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Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into twodimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices. [ABSTRACT FROM AUTHOR]

Reasonable exercise is beneficial to human health. However, it is difficult for ordinary athletes to judge whether they are already in a state of fatigue that is not suitable for exercise. In this case, it is easy to cause physical damage or even life-threatening. Therefore, to health sports, protecting the human body in sports not be injured by unreasonable sports, this study proposes an exercise fatigue diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN). The method analyzes and diagnoses the real-time electrocardiogram, and obtains whether the current exerciser has exercise fatigue according to the electrocardiogram. The algorithm first performs short-time Fourier transform on the electrocardiogram (ECG) signal to obtain the time spectrum of the signal, which is divided into training set and validation set. The training set is then fed into the convolutional neural network for learning, and the network parameters are adjusted. Finally, the trained convolutional neural network model is applied to the test set, and the recognition result of fatigue level is output. The validity and feasibility of the method are verified by the ECG experiment of exercise fatigue degree. The experimental recognition accuracy rate can reach 97.70%, which proves that the constructed sports fatigue diagnosis model has high diagnostic accuracy and is feasible for practical application. [ABSTRACT FROM AUTHOR]

Objective: Antibodies to CA 125 have been used to predict relapse of ovarian cancer, but have performed poorly as therapeutic agents. One rationale for this is antibody binding to circulating shed antigen. Our aim in this study was to develop antibodies to human CA 125 that have enhanced selectivity for the cell-associated form of the antigen. Methods: Monoclonal antibodies were raised to a recombinant fragment of CA 125 that included sequence proximal to the putative membrane attachment site. Antibodies were characterized in terms of their binding site, affinity and selectivity for cell-associated CA 125. Results: In assays using patient-derived CA 125, a subset of high-affinity (KD <5 nM) monoclonal antibodies demonstrated a 10- to greater than 200-fold increase in selectivity for cell-associated CA 125 when compared with controls. Based on mapping of the various monoclonal antibodies obtained, it was determined that shedding of CA 125 most likely occurs in the most C-terminal repeat domain. Conclusion: Results from competition analysis using patient-derived shed antigen predict that the antibodies described in this study may have significantly enhanced tumor-targeting properties when compared with existing antibodies to CA 125 in a tumor environment having high concentrations (>10,000 CA 125 units) of shed CA 125. Copyright © 2006 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Examines the retroviral-mediated transmission of a mouse VL30 RNA to human melanoma cells. Promotion of metastasis in an immunodeficient mouse model; Structural features of retroviral genome; Transmission of cellular gene from other cells.