Conventional Detection Methods Research Articles

Genetic screening of α-thalassemia fusion gene using routine flow-through hybridization

ObjectiveThe fusion gene is a rare form of α-thalassemia. Patients carrying the fusion gene could be misdiagnosed as normal or -α4.2deletion by the conventional thalassemia detection methods. The aim of this study was to present the detection of fusion genes using routine flow-through hybridization, as well as to analyze hematological and molecular characteristics.MethodsSamples were collected at our hospital from January 2019 to January 2024. Common thalassemia mutations in the Chinese population were conducted by flow-through hybridization. Samples showing faint coloration at the -α4.2 mutation site on hybridization membrane were considered suspicious. Samples detected as suspicious for -α4.2deletion were rechecked by conventional Gap-PCR. Those samples suspected of having -α4.2deletions were finally confirmed with specific primers for Gap-PCR and Sanger sequencing.ResultsOf the 32,083 samples, 25 samples (0.08%) were detected as suspected of having -α4.2 deletion by flow-through hybridization. However, upon reevaluation wtih conventional Gap-PCR reagents capable of detecting -α4.2 deletion, all were found to be negative for the deletion. Specific primers for Gap-PCR were designed, and fusion gene fragments were amplified. DNA sequencing of the HBA gene showed a 7-base mutation corresponding to the α-thalassemia fusion gene. Among the 25 samples, 22 were heterozygous carriers. Three samples were combined: one with Hb QS, one with β-thalassemia, and one with Hb G-Honolulu.Most hematological indices and capillary electrophoresis results were in the normal reference range.ConclusionThe fusion gene was present in 0.08% of the population in the Guangzhou region of Guangdong province, southern China. Conventional genetic methods tend to misdiagnose the fusion gene but can be effectively screened with flow-through hybridization.

Automatic Watershed Segmentation of Cancerous Lesions in Unsupervised Breast Histology Images

Segmentation of nuclei in histology images is key in analyzing and quantifying morphology changes of nuclei features and tissue structures. Conventional diagnosis, segmenting, and detection methods have relied heavily on the manual-visual inspection of histology images. These methods are only effective on clearly visible cancerous lesions on histology images thus limited in their performance due to the complexity of tissue structures in histology images. Hence, early detection of breast cancer is key for treatment and profits from Computer-Aided-Diagnostic (CAD) systems introduced to efficiently and automatically segment and detect nuclei cells in pathology. This paper proposes, an automatic watershed segmentation method of cancerous lesions in unsupervised human breast histology images. Firstly, this approach pre-processes data through various augmentation methods to increase the size of dataset images, then a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, data enhancement techniques namely; erosion, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and, assigns them to their relevant labeled component binary mask. Once all binary masked groups have been determined, a deep-learning recurrent neural network from the Keras architecture uses this information to automatically segment nuclei objects with cancerous lesions and their edges on the image via watershed filling. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 11,151 images. This proposed method produced a significant evaluation result of 98% F1-accuracy score.

Synergistic applications of quantum dots and magnetic nanomaterials in pathogen detection: a comprehensive review.

The rapid and accurate detection of pathogens is crucial for effective disease prevention and management in healthcare, food safety, and environmental monitoring. While conventional pathogen detection methods like culture-based techniques and PCR are sensitive and selective, they are often time-consuming, require skilled operators, and are not suitable for point-of-care or on-site testing. To address these limitations, innovative sensor technologies have emerged that leverage the unique properties of nanomaterials. Quantum dots (QDs) and magnetic nanomaterials are two classes of nanomaterials that have shown particular promise for pathogen sensing. This review comprehensively examines the synergistic applications of QDs and magnetic nanomaterials for detecting bacteria, viruses, phages, and parasites.

Edge Detection of Source Body from Magnetic Anomaly Based on ResNet

Utilizing magnetic anomaly data for effective edge detection of source bodies can provide crucial evidence for the delineation of geological units and the division of fault structures. However, the existing edge detection methods of source bodies from magnetic anomalies are influenced by factors such as the source bodies’ burial depth, magnetization direction, and mutual interference of magnetic anomalies, leading to errors in subsequent interpretation tasks. The advanced convolutional neural network possesses robust capabilities for feature representation and deep learning, prompting this paper to introduce an edge detection method for source bodies based on convolutional neural networks. The issue is initially framed as a semantic segmentation problem, and four network architectures aimed at edge detection of a source body from magnetic anomaly are designed and modified based on the U-Net and ResNet. Subsequently, a multitude of high-quality sample data sets are constructed using models with varying locations, scales, quantities, and physical properties to train the network. This paper then details model experiments that escalate from simple to complex, taking into account the combined effects of burial depth and inclined magnetization on edge detection. Compared to conventional edge detection methods, the method proposed in this paper is shown to accurately identify edges of source bodies at various depths with little impact from inclined magnetization and can automatically extract edge information without manual intervention. The method’s efficacy is corroborated through real data tests.

Enhanced detection of Opisthorchis viverrini infection: A comparative evaluation of modified one-step FECT and conventional diagnostic methods in low-intensity setting

The formalin-ethyl acetate concentration technique (FECT) is one of the most sensitive diagnostic method not only for all helminths, but also for Opisthorchis viverrini infections in stool examinations. However, it remains a diagnostic problem for light infections. We modified the one-step FECT to determine the low-intensity of O. viverrini infection and compared with various conventional detection methods. The study utilized 160 egg-positive and 160 randomly negative stool samples for O. viverrini eggs by conventional FECT (cFECT) to compare the methods, including the simple smear, the Kato-Katz method, the two commercial stool examination kits, and the one-step FECT. Our results showed that the one-step FECT method had the highest sensitivity (95.6 %), followed by cFECT (87.9 %), the Kato-Katz (55.5 %), Aquisfek SF-FIX (48.3 %), simple smear (42.3 %), and Mini Parasep® SF (35.1 %). The ability of one-step FECT exhibited better ability to detect low parasite intensities compared to the cFECT (18 eggs per gram (e.p.g.) versus 34 e.p.g.) and the other conventional diagnostic methods. In addition, the investigation of O. viverrini infection in endemic regions in northeastern Thailand based on 3900 fecal samples revealed that the one-step FECT with an intensity of 66.8 e.p.g. (range 18–226) was significantly higher in sensitivity than cFECT, which had an intensity of 58.0 e.p.g. (range 34–214). Interestingly, fecal samples with less than 50 e.p.g. could not be detected by cFECT in 67 % of cases, and 69 out of 3900 samples were negative. In conclusion, one-step FECT improves the detection of low-intensity O. viverrini infection, which is suitable for parasites screening, especially for low-intensity infections in the community.

A simple lable-free aptasensor based on liquid exfoliated graphene modified electrode for chloramphenicol detection in milk

Antibiotic residues in animal derived foods considered as one of the most serious food contamination poses significant risks to human health. Conventional methods for antibiotic residue detection rely on expensive and bulky instruments, complicated sample pretreatment and time-consuming, thus limiting their applications. Consequently, developing miniaturized and low-cost analytical technology for ultrasensitive monitoring and accurate evaluation of antibiotic residues in foods is of great significance. Herein, a simple and label-free electrochemical sensing platform for the highly sensitive detection of chloramphenicol (CAP) in milk was demonstrated by employing aptamer specific for CAP as a biological recognition element and liquid exfoliated graphene (LEG) modified indium tin oxide electrode (LEG/ITO) as substrate. LEG is believed to be the most economic few-layer graphene with remarkable electrical properties and larger specific surface area not only promotes electron transfer but also provides a large number of binding sites for aptamers to be assembly immobilized via the π–π stacking of DNA bases. Under optimal experimental factors, the aptasensor displayed a wider linear range for detecting CAP (1 fM ∼ 100 nM), accompanied by a limit of detection (LOD) (1 fM), as well as satisfactory performance with specificity, reproducibility, and stability. In addition, the designed strategy allowed the direct analysis of actual milk samples and the recovery rates were from 97.92 % to 103.34 %, rendering a ideal sensing strategy for quantitative determination of various analytes in food by adapting the specific aptamer.

Quantitative reconstruction of debonding in ultra-thin honeycomb sandwich panel based on noncontact thermoelastic laser tapping

Aluminum skin honeycomb sandwich panels (HSPs) are commonly used in aerospace structures. Conventional contact-based detection methods may scratch the skin surface or result in couplant immersion, and thus a novel quantitative, baseline-free, and noncontact laser tapping (LT) algorithm is proposed to detect the debonding in the aerospace HSP with an ultra-thin 0.3 mm thick skin and a 0.04 mm thick honeycomb wall. The algorithm features an LT C-scan imaging with a baseline-free debonding index, followed by an image processing-based reference-reconstruction quantification algorithm. The numerical results validate the robustness of the proposed algorithm against strong noises with an average accuracy over 99 %. Experimentally, the proposed algorithm shows the detectability of a single debonding wall not fulfilled with the conventional contact ultrasound, and the accuracies of 90.80 % and 95.81 % experimentally in quantifying two types of debonding defects are achieved, spotlighting the application potential of the proposed technique to noncontact HSP debonding detection.

Detection of E. coli using bacteriophage T7 and analysis of excitation‑emission matrix fluorescence spectroscopy

Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and E. coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier and Random Forest, demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 hours. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodologies, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.

Bronchoalveolar lavage fluid and lung biopsy tissue metagenomic next-generation sequencing in the diagnosis of pulmonary cryptococcosis

ObjectiveTo evaluate the diagnostic value of metagenomic next-generation sequencing (mNGS) in pulmonary cryptococcosis (PC) using bronchoalveolar lavage fluid (BALF) and lung biopsy tissue specimens.MethodsIn this retrospective study, 321 patients diagnosed with lower respiratory tract diseases who underwent mNGS using BALF and LBT samples, between January 2021 and December 2023 were included. Individuals were classified into PC and non-PC groups according to the diagnostic criteria for PC, and conventional fungal cultures were performed. A serum/BALF cryptococcal antigen (CrAg) test was performed in some patients with PC. The diagnostic efficiencies of three methods for PC (mNGS, conventional culture, and CrAg) were compared. Additionally, two mNGS methods were used in this study: original mNGS (OmNGS, testing time from January 2021 to December 2022) and modified mNGS (MmNGS, testing time from January to December 2023). The diagnostic efficiency of the two mNGS methods on PC was simultaneously compared.ResultsAmong the 321 patients, 23 (7.2%) had PC and 298 (92.8%) did not. Compared with the composite reference standard for PC diagnosis, the sensitivity, specificity, and accuracy of mNGS for PC were 78.3% (95% confidence interval [CI], 55.8%–91.7%), 98.7% (95% CI, 96.4%–99.6%), and 97.2% (95% CI, 94.7%–98.7%), respectively. The sensitivity of mNGS was similar to that of CrAg (80.0%, 12/15) (P > 0.05). The diagnostic sensitivity of both mNGS and CrAg was higher than that of conventional culture (35.0%, 7/20) (P = 0.006, P = 0.016), and the combined detection of mNGS and CrAg further improved the diagnostic sensitivity of PC (93.3%, 14/15). The area under the receiver operating characteristic curve of mNGS was superior to that of conventional culture (0.885 vs. 0.675). In addition, the diagnostic sensitivity of PC was higher than that of OmNGS (P = 0.046).ConclusionThe sensitivity of mNGS is better than that of conventional culture. The combination of mNGS and CrAg improves the testing sensitivity of Cryptococcus. MmNGS could further improve the detection of Cryptococcus. Conventional PC detection methods are indispensable and mNGS can be used as a rapid and accurate auxiliary diagnostic method for PC.

SiamVIT: A patchwise network for gamma-ray point source detection

Conventional point source detection methods generally work in a pixelwise manner and can hardly exploit the overall semantic information of sources; consequently, these methods usually suffer from low precision. In this work we achieve point source detection in fully patchwise mode by proposing a siamese network called SiamVIT that includes a visual transformer (VIT). SiamVIT can effectively and accurately locate point sources from gamma -ray maps with high purity not only in higher flux regions, but also in lower flux regions, which is extremely challenging to achieve with state-of-the-art methods. SiamVIT consists of two VIT branches and a matching block. In the feature extraction stage, gamma -ray maps are fed into one VIT branch to obtain patch representations with adequate semantic and contextual information, whereas detection templates with location information are fed into the other branch to produce template representations. In the location stage, a patch representation and all template representations are fed into the matching block to determine whether the associated gamma -ray map patch contains a point source and where that point source is located, if applicable. We compare our proposed SiamVIT with the current advanced methods and find that SiamVIT has significantly better purity and completeness and a superior Dice coefficient on the test set. In addition, when point sources overlap, SiamVIT can better distinguish different point sources.

Study on AI Generated Fake-Media Detection

The rapid growth of AI-generated images, especially with techniques such as Generative Adversarial Networks (GANs), has complicated the ability to tell apart genuine content from artificial creations. This issue is vital for preserving the authenticity of visual media, where conventional detection methods often struggle. Current detection approaches concentrate on machine learning and deep learning techniques, including neural networks (CNNs). These methods aim to reveal subtle flaws and irregularities in images, like inconsistencies in pixel distribution and lighting, which serve as critical signs of AI involvement. The research emphasizes the necessity for ongoing development in detection technologies to keep up with the quick progress of AI advancements. Ensuring that these detection methods are accurate and dependable is crucial for protecting against misinformation and maintaining confidence in digital content. This paper reviewed Deepfake detection system.

Significance of Viable But Non-culturable (VBNC) State in Vibrios and Other Pathogenic Bacteria: Induction, Detection and the Role of Resuscitation Promoting Factors (Rpf).

Still, it remains a debate after four decades of research on surviving cells, several bacterial species were naturally inducted and found to exist in a viable but non-culturable (VBNC) state, an adaptive strategy executed by most bacterial species under different stressful conditions. VBNC state are generally attributed when the cells lose its culturability on standard culture media, diminish in conventional detection methods, but retaining its viability, virulence and antibiotic resistance over a period of years and may poses a risk to marine animals as well as public health and food safety. In this present review, we mainly focus the VBNC state of Vibrios and other human bacterial pathogens. Exposure to several factors like nutrient depletion, temperature fluctuation, changes in salinity and oxidative stress, antibiotic and other chemical stress can induce the cells to VBNC state. The transcriptomic and proteomic changes during VBNC, modification in detection techniques and the most significant role of Rpf in conversion of VBNC into culturable cells. Altogether, detection of unculturable VBNC forms has significant importance, since it may not only regain its culturability, but also reactivate its putative virulence determinants causing serious outbreaks and illness to the individual.

Occurrence of Extended-spectrum β-lactamase (ESBL) and Carbapenemase-producing Escherichia coli isolated from Childhood Diarrhoea in Yaoundé, Cameroon

IntroductionExtended-spectrum β-lactamase (ESBL)-producing pathogenic E. coli is a global public health issue, especially in sub-Saharan African countries such as Cameroon. It contributes to increase significantly hospital length of stay, morbidity, mortality and economic costs because of treatment failures. This study aims at determining the resistance background and virulence profiles of ESBL-E. coli isolates among childhood diarrhoea during the cholera outbreak occuring in Yaoundé, Cameroon.Materials and methodsDuring a four-month periods, from March 1st to June 30th, 2023, a total of 84 stool samples were collected from 90 under five children presenting clinical signs of gastroenteritis and attending four hospitals in Yaoundé, Cameroon. Bacterial identification was done using API20E and antimicrobial susceptibility test was performed using the Kirby-Bauer disc diffusion method. After extraction, genomic DNA was subjected to conventional and multiplex polymerase chain reaction methods (PCRs) for detection of resistance and virulence genes. Statistical analysis was performed using Epi info™ (7.2.5.0). Statistical significance was considered at a p-value < 0.05.ResultsOut of 150 patients contacted, 90 patients were enrolled, 84 samples were collected, 52.38%(44/84) and 3.57%(03/84) were confirmed as extended-spectrum β-lactamase and carbapenemase-producing E. coli respectively. The risk factors were analyzed, and children who drank natural fruit juice (OR: 0.4, p-value: 0.03) were found to be significantly associated with ESBL-producing E. coli. The ESBL-producing E. coli isolates showed a high level of resistance to amoxicillin-clavulanic acid, cefotaxime, ceftazidime, cefepime, colistin, and tetracycline. The blaCTX−M was more prevalent ß-lactamase resistance gene. The tetracycline resistance genes tet(A) and tet(B) were also detected. The most important virulence genes detected were FimH (81.81%) and papA (79.54%).ConclusionThese findings suggest implementing routine surveillance and screening for antimicrobial resistance among children under five. Antimicrobial stewardship strategies (ASP) need to be implemented to curb the emergence and dissemination of ESBL-producing E. coli. In addition, a national surveillance program for antimicrobial resistance needs to be implemented at local and regional levels in order to reduce morbidity in Cameroon.

Integration of FAERS, DrugBank and SIDER Data for Machine Learning-based Detection of Adverse Drug Reactions

AbstractTraditionally, disproportionality analysis (DPA) methods are employed for signal detection in pharmacovigilance, but these methods utilize only a limited portion of the data available from spontaneous event reports (SERs). This research aims to enhance signal detection by applying machine learning (ML) methods that can leverage additional data. We create a dataset by integrating SER data from the FDA Adverse Event Reporting System (FAERS) with biological and chemical data from DrugBank, and information on known adverse drug reactions (ADRs) from Side Effect Resource (SIDER). The known ADRs from SIDER are used to label the dataset for ML training. Using the AutoML library TPOT, ML models are trained on this dataset. Our findings indicate that ML models, even when trained with the same features as DPA methods, achieve higher recall and precision. Moreover, incorporating additional features related to drugs and events significantly boosts the performance of ML models. Analysis using the explainable AI (XAI) technique SHAP reveals that the drug name, event name, and fifth-level ATC code are the most influential features for model predictions. These ML models offer a promising alternative or supplement to conventional DPA methods for signal detection in pharmacovigilance.

Evaluation of mrkD, pgaC and wcaJ as biomarkers for rapid identification of K. pneumoniae biofilm infections from endotracheal aspirates and bronchoalveolar lavage

Klebsiella pneumoniae has been identified as one of the most important opportunistic pathogens responsible for nosocomial infections. Antibiotic resistance and the ability to form biofilms are the two main factors involved in the persistence of infections. Conventional detection methods involve culture isolation and identification followed by biofilm assay that takes 48–72 h. Timely detection of biofilm-forming resistant pathogens is essential to appropriately treat the infection with the right dose and combinations. The present study focuses on evaluating an RT-PCR panel using mrkD, pgaC, and wcaJ genes to screen for biofilm-forming K. pneumoniae from ETA/BAL specimens. The assay accurately identified K. pneumoniae harboring samples with a limit of detection of 1 ng/µl total RNA. Representative culture-negative-PCR-positive samples were subjected to metagenomics which identified K. pneumoniae reads in these samples confirming the specificity of RT-PCR. mrkD and pgaC act as K. pneumoniae specific identification whereas wcaJ acts as a negative marker for biofilm-forming K. pneumoniae. In addition, RT-PCR results correlated well with the phenotypic biofilm-forming assay. This RT-PCR assay is the first of its kind for rapid identification of biofilm-forming K. pneumoniae. The result of this study highlights that the rapid detection of K. pneumoniae biofilms based on the RT-PCR results coupled with clinical conditions would be appropriate to treat emerging infections or to prevent re-infections in clinical settings.

Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method

It is crucial to appropriately maintain automatic ticket gates (ATGs) to keep transportation operating smoothly in urban areas. Although the average failure rate of new ATGs is extremely low, continuous operation for many years might lead to unstable performance due to deterioration, and the need for periodic maintenance to avoid fatal faults might halt operations for extended periods. To detect anomalies at an early stage, “anomaly signs” can be utilized to flag ATGs for maintenance by service engineers before anomalies occur. In addition, to minimize the cost of ATG monitoring, the necessary computing resources should be minimized, which means using only light-weight statistical methods rather than deep learning or machine learning. In this paper, we focus on the automatic separation modules inside ATGs that separate multiple tickets by complicated mechatronic controls because this module is the major cause of maintenance calls from station attendants. We propose a simple anomaly sign detection, called the histogram limitation method (HLM). We evaluated the anomaly sign scores over time with maintenance timing and compared them with the conventional fast unsupervised anomaly detection method, Histogram-Based Outlier Score (HBOS) widely used in various domains. The experimental results using real field ATG monitoring data show that HLM successfully detected anomaly signs before a maintenance call was necessary, which is better performance compared with HBOS. Despite being a simple modification based on HBOS, HLM also provides anomaly sign scores that agree adequately with assessments by maintenance service engineers.

High-throughput DART-MS/MS for quantification of carboxymethyl lysine and carboxyethyl lysine in beef

Carboxymethyl lysine (CML) and carboxyethyl lysine (CEL), commonly found in heat-processed foods, pose considerable health risks. Conventional detection methods for these compounds are often slow and inefficient. This study presents a high-throughput and effective method utilizing Direct Analysis in Real-Time ionization coupled with triple quadrupole tandem mass spectrometry (DART-MS/MS) for quantifying CML and CEL in beef. The method demonstrated strong linearity between concentration and peak area across a range of 1–50 μg/mL, with detection and quantification limits of 0.15 μg/g and 0.6 μg/g, respectively. Remarkably, it enables the analysis of eight samples in just two minutes per run. Additionally, the use of buckwheat extract as an antioxidant during yak beef cooking resulted in a 7.72 % reduction in CML and a 1.46 % reduction in CEL. This study provides a robust and rapid method for detecting CML and CEL in food products, contributing to the improvement of food safety.

The Evolution of Illicit-Drug Detection: From Conventional Approaches to Cutting-Edge Immunosensors-A Comprehensive Review.

The increasing use of illicit drugs has become a major global concern. Illicit drugs interact with the brain and the body altering an individual's mood and behavior. As the substance-of-abuse (SOA) crisis continues to spread across the world, in order to reduce trafficking and unlawful activity, it is important to use point-of-care devices like biosensors. Currently, there are certain conventional detection methods, which include gas chromatography (GC), mass spectrometry (MS), surface ionization, surface-enhanced Raman spectroscopy (SERS), surface plasmon resonance (SPR), electrochemiluminescence (ECL), high-performance liquid chromatography (HPLC), etc., for the detection of abused drugs. These methods have the advantage of high accuracy and sensitivity but are generally laborious, expensive, and require trained operators, along with high sample requirements, and they are not suitable for on-site drug detection scenarios. As a result, there is an urgent need for point-of-care technologies for a variety of drugs that can replace conventional techniques, such as a biosensor, specifically an immunosensor. An immunosensor is an analytical device that integrates an antibody-based recognition element with a transducer to detect specific molecules (antigens). In an immunosensor, the highly selective antigen-antibody interaction is used to identify and quantify the target analyte. The binding event between the antibody and antigen is converted by the transducer into a measurable signal, such as electrical, optical, or electrochemical, which corresponds to the presence and concentration of the analyte in the sample. This paper provides a comprehensive overview of various illicit drugs, the conventional methods employed for their detection, and the advantages of immunosensors over conventional techniques. It highlights the critical need for on-site detection and explores emerging point-of-care testing methods. The paper also outlines future research goals in this field, emphasizing the potential of advanced technologies to enhance the accuracy, efficiency, and convenience of drug detection.

Voltammetric-based immunosensing of Newcastle disease virus on polyethylene glycol-containing self-assembled monolayer modified gold electrode

A voltammetric immunosensor for the detection of Newcastle disease virus (NDV) has been developed by employing polyclonal antibody targeting NDV (anti-NDV) as a bioreceptor. Anti-NDV was immobilized on polyethylene glycol (PEG)-containing self-assembled monolayer (SAM) which was activated with N-(3-dimethylaminopropyl)-N′-ethylcarbodiimidehydrochloride (EDC) and N-hydroxy succinimide (NHS) coupling on screen-printed gold electrode (SPGE). The introduction of PEG-containing SAM on the SPGE allowed the bioreceptor to covalently bound to the electrode surface whilst still providing a hydrophilic layer on the electrode which is important to greatly reduce non-specific bindings. The bioreceptor functionalized electrode was then allowed to be incubated with NDV-spiked samples. The electrode surface modification with PEG-containing SAM, immobilization of anti-NDV as bioreceptor, up to the detection of NDV were characterized electrochemically through differential pulse voltammetry (DPV) analysis in [Fe(CN)6]3- as the redox probe. Decrement of anodic current peak (Ipa) of [Fe(CN)6]3- was seen as the concentration of NDV increased from 0.156 to 20 HA μL-1 with the limit of detection (LoD) of 1.50 HA μL-1 at 3σ m-1. The detection of NDV in HA μL-1 unit in this study would ease interlaboratory interpretation as it was the same unit used in hemagglutination (HA) assay of conventional NDV diagnosis. The specificity of anti-NDV used as bioreceptor towards NDV was confirmed through western blot analysis, whilst the selectivity of the bioreceptor-functionalized electrode has been tested with allantoic fluid as the negative control in which no apparent changes of anodic peak (Ipa) has been seen. This simple, fast, and less laborious electrochemical detection method could become an alternative to the conventional method for NDV detection.

Development of a graphene field effect transistor-based immersible biosensor for immunodetection of the birch pollen allergen Bet v 1 in air samples

Pollen traps, the current gold standard to determine pollen load and thereby the allergy season, are not sufficient to determine the allergenic risk. Therefore, the establishment of highly sensitive assays for allergen measurement is of highest interest. Herein, a graphene field-effect transistor (GFET) was constructed on an interdigitated electrodes chip to develop an immersible biosensor, which was used to detect the major birch pollen allergen Bet v 1. Graphene was wet-transferred on interdigitated electrodes that contain a reference electrode used as a liquid gate in the GFET. Using a standard ELISA protocol, two different anti-Bet v 1 antibodies were chosen and immobilized on graphene for the specific capture of the target allergen. The sensitivity of the GFET biosensor was evaluated using a standard Ag/AgCl liquid gate electrode and a reference electrode when the chip was immersed in Bet v 1-containing solutions. The results showed a higher performance and sensitivity for Bet v 1 detection compared to a mediator release method, one of the most sensitive assays for allergen detection. Compared with conventional methods of allergen detection, these immersible biosensors significantly improved the speed and level of detection providing the foundation of a point-of-need platform for in-field application. Furthermore, the proposed technique provides both a new biosensor for allergen detection and a strategy for designing low-cost integrated biosensors.