Electrochemical Signal Substance for Multiplexed Immunosensing Interface Construction: A Mini Review

Appropriate labeling method of signal substance is necessary for the construction of multiplexed electrochemical immunosensing interface to enhance the specificity for the diagnosis of cancer. So far, various electrochemical substances, including organic molecules, metal ions, metal nanoparticles, Prussian blue, and other methods for an electrochemical signal generation have been successfully applied in multiplexed biosensor designing. However, few works have been reported on the summary of electrochemical signal substance applied in constructing multiplexed immunosensing interface. Herein, according to the classification of labeled electrochemical signal substance, this review has summarized the recent state-of-art development for the designing of electrochemical immunosensing interface for simultaneous detection of multiple tumor markers. After that, the conclusion and prospects for future applications of electrochemical signal substances in multiplexed immunosensors are also discussed. The current review can provide a comprehensive summary of signal substance selection for workers researched in electrochemical sensors, and further, make contributions for the designing of multiplexed electrochemical immunosensing interface with well signal.

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Using class-based feature selection for the classification of hyperspectral data

International Journal of Remote Sensing ◽

10.1080/01431161.2010.486416 ◽

2011 ◽

Vol 32 (15) ◽

pp. 4311-4326 ◽

Cited By ~ 19

Author(s):

Yasser Maghsoudi ◽

Mohammad Javad Valadan Zoej ◽

Michael Collins

Keyword(s):

Feature Selection ◽

Hyperspectral Data ◽

Selection For

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MnO2@Reduced Graphene Oxide Nanocomposite-Based Electrochemical Sensor for the Simultaneous Determination of Trace Cd(II), Zn(II) and Cu(II) in Water Samples

Membranes ◽

10.3390/membranes11070517 ◽

2021 ◽

Vol 11 (7) ◽

pp. 517

Author(s):

Siyamthanda Hope Mnyipika ◽

Tshimangadzo Saddam Munonde ◽

Philiswa Nosizo Nomngongo

Keyword(s):

Heavy Metals ◽

Electrochemical Sensor ◽

Simultaneous Determination ◽

Rapid Detection ◽

Electrochemical Sensors ◽

Simultaneous Detection ◽

Cyclic Voltammograms ◽

Current Response ◽

Mno2 Nanoparticles

The rapid detection of trace metals is one of the most important aspect in achieving environmental monitoring and protection. Electrochemical sensors remain a key solution for rapid detection of heavy metals in environmental water matrices. This paper reports the fabrication of an electrochemical sensor obtained by the simultaneous electrodeposition of MnO2 nanoparticles and RGO nanosheets on the surface of a glassy carbon electrode. The successful electrodeposition was confirmed by the enhanced current response on the cyclic voltammograms. The XRD, HR-SEM/EDX, TEM, FTIR, and BET characterization confirmed the successful synthesis of MnO2 nanoparticles, RGO nanosheets, and MnO2@RGO nanocomposite. The electrochemical studies results revealed that MnO2@RGO@GCE nanocomposite considerably improved the current response on the detection of Zn(II), Cd(II) and Cu(II) ions in surface water. These remarkable improvements were due to the interaction between MnO2 nanomaterials and RGO nanosheets. Moreover, the modified sensor electrode portrayed high sensitivity, reproducibility, and stability on the simultaneous determination of Zn(II), Cd(II), and Cu(II) ions. The detection limits of (S/N = 3) ranged from 0.002–0.015 μg L−1 for the simultaneous detection of Zn(II), Cd(II), and Cu(II) ions. The results show that MnO2@RGO nanocomposite can be successfully used for the early detection of heavy metals with higher sensitivity in water sample analysis.

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Highly sensitive electrochemical immunosensor for the simultaneous detection of multiple tumor markers for signal amplification

Talanta ◽

10.1016/j.talanta.2021.122133 ◽

2021 ◽

Vol 226 ◽

pp. 122133

Author(s):

Na Wang ◽

Jing Wang ◽

Xiaoqing Zhao ◽

Hou Chen ◽

Hui Xu ◽

...

Keyword(s):

Tumor Markers ◽

Signal Amplification ◽

Simultaneous Detection ◽

Electrochemical Immunosensor ◽

Multiple Tumor ◽

Highly Sensitive

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MultiKOC: Multi-One-Class Classifier Based K-Means Clustering

Algorithms ◽

10.3390/a14050134 ◽

2021 ◽

Vol 14 (5) ◽

pp. 134

Author(s):

Loai Abdallah ◽

Murad Badarna ◽

Waleed Khalifa ◽

Malik Yousef

Keyword(s):

Clustering Algorithm ◽

Main Idea ◽

Molecular Classification ◽

Positive Sample ◽

Classification Problems ◽

Multiple Cancer ◽

Multiple Tumor ◽

One Class Classifier ◽

The Given

In the computational biology community there are many biological cases that are considered as multi-one-class classification problems. Examples include the classification of multiple tumor types, protein fold recognition and the molecular classification of multiple cancer types. In all of these cases the real world appropriately characterized negative cases or outliers are impractical to achieve and the positive cases might consist of different clusters, which in turn might lead to accuracy degradation. In this paper we present a novel algorithm named MultiKOC multi-one-class classifiers based K-means to deal with this problem. The main idea is to execute a clustering algorithm over the positive samples to capture the hidden subdata of the given positive data, and then building up a one-class classifier for every cluster member’s examples separately: in other word, train the OC classifier on each piece of subdata. For a given new sample, the generated classifiers are applied. If it is rejected by all of those classifiers, the given sample is considered as a negative sample, otherwise it is a positive sample. The results of MultiKOC are compared with the traditional one-class, multi-one-class, ensemble one-classes and two-class methods, yielding a significant improvement over the one-class and like the two-class performance.

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An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Complex & Intelligent Systems ◽

10.1007/s40747-021-00310-3 ◽

2021 ◽

Author(s):

Muhammad Irfan Sharif ◽

Jian Ping Li ◽

Javeria Amin ◽

Abida Sharif

Keyword(s):

Brain Tumor ◽

Tumor Detection ◽

Entropy Method ◽

Feature Extraction And Selection ◽

Tumor Region ◽

Selection For ◽

Magnetic Resonance Imaging Mri ◽

The Brain ◽

Complicated Task

AbstractBrain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

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