An electrochemical immunosensor employs antibodies as capture and detection means to produce electrical charges for the quantitative analysis of target molecules. This sensor type can be utilized as a miniaturized device for the detection of point-of-care testing (POCT). Achieving high-performance analysis regarding sensitivity has been one of the key issues with developing this type of biosensor system. Many modern nanotechnology efforts allowed for the development of innovative electrochemical biosensors with high sensitivity by employing various nanomaterials that facilitate the electron transfer and carrying capacity of signal tracers in combination with surface modification and bioconjugation techniques. In this review, we introduce novel nanomaterials (e.g., carbon nanotube, graphene, indium tin oxide, nanowire and metallic nanoparticles) in order to construct a high-performance electrode. Also, we describe how to increase the number of signal tracers by employing nanomaterials as carriers and making the polymeric enzyme complex associated with redox cycling for signal amplification. The pros and cons of each method are considered throughout this review. We expect that these reviewed strategies for signal enhancement will be applied to the next versions of lateral-flow paper chromatography and microfluidic immunosensor, which are considered the most practical POCT biosensor platforms.
Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K-means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K-means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.
To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG’s asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG’s asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.
Objective The primary objective of this study was to predict subgroups of autism spectrum disorder (ASD) based on the Diagnostic Statistical Manual for Mental Disorders-IV Text Revision (DSM-IV-TR) by machine learning (ML). The secondary objective was to set up a ranking of Autism Diagnostic Interview-Revised (ADI-R) diagnostic algorithm items based on ML, and to confirm whether ML can sufficiently predict the diagnosis with these minimum items. Methods In the first experiment, a multiclass decision forest algorithm was applied, and the diagnostic algorithm score value of 1,269 Korean ADI-R test data was used for prediction. In the second experiment, we used 539 Korean ADI-R case data (over 48 months with verbal language) to apply mutual information to rank items used in the ADI diagnostic algorithm. Results In the first experiment, the results of predicting in the case of pervasive developmental disorder not otherwise specified as "ASD" were almost three times higher than predicting it as "No diagnosis. " In the second experiment, the top 10 ranking items of ADI-R were mainly related to the quality abnormality of communication. Conclusion In conclusion, we verified the applicability of ML in diagnosis and found that the application of artificial intelligence for rapid diagnosis or screening of ASD patients may be useful.
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