Last few decades, viruses are a real menace to human safety. Therefore, the rapid identification of viruses should be one of the best ways to prevent an outbreak and important implications for ...medical healthcare. The recent outbreak of coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus which belongs to the single-stranded, positive-strand RNA viruses. The pandemic dimension spread of COVID-19 poses a severe threat to the health and lives of seven billion people worldwide. There is a growing urgency worldwide to establish a point-of-care device for the rapid detection of COVID-19 to prevent subsequent secondary spread. Therefore, the need for sensitive, selective, and rapid diagnostic devices plays a vital role in selecting appropriate treatments and to prevent the epidemics. During the last decade, electrochemical biosensors have emerged as reliable analytical devices and represent a new promising tool for the detection of different pathogenic viruses. This review summarizes the state of the art of different virus detection with currently available electrochemical detection methods. Moreover, this review discusses different fabrication techniques, detection principles, and applications of various virus biosensors. Future research also looks at the use of electrochemical biosensors regarding a potential detection kit for the rapid identification of the COVID-19.
•Electrochemical biosensing platform helps to early diagnosis of pathogenic viruses.•Different electrochemical transduction system strategies are explained.•We covered more than 125 recent research articles on virus biosensors.•The advantages of each biosensor for COVID-19 detection are highlighted.•The advantages, drawbacks, and future perspectives of biosensors are discussed.
An innovative biosensor assembly relying on glassy carbon electrodes modified with nanocomposites consisting of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) as a host matrix ...with functionalized gold nanoparticles (GCE/PEDOT:PSS-AuNPs) is presented for the selective and sensitive detection of xanthine (XA). The developed sensor was successfully applied for the quantification of XA in the presence of significant interferents like hypoxanthine (HXA) and uric acid (UA). Different spectroscopy and electron microscopy analyses were done to characterize the as-prepared nanocomposite. Calibration responses for the quantification of XA was linear from 5.0 × 10
−8
to 1.0 × 10
−5
M (
R
2
= 0.994), with a detection limit as low as 3.0 × 10
−8
(S/N = 3). Finally, the proposed sensor was applied for the analyses of XA content in commercial fish and meat samples and satisfactory recovery percentage was obtained.
An innovative biosensor with glassy carbon electrodes modified with poly(3,4-ethylenedioxythiophene) polystyrene sulfonate nanocomposites as a host matrix with functionalized gold nanoparticles for the selective and sensitive detection of xanthine.
Due to the epidemics of emerging microbial diseases worldwide, the accurate and rapid quantification of pathogenic bacteria is extremely critical. In this work, a highly sensitive DNA-based ...electrochemical biosensor has been developed to detect Vibrio cholerae using gold nanocube and 3-aminopropyltriethoxysilane (APTES) modified glassy carbon electrode (GCE) with DNA carrier matrix. Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) experiments were performed to interrogate the proposed sensor at each stage of preparation. The biosensor has demonstrated high sensitivity with a wide linear response range to target DNA from 10−8 to 10−14 (R2= 0.992) and 10−14 to 10−27 molL−1 (R2= 0.993) with a limit of detection (LOD) value of 7.41 × 10−30 molL−1 (S/N = 5). The biosensor also exhibits a selective detection behavior in bacterial cultures that belong to the same and distant genera. Moreover, the proposed sensor can be used for six consecutive DNA assays with a repeatability relative standard deviations (RSD) value of 5% (n = 5). Besides, the DNA biosensor shows excellent recovery for detecting V. cholerae in poultry feces, indicating that the designed biosensor could become a powerful tool for pathogenic microorganisms screening in clinical diagnostics, food safety, and environmental monitoring.
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•A novel electrochemical DNA biosensor is developed for sensitive detection of Vibrio cholerae.•The biosensor has demonstrated high sensitivity with a limit of detection value of 7.41 × 10−30 molL−1.•The proposed sensor could be reused for six consecutive DNA assays with a repeatability RSD value of 5%.•This technique might be a promising alternative for monitoring the bacterium in real samples.
Cancer is the most frequent life-threatening disease which has the highest mortality rate throughout the world. Diagnosis of cancer at the early stage can plays a critical role for its effective and ...successful treatment. Traditional diagnostic methods for cancer screening are costly, time-consuming, and not practical for repeated screenings. However, a biomarker-based cancer diagnosis is emerging as one of the most promising strategies for early diagnosis, monitoring disease progression, and subsequent cancer treatment. This review describes the recent advances and improvements in the electrochemical biosensors designed for detecting various cancer biomarkers using different signal transduction techniques and biological recognition strategies.
•Electrochemical biosensor helps to early diagnosis of cancer biomarkers.•Different electrochemical transduction system strategies are well explained.•More than 75 recent research articles on biosensors for cancer biomarker detection have been reviewed.•The advantages and future perspectives of each biosensor are highlighted.
An electrochemical sensor based on molecular imprinted polymer (MIP) to detect ceftizoxime (CFX) with high sensitivity and selectivity is demonstrated. MIP was synthesized by electropolymerization of ...poly-cysteine (P-Cys) on a multi-walled carbon nanotube (MWCNT) modified glassy carbon electrode (GCE). A targeted drug was used as a template molecule during the polymerization process. The bare GCE was coated with a layer of MWCNT before the synthesis of MIP to improve the sensor sensitivity. Experimental parameters such as polymerization conditions, the influence of pH, molar ratio of the template molecules and the monomer molecules were all optimized. The peak potential exhibited linearity with CFX concentration in the ranges of 1
×
10
-
9
–1×
10
-
7
molL
−1
(
R
2
= 0.9904) and 2×
10
-
7
–1×
10
-
4
molL
−1
(
R
2
= 0.9949). The LOD of the MIP sensor was found to be
1
×
10
-
10
molL
−1
under optimal conditions using a differential pulse voltammetry (DPV). The proposed sensor was tested on real samples, and good recovery results were obtained.
Here we introduce a composite material that consists of graphene oxide (GO) sheets crosslinked with
-hydroxysuccinimide (NHS) and functionalized with gold nanoflowers (AuNFs). Furthermore, a screen ...printed electrode (SPE) modified with the introduced composite is electrochemically reduced to obtain an SPE/rGO-NHS-AuNFs electrode for sensitive and selective determination of chloramphenicol (CAP) antibiotic drug. The morphological structure of the as-prepared nanocomposite was characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, cyclic voltammetry, Fourier-transform infrared spectroscopy and electrochemical impedance spectroscopy. The proposed sensor demonstrated excellent performance with a linear concentration range of 0.05 to 100 μM and a detection limit of 1 nM. The proposed electrode offers a high level of selectivity, stability, reproducibility and a satisfactory recovery rate for electrochemical detection of CAP in real samples such as blood serum, poultry feed, milk, eggs, honey and powdered milk samples. This further demonstrates the practical feasibility of the proposed sensor in food analysis.
Here we introduce a composite material that consists of graphene oxide (GO) sheets crosslinked with
N
-hydroxysuccinimide (NHS) and functionalized with gold nanoflowers (AuNFs). Furthermore, a screen ...printed electrode (SPE) modified with the introduced composite is electrochemically reduced to obtain an SPE/rGO-NHS-AuNFs electrode for sensitive and selective determination of chloramphenicol (CAP) antibiotic drug. The morphological structure of the as-prepared nanocomposite was characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, cyclic voltammetry, Fourier-transform infrared spectroscopy and electrochemical impedance spectroscopy. The proposed sensor demonstrated excellent performance with a linear concentration range of 0.05 to 100 μM and a detection limit of 1 nM. The proposed electrode offers a high level of selectivity, stability, reproducibility and a satisfactory recovery rate for electrochemical detection of CAP in real samples such as blood serum, poultry feed, milk, eggs, honey and powdered milk samples. This further demonstrates the practical feasibility of the proposed sensor in food analysis.
Here we introduce a composite material that consists of graphene oxide (GO) sheets crosslinked with
N
-hydroxysuccinimide (NHS) and functionalized with gold nanoflowers (AuNFs).
•A novel lightweight architecture is proposed based on a U-shaped architecture.•A novel CNL module is introduced for suppressing false positive and negative outputs.•Res-path is used for better ...semantic compatibility between the encoder and decoder.•The concept of transfer learning is adapted to speed up the convergence procedure.•Experimental results show that the proposed CNL-UNet outperforms existing networks.
Automatic biomedical image segmentation plays an important role in speeding up disease detection and diagnosis. The rapid development of Deep Learning has shown ground-breaking improvements in this context. However, state-of-the-art networks like U-Net and SegNet often have poor performance on challenging domains. Most of the recent works were domains specific and computationally expensive. This paper proposes a novel lightweight architecture named CNL-UNet for 2D multimodal Biomedical Image Segmentation. The proposed CNL-UNet has a pre-trained encoder enriched with transfer learning techniques to learn sufficiently from the small amount of data. It has modified skip connections to reduce semantic gaps between the corresponding level of the encoder-decoder layer. Furthermore, the proposed architecture is enhanced with a novel Classifier and Localizer (CNL) module. This module provides us with additional classification and localization information with greater accuracy. Fusing this information with the segmentation output, the CNL-UNet can suppress false positives and false negatives. The proposed architcture has comparatively fewer parameters (11.5M) than U-Net (31M), SegNet (29M), and most of the recent works. Thus it is a lightweight architecture and also less prone to overfit. Besides, in the case of simple datasets, the pruned version of the CNL-UNet can be used. We evaluated our proposed architecture on multimodal biomedical image datasets, namely Chest X-ray, Dermoscopy, Microscopy, Ultrasound, and MRI images. The results demonstrate the superior performance of our proposed architecture over most of the existing networks. We have shown that our model can learn quickly, segment precisely, and automatically suppress falsely classified outputs.
Analysis of CNL-UNet for Efficient Biomedical Image Segmentation Ahommed, Rifat; Shuvo, Md. Badiuzzaman; Hashem, M. M. A.
2021 5th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT),
2021-Nov.-18
Conference Proceeding
Biomedical image segmentation has a lot of significance in disease detection and diagnosis. Many researchers proposed many deep learning architectures such as U-Net, SegNet, DualChexNet, DualANet for ...biomedical image segmentation. Recently, a novel lightweight architecture namely CNL-UNet is proposed for multimodal biomedical image segmentation. The CNL-UNet used the CNL module, res-path, and transfer learning for effective biomedical image segmentation. In this paper, we made an in-depth analysis of the CNL-UNet to prove its robustness on multimodal biomedical image segmentation. We experimented in detail with the various components of the CNL-UNet. We made eight combinations of the CNL-UNet by removing or adding these components and experimented with these eight combinations on the ultrasound and MRI datasets. The results show that the CNL module helps the model to reduce the false outputs and gain high precision, recall, and F1 score. The res-path has a remarkable contribution to provide precise segmentation and increasing the performance of the model. And also, transfer learning plays an important role in faster convergence and also in increasing the performance of the network. In essence, this experiment increases the confidence of the CNL-UNet regarding its remarkable performance on multimodal biomedical image segmentation.
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