Establishment and characterization of a highly metastatic hepatocellular carcinoma cell line.

The prevalence of alcohol-related hepatocellular carcinoma (HCC) has been increasing during the last decade. Cancer research requires cell lines suitable for both in vitro and in vivo assays. However, there is a lack of cell lines with a high in vivo metastatic capacity for this HCC subtype. Herein, a new HCC cell line was established, named HCC-ZJ, using cells from a patient diagnosed with alcohol-related HCC. The karyotype of HCC-ZJ was 46, XY, del (p11.2). Whole-exome sequencing identified several genetic variations in HCC-Z that occur frequently in alcohol-associated HCC, such as mutations in TERT, CTNNB1, ARID1A, CDKN2A, SMARCA2, and HGF. Cell counting kit-8 assays, colony formation assays, and Transwell assays were performed to evaluate the proliferation, migration, and sensitivity to sorafenib and lenvatinib of HCC-Z in vitro. HCC-ZJ showed a robust proliferation rate, a weak foci-forming ability, a strong migration capacity, and a moderate invasion tendency in vitro. Finally, the tumorigenicity and metastatic capacity of HCC-Z were evaluated using a subcutaneous xenograft model, an orthotopic xenograft model, and a tail-veil injection model. HCCZJ exhibited strong tumorigenicity in the subcutaneous xenograft and orthotopic tumor models. Moreover, HCC-ZJ spontaneously formed pulmonary metastases in the orthotopic tumor model. In summary, a new HCC cell line derived from a patient with alcohol-related HCC was established, which showed a high metastatic capacity and could be applied for in vitro and in vivo experiments during pre-clinical research.Highlights• An alcohol-related HCC cell line, HCC-ZJ, was established• HCC-ZJ was applicable for in vitro functional experiment and gene editing• HCC-ZJ was applicable for in vivo tumor growth and spontaneous metastasis models.

Arrhythmia Disease Diagnosis Based on ECG Time-Frequency Domain Fusion and Convolutional Neural Network.

Electrocardiogram (ECG) signals are often used to diagnose cardiac status. However, most of the existing ECG diagnostic methods only use the time-domain information, resulting in some obviously lesion information in frequency-domain of ECG signals are not being fully utilized. Therefore, we propose a method to fuse the time and frequency domain information in ECG signals by convolutional neural network (CNN). First, we adapt multi-scale wavelet decomposition to filter the ECG signal; Then, R-wave localization is used to segment each individual heartbeat cycle; And then, the frequency domain information of this heartbeat cycle is extracted via fast Fourier transform. Finally, the temporal information is spliced with the frequency domain information and input to the neural network for classification. The experimental results show that the proposed method has the highest recognition accuracy (99.43%) of ECG singles compared with state-of-the-art methods. Clinical and Translational Impact Statement- The proposed ECG classification method provides an effective solution for ECG interrogation to quickly diagnose the presence of arrhythmia in a patient from the ECG signal. It can increase the efficiency of the interrogating physician by aiding diagnosis.