pathogens. In addition, this review presents the limitations and challenges of the detection methods which include conventional culture method and conventional PCR method in detecting Salmonella and norovirus. Furthermore, several advances of qPCR such as viability PCR (vPCR) and digital PCR (dPCR) have been discussed in the detection of Salmonella and norovirus. Good practice of analysis of the food-borne pathogens and other contaminants in the food industry as well as the advancement of molecular detection methods will help improve and ensure food safety and food quality.

In recent years, rapid detection methods such as polymerase chain reaction (PCR) and quantitative real-time PCR (qPCR) have been continuously developed to improve the detection of food-borne pathogens in food samples. The recent developments of PCR and qPCR in the detection and identification of these food-borne pathogens are described and elaborated throughout this review. Specifically, further developments and improvements of qPCR are discussed in detecting

In this study, a direct and label-free immunosensor was designed and constructed by modifying the screen-printed electrode with graphene nanoplatelets (GNPs) for the detection of the cardiac troponin T (cTnT). Firstly, GNPs were drop-casted onto carbon working electrode. Monoclonal cTnT antibodies were then immobilized on the GNPs via physical adsorption; finally, BSA was introduced to block non-specific binding sites. The detection of cTnT was performed using an electrochemiluminescence (ECL) technique with tris(bipyridine)ruthenium(II) chloride ([Ru(bpy)3]Cl2) used as a luminophore and TPrA (tripropylamine) as a co-reactant. The ECL intensity was demonstrated to be directly proportional to the cTnT concentration where a linear range from 100 pg mL-1 to 5 fg mL-1 of the cTnT detection was established. An extremely low limit of detection was achieved to be 0.05 fg mL-1 with an outstanding specificity. Additionally, this immunosensor showed excellent percentage recovery for real samples analyses in artificially spiked human serum.

The detection of extracellular vesicles, or exosomes are important mediators in intercellular communication and often play a role in cancer progression. CD63 is a key exosomal protein due to its distinctive cellular functions and association with many cancers. This describes a label-free electrochemiluminescence (ECL) nanoimmunosensor for the detection of CD63 protein over mesoporous carbon screen-printed electrode (MC-SPE) modified with novel nanocomposite of carbon nanochips (CNCs), iron oxide (Fe3O4) and nafion (NAF). Fourier-transform infrared spectroscopy and field emission scanning electron microscopy were used to analyse nanocomposite. All the analytical performance of fabricated CD63 immunosensor were conducted applying ECL. In spite of the simple fabrication strategies utilized, the fabricated immunosensor showcased a broad linear range to detect CD63 from 100 fg mL−1 to 10 ng mL−1, with a limit of detection of 100 fg mL−1, excellent selectivity, interference-resistance capability and potential to detect CD63 in real clinical samples.

Ultrasensitive biosensors have become a necessity in the world of scientific research, and several signal enhancement strategies have been employed to attain exceptionally low detection limits. Nanotechnology turns out to be a strong contender for signal amplification, as they can be employed as platform modifiers, catalysts, carriers or labels. Here, we have described the most recent advancements in the utilization of nanomaterials as signal amplification components in aptamer-based electrochemical biosensors. We have briefly reviewed the methods that utilized nanomaterials, namely gold and carbon, as well as nanocomposites such as: graphene, carbon nanotubes, quantum dots, and metal-organic frameworks.

Currently, the world is facing extreme turmoil due to the spread of the COVID-19 virus, but the effects of this detrimental virus have been exceedingly dreadful for developing countries like Bangladesh. Being a developing country, Bangladesh has many social concerns to overcome and among them child labour has been a constant one for decades. It has been anticipated by several researchers that the contingency of COVID-19 virus would cause a surplus of such child labour occurrences throughout the world especially in the South Asian region. Considering the concern, we are proposing a benevolent yet technical solution that has the potential to eradicate the problem on a large scale. This paper beholds the idea of a child labour detection technique to prevent such illegal instances taking place in the construction areas, brickfields, coal mining fields, make-up product mining fields, etc. on the South Asian countries like Bangladesh, India, Pakistan, Nepal. Considering the unavailability of an open sourced child labourers dataset in the context of the mentioned region, a noble dataset of such kind has been formulated in this project. A child labour recognizing model has been built based on Convolutional Neural Network (CNN) using the dataset. Additionally, the resulting accuracy has also been compared using two well-known transfer learning methods for better perception. The CNN model possessed 90.625% test accuracy whereas the two transfer learning based models showed 95.312% and 96.875% respectively.

A synergistic nanocomposite film composed of gold nanourchins (AuNU), oxidised carbon nanohorns (CNH), and chitosan functioned as an electrode modifier in the fabrication of the sensitive lipocalin-2 (LCN-2) aptasensor. The AuNUs/CNH/CS composite increased the surface area and thereby amplified the signal transduction. The amine-terminated LCN-2 aptamer was immobilised through the amide bond formed between the carboxyl group of polyglutamic acid (PGA) and the amine group of aptamer. Interaction of LCN-2 with the aptamer caused conformational changes in the structure of the aptamer. This generated higher conductivity, resulting in increased DPV peak current. The DPV signal increased with increasing concentration of LCN-2, and the change in signal was used for quantitative detection. The proposed aptasensor was able to detect LCN-2 in the linear range of 0.1 - 100.0 pg mL-1, with a low detection limit of 10 fg mL-1. The aptasensor showed high sensitivity, selectivity, reproducibility, and was able to detect LCN-2 in serum samples.

This study presents a novel and ultrasensitive electrochemiluminescence approach for the quantitative assessment of creatine kinase MB (CK-MB). Both carbon, carbon nano-onions (CNOs) and metal-based nanoparticles, such as gold nanoparticles (AuNPs) and iron oxide (Fe3O4), were combined to generate a unique nanocomposite for the detection of CKMB. The immunosensor construction involved the deposition of the nanocomposite on the working electrode, followed by the incubation of an antibody and a blocking agent. Tris(2,2′-bipyridyl)-ruthenium(II) chloride ([Ru(bpy)3]2+Cl) was used as a luminophore, where tri-n-propylamine (TPrA) was selected as the co-reactant due to its aqueous immobility and luminescence properties. The analytical performance was demonstrated by cyclic voltammetry on ECL. The characterization of each absorbed layer was performed by cyclic voltammetry (CV) and chronocoulometry (CC) techniques in both EC and ECL. For further characterization of iron oxide, gold nanoparticles and carbon nano-onions, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD) were performed. The proposed immunosensor showcases a wide linear range (10 ng mL−1 to 50 fg mL−1), with an extremely low limit of detection (5 fg mL−1). This CKMB immunosensor also exhibits remarkable selectivity, reproducibility, stability and resistance capability towards common interferences available in human serum. In addition, the immunosensor holds great potential to work with real serum samples for clinical diagnosis.

Facial expression recognition in a wild situation is a challenging problem in computer vision research due to different circumstances, such as pose dissimilarity, age, lighting conditions, occlusions, etc. Numerous methods, such as point tracking, piecewise affine transformation, compact Euclidean space, modified local directional pattern, and dictionary-based component separation have been applied to solve this problem. In this paper, we have proposed a deep learning–based automatic wild facial expression recognition system where we have implemented an incremental active learning framework using the VGG16 model developed by the Visual Geometry Group. We have gathered a large amount of unlabeled facial expression data from Intelligent Technology Lab (ITLab) members at Inha University, Republic of Korea, to train our incremental active learning framework. We have collected these data under five different lighting conditions: good lighting, average lighting, close to the camera, far from the camera, and natural lighting and with seven facial expressions: happy, disgusted, sad, angry, surprised, fear, and neutral. Our facial recognition framework has been adapted from a multi-task cascaded convolutional network detector. Repeating the entire process helps obtain better performance. Our experimental results have demonstrated that incremental active learning improves the starting baseline accuracy from 63% to average 88% on ITLab dataset on wild environment. We also present extensive results on face expression benchmark such as Extended Cohn-Kanade Dataset, as well as ITLab face dataset captured in wild environment and obtained better performance than state-of-the-art approaches.

In this work, a nanocomposite of gold nanoparticles (AuNPs), carbon nano-onions (CNOs), single-walled carbon nanotubes (SWCNTs) and chitosan (CS) (AuNPs/CNOs/SWCNTs/CS) was prepared for the development of highly sensitive electrochemical immunosensor for the detection of carcinoembryonic antigen (CEA), clinical tumor marker. Firstly, layer-by-layer fabrication of the CEA-immunosensors was studied using cyclic voltammetry (CV) and square wave voltammetry (SWV). By combining the advantages of large surface area and electronic properties of AuNPs, CNOs, SWCNTs, and film forming properties of CS, AuNPs/CNOs/SWCNTs/CS-nanocomposite-modified glassy carbon electrode showed a 200% increase in effective surface area and electronic conductivity. The calibration plot gave a negative linear relationship between log[concentration] of CEA and electrical current with a correlation coefficient of 0.9875. The CEA-immunosensor demonstrated a wide linear detection range of 100 fg mL−1 to 400 ng mL−1 with a low detection limit of 100 fg mL−1. In addition to high sensitivity, reproducibility and large stability, CEA-immunosensor provided an excellent selectivity and resistant-to-interference in the presence of other antigens in serum and hence a potential to be used with real samples.

Porcine adulteration of food is objectionable to a sizeable percentage of the global population owing to health concerns and/or religious faiths. However, unintentional or intentional porcine adulteration is common in the food industry - for which a strong demand prevails for a fast and sensitive method to detect and quantify porcine DNA in foods. In this study, we are reporting the development of a real-time qPCR assay based on the novel ZEN™ probe for the fast and sensitive detection of porcine DNA in real food samples. The assay's specificity to porcine DNA was confirmed against nine species. Standard curve was developed and sensitivity of the assay was tested with six ten-fold dilutions of the DNA standard. Our novel assay successfully detected as low as 1 pg/μl of porcine DNA and as low as 0.001% pork in the raw pork-chicken binary mixture.

In this paper, we address the combination of the active learning (AL) and semi-supervised (SSL) learnings, called ASSL, to leverage the strong points of the both learning paradigms for improving the performance of object detection. Considering the pros and cons of the AL and SSL learning methods, ASSL where SSL method provides the incremental improvement of semi-supervised detection performance by combining the concept of diversity imported from AL methods. The proposed method demonstrates outstanding performance compared with state-of-art methods on the challenging Caltech pedestrian detection dataset, reducing the miss rate to 12.2%, which is significantly smaller than current state-of-art. In addition, extensive experiments have been carried out using ILSVRC detection dataset and online evaluation for activity recognition.

Performance of busbar insulators is affected by the accumulation of pollutants on their surfaces. The combined action of both the chemical and natural dust pollutants accumulated on the surfaces of the insulators results in the likelihood of surface flashovers. In this paper, the pollution level in terms of equivalent salt deposit density (ESDD) and chemical analyses of a 12-kV generator busbar insulators are carried out to identify the impacts of pollutants presented on the insulators. It is found by actual measurements that the ESDD values of five types of busbar insulators are 0.569, 1.66, 1.61, 1.58, and $$2.44~\hbox {mg}/\hbox {cm}^{2}$$ , respectively, and all of these values are beyond the acceptable limits. New mathematical models are proposed in between ESDD and chemical anions/cations using a multivariable linear regression analysis. A positive correlation is found between ESDD and cations for the T1-type insulator, whereas for the T5-type insulator, it is found between ESDD and anions.

In this paper, we propose an open-set object detection framework based on a dynamic hierarchical structure with incremental learning capabilities for unseen object classes. We were motivated by the observation that deep features extracted from visual objects show a strong hierarchical clustering property. The hierarchical feature model (HFM) was used to learn a new object class by using collaborative sampling (CS), and open-set-aware active semi-supervised learning (ASSL) algorithms. We divided object proposals into superclasses by using the agglomerative clustering algorithm. Data samples in each superclass node were classified into multiple augmented class nodes instead of directly associating with regular object classes. One or more augmented class nodes are related to a regular object class, and each augmented class has only one superclass. Object proposals from inexperienced data distribution are assigned to an augmented class node. Dynamic HFM nodes in the decision path are assembled to constitute an ensemble prediction, and the new augmented object is associated with a new regular object class. Our experimental results showed that the proposed method uses standard benchmark datasets such as PASCAL VOC, MS COCO, ILSVRC DET, and local datasets to perform better than state-of-the-art techniques.

Analyses to ensure food safety and quality are more relevant now because of rapid changes in the quantity, diversity and mobility of food. Food-contamination must be determined to maintain health and up-hold laws, as well as for ethical and cultural concerns. Real-time polymerase chain reaction (RT-PCR), a rapid and inexpensive quantitative method to detect the presence of targeted DNA-segments in samples, helps in determining both accidental and intentional adulterations of foods by biological contaminants. This review presents recent developments in theory, techniques, and applications of RT-PCR in food analyses, RT-PCR addresses the limitations of traditional food analyses in terms of sensitivity, range of analytes, multiplexing ability, cost, time, and point-of-care applications. A range of targets, including species of plants or animals which are used as food ingredients, food-borne bacteria or viruses, genetically modified organisms, and allergens, even in highly processed foods can be identified by RT-PCR, even at very low concentrations. Microfluidic RT-PCR eliminates the separate sample-processing step to create opportunities for point-of-care analyses. We also cover the challenges related to using RT-PCR for food analyses, such as the need to further improve sample handling.

Rapid, sensitive, and selective pathogen detection is of paramount importance in infectious disease diagnosis and treatment monitoring. Currently available diagnostic assays based on polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) are time-consuming, complex, and relatively expensive, thus limiting their utility in resource-limited settings. Loop-mediated isothermal amplification (LAMP) technique has been used extensively in the development of rapid and sensitive diagnostic assays for pathogen detection and nucleic acid analysis and hold great promise for revolutionizing point-of-care molecular diagnostics. Here, we review novel LAMP-based lab-on-a-chip (LOC) diagnostic assays developed for pathogen detection over the past several years. We review various LOC platforms based on their design strategies for pathogen detection and discuss LAMP-based platforms still in development and already in the commercial pipeline. This review is intended as a guide to the use of LAMP techniques in LOC platforms for molecular diagnostics and genomic amplifications.

In this study, Gold-microrods (AuMRs), Pd-nanoparticles (PdNPs), and Polyaniline (PANI) nanocomposite-interface was fabricated on the screen-printed carbon-microelectrode (SPE). Each layer of the interface was characterised using field emission-scanning electron microscopy (FE-SEM) and cyclic voltammetry (CV). The fabricated SPE/AuMRs/PdNPs/PANI interface demonstrated the highest electronic current and showed excellent peroxidase-mimic towards H2O2 using chronoamperometry (CA). Furthermore, the SPE/AuMRs/PdNPs/PANI interface was utilised for the construction of a highly sensitive label-free electrochemical biosensor for the detection of Tpm in seafood samples. Label-free electrochemical detection of the Tpm was performed using both CA and differential pulse voltammetry (DPV) techniques. Preliminary data showed that both methods could detect Tpm as low as 0.01 pg/mL. Moreover, the developed biosensor for the detection of Tpm demonstrated excellent selectivity, high reproducibility and longer stability with an evident potential to detect Tpm in real seafood samples.

Recent advancements in the field of computer vision with the help of deep neural networks have led us to explore and develop many existing challenges that were once unattended due to the lack of necessary technologies. Hand Sign/Gesture Recognition is one of the significant areas where the deep neural network is making a substantial impact. In the last few years, a large number of researches has been conducted to recognize hand signs and hand gestures, which we aim to extend to our mother-tongue, Bangla (also known as Bengali). The primary goal of our work is to make an automated tool to aid the people who are unable to speak. We developed a system that automatically detects hand sign based digits and speaks out the result in Bangla language. According to the report of the World Health Organization (WHO), 15% of people in the world live with some kind of disabilities. Among them, individuals with communication impairment such as speech disabilities experience substantial barrier in social interaction. The proposed system can be invaluable to mitigate such a barrier. The core of the system is built with a deep learning model which is based on convolutional neural networks (CNN). The model classifies hand sign based digits with 92% accuracy over validation data which ensures it a highly trustworthy system. Upon classification of the digits, the resulting output is fed to the text to speech engine and the translator unit eventually which generates audio output in Bangla language. A web application to demonstrate our tool is available at http://bit.ly/signdigits2banglaspeech.

Legionella pneumophila is a waterborne pathogen that causes respiratory ailments including Pontiac fever and Legionnaires’ disease. A culture-free, fast and sensitive detection technique is very important for detection of the pathogen. The present study describes the implementation of rapid cycle real-time PCR in the detection of Legionella pneumophila in water through design and development of a real-time qPCR assay based on the ZENTM probe chemistry. The assay targeted the mip gene for the fast and specific detection of Legionella pneumophila. The novel assay was very specific and fast as the amplification was obtained within 30 minutes. Sensitivity of the assay as evaluated in terms of its limit of detection (LoD) was as low as 100 cells/reaction with the quantification range between 1x103 and 1x107 cells/reaction. The assay has been confirmed for repeatability and reproducibility with approximately less than 1% mean intra- and inter-assay variations (CV%). Therefore, the assay reported can be used for a fast, sensitive and specific culture-free detection and quantification of Legionella pneumophila in water.

Nanoparticles have been widely developed and shown to have intrinsic enzymatic ability, and are used in biosensors. Compared to biological enzymes used in biosensors, which are expensive and tedious to harvest, enzyme-mimic nanoparticles or nanozymes are both more stable and sensitive. An important area in this work is the development of a simple detection principle of immunosensor based on the one-step synthesis of silver nanoparticle seeded onto a gold core. The gold-silver core-shell nanoparticle acts as a peroxidase mimic, which enables them to oxidise 3,3',5,5'-tetramethylbenzidine (TMB) with H2O2, giving a colourimetric response. Herein, the analytical performance of the nanozyme is exploited to detect haptoglobin as a model analyte in a 96-well plate and measured the colourimetric product using spectrophotometer. The sensitivity of the immunosensor was as low as 100 pg mL-1. The viability of our immunosensor was shown to have good selectivity and satisfactory recovery in real serum samples.

In recent years, tremendous advances have been made in biosensors based on nanoscale electrochemical immunosensors for use in the fields of agriculture, food safety, biomedicine, quality control, and environmental and industrial monitoring. One of the main challenges in biosensors is achieving an extremely low limit of detection. A current trend to address this is fabrication of highly sensitive electrochemical immunosensors through the use of nanotechnology; for example, biofunctionalization of nanomaterials that are used as a catalyst, label or biosensing transducer. This review introduces recent advances in signal amplification strategies for electrochemical immunosensing applications, with a particular focus on nanotechnology. The strategies employed and their general principles to increase sensitivity, as well as the advantages and limitations of electrochemical immunosensors developed to date are also considered.

Electrochemical bio-sensing using paper-based detection systems is the main focus of this review. The different existing designs of 2-dimensional and 3-dimensional sensors, and fabrication techniques are discussed. This review highlights the effect of adopting different sensor designs, distinct fabrication techniques, as well as different modification methods, in order to produce reliable and reproducible reading. The use of various nanomaterials have been demonstrated in order to modify the surface of electrodes during fabrication to further enhance the signal for subsequent analysis. The reviewed sensors were classified into categories based on their applications, such as diagnostics, environmental and food testing. One of the major advantages of using paper-based electrochemical sensors is the potential for miniaturization, which only requires relatively small amount of samples, and the low cost for the purpose of mass production. Additionally, most of the devices reviewed were made to be portable, making them well-suited for on-site detection. Finally, paper-based detection is an ideal platform to fabricate cost-effective, user-friendly and sensitive electrochemical biosensors, with large capacity for customization depending on functional needs.

Enzyme-linked immunosorbent assay (ELISA) is a popular detection technique for the screening and diagnosis of diseases. The sensitivity of ELISA can be increased by the incorporation of nanoparticles. Through this article, we discuss the utilization of nanoparticles in ELISA. Nanoparticles possess an intrinsic biological peroxidase-like activity which allows it to act as an enzyme mimic for the development of an improved analysis method. Different nanoparticles (gold nanoparticles, silver nanoparticles, etc.) carry different peroxidase-mimic characteristics. Besides this, nanoparticles can also perform as a colorimetric substrate in ELISA where it gives a more prominent color change compared to the commonly used colorimetric substrate TMB. This article also focuses on the mechanisms behind this color change including aggregation, in situ nanoparticle growth, seeding, and etching.

People with disabilities (PWD) face a number of challenges such as obstacle avoidance or taking a minimum path to reach a destination while travelling or taking public transport, especially in airports or bus stations. In some cases, PWD, and specifically visually impaired people, have to wait longer to overcome these situations. In order to solve these problems, the computer-vision community has applied a number of techniques that are nonetheless insufficient to handle these situations. In this paper, we propose a visual simultaneous localization and mapping for moving-person tracking (VSLAMMPT) method that can assist PWD in smooth movement by knowing a position in an unknown environment. We applied expected error reduction with active-semisupervised-learning (EER&ndash, ASSL)-based person detection to eliminate noisy samples in dynamic environments. After that, we applied VSLAMMPT for effective smoothing, obstacle avoidance, and uniform navigation in an indoor environment. We analyze the joint approach symmetrically and applied the proposed method to benchmark datasets and obtained impressive performance.

One of the significant challenges in healthcare is the development of point-of-care (POC) diagnostics. POC diagnostics require low-cost devices that offer portability, simplicity in operation and the ability for high-throughput and quantitative analysis. Here, we present a novel roll-to-roll ribbon fluid-handling device for electrochemical real-time monitoring of nucleic acid (NA) amplification and bacteria detection. The device rendered loop-mediated isothermal amplification (LAMP) and real-time electrochemical detection based on the interaction between LAMP amplicon and the redox-reactive osmium complex. We have shown the detection of 30 CFU/ml of Escherichia coli (in the range between 30 and 3×107 CFU/ml) and 200 CFU/ml of Staphylococcus aureus (in the range of 200–2×105 CFU/ml) cultured samples in both real-time and end point detection. This device can be used for the detection of various Gram-negative and a number of Gram-positive bacterial pathogens with high sensitivity and specificity in a high-throughput format. Using a roll-to-roll cassette approach, we could detect 12 samples in one assay. Since the LAMP and electrochemical analysis are implemented within sealed flexible biochips, time-consuming processing steps are not required and the risk of contamination is significantly reduced.

This study describes a highly sensitive electrochemical immunosensor for the detection of human chorionic gonadotropin (hCG) that uses gold nanoparticles (AuNP) as the electrochemical label and graphene as electrode material. The primary antibody was first immobilized on the graphene working electrode surface by physical adsorption. Antigen hCG was then added and sandwiched with a secondary antibody labelled with AuNPs. After this, a series of sandwich-type immunoreactions were performed on the electrode, AuNPs were quantified by subjecting the immunocomplex to a preoxidation process of high potential at 1.2 V for 40 s and immediately reduced and scanned by differential pulse voltammtery (DPV). Electrodeposition of gold during the reduction stage of the redox reaction was determined by cyclic voltammetry (CV) that showed a linear relationship with the different hCG concentrations. In this study, a linear relationship between reduction peak current signals and hCG concentration from 0 to 500 pg mL−1 (correlation coefficient of 0.97351) with a detection limit of 5 pg mL−1 was obtained.

This study aims to demonstrate the potential use of reusable electrochemical sensor for detecting pork DNA in solution. The approach was based on electrochemical principle in which the electrostatic interaction between DNA and redox species will generate detectable signal upon introduction of electrical charge. In this study, Ruthenium Hexaamine (RuHex) was used as the redox species and result was based on the output current. Coupled with highly specific Polymerase Chain Reaction (PCR) primers designed for pork DNA, this study has successfully demonstrated the reliability of proposed novel detection approach that utilized reusable electrochemical sensor and can potentially be developed into a rapid detection tool for halal and kosher food industry.

In this work we exploited the electrostatic interaction of double stranded DNA (dsDNA) with drug components to construct a simple, but highly sensitive, DNA-electrochemical sensor for detecting ciprofloxacin. The following straightforward three-step procedure was performed to determine ciprofloxacin: (i) dsDNA-layer immobilization on the surface of the working graphene-modified screen-printed carbon electrode; (ii) dsDNA-ciprofloxacin interaction for 2 min; and (iii) electrochemical measurement using square-wave voltammetry. An increased oxidation of the guanine component was observed, at +1.0 V, as a result of the electrostatic interaction of positively charged ciprofloxacin with the negatively charged nucleic acid sugar phosphate. Based on the International Conference on Harmonization Guidelines, a linear relationship between the guanine oxidation peak and ciprofloxacin concentration (0.1 to 100 μM) was obtained with a detection limit of 0.1 μM. Our developed sensor is straightforward to construct and use, requiring no multi-step time-consuming preconditioning of electrodes. It is highly sensitive and selective in the detection of ciprofloxacin, and has the potential to be useful in the future fabrication of rapid and portable on-site food safety analysis devices.

Electrochemiluminescence (ECL) has been widely rendered for nucleic acid testing. Here, we integrate loop-mediated isothermal amplification (LAMP) with ECL technique for DNA detection and quantification. The target LAMP DNA bound electrostatically with [Ru(bpy)3](+2) on the carbon electrode surface, and an ECL reaction was triggered by tripropylamine (TPrA) to yield luminescence. We illustrated this method as a new and highly sensitive strategy for the detection of sequence-specific DNA from different meat species at picogram levels. The proposed strategy renders the signal amplification capacities of TPrA and combines LAMP with inherently high sensitivity of the ECL technique, to facilitate the detection of low quantities of DNA. By leveraging this technique, target DNA of Sus scrofa (pork) meat was detected as low as 1pg/µL (3.43×10(-1)copies/µL). In addition, the proposed technique was applied for detection of Bacillus subtilis DNA samples and detection limit of 10pg/µL (2.2×10(3)copies/µL) was achieved. The advantages of being isothermal, sensitive and robust with ability for multiplex detection of bio-analytes makes this method a facile and appealing sensing modality in hand-held devices to be used at the point-of-care (POC).

Microfluidic electrochemical biosensor for performing Loop-mediated isothermal amplification (LAMP) was developed for the detection and quantification of Escherichia coli. The electrochemical detection for detecting the DNA amplification was achieved using Hoechst 33258 redox molecule and linear sweep voltametry (LSV). The DNA aggregation and minor groove binding with redox molecule cause a significant drop in the anodic oxidation of LSV. Unlike other electrochemical techniques, this method does not require the probe immobilization and the detection of the bacteria can be accomplished in a single chamber without DNA extraction and purification steps. The isothermal amplification time has a major role in the quantification of the bacteria. We have shown that we could detect and quantify 24 CFU/ml of bacteria and 8.6 fg/μl DNA in 60 min and 48 CFU/ml of bacteria in 35 min in LB media and urine samples. We believe that this microfluidic chip has great potential to be used as a point of care diagnostic (POC) device in the clinical/hospital application.

In this study, we have developed shRNA expression vector and determined their RNAi activity. The endothelial cells (ECs) were transfected with the Amphoterin-induced gene and ORF (AMIGO-2) expression vector or AMIGO-2 shRNA expression vector using hemagglutinating virus of Japan (HVJ) Envelope Vector and then the cells were assayed for AMIGO-2 mRNA levels and its survival. In our study, here we have performed expression of AMIGO gene family in primary cultured human vascular cells, and found predominant expression on human microvascular endothelial cells (HMVEC). The expression of AMIGO-2 gene in HMVEC under hypoxia, showed AMIGO-2 gene decreased significantly, suggested that AMIGO-2 maybe involved in vascular remodeling. Based on our study of AMIGO-2 down regulation and over expression showed the down regulation appeared to cause cell death of ECs, and over expression appeared to protect EC death due to reactive oxygen species. Finally, our this study suggest that AMIGO-2 may have an important role in the vascular system as a cell survival promoting factor for vascular ECs, probably as being involved in vascular development, angiogenesis and/or vascular remodeling. (where x can be any amino acid and L could also be replaced by V, I or F). The LRRs are protein-protein interaction motifs and are found in a large number of proteins (2). Some LRR-containing plasma membrane proteins are expressed almost exclusively in the brain, implying specific functions in the central nervous system. Expression analyses indicate that AMIGO-1 is specifically detected in axonal fibers and tracts in the brain. AMIGO-2 and AMIGO-3 expressions are more widespread but are also brain-enriched. Members of the AMIGO family exhibit both homophilic and heterophilic binding, which suggest that they function as novel cell adhesion molecules in neurons. Immobilized ectodomain of AMIGO-1 promoted neurite extension of cultured hippocampal neurons, but, when added to the medium, the same soluble AMIGO-1 inhibited fasciculation of neurites.