Coronavirus disease 2019 (COVID-19) poses massive challenges for the world. Public sentiment analysis during the outbreak provides insightful information in making appropriate public health responses. On Sina Weiboa, a popular Chinese social media, posts with negative sentiment are valuable in analyzing public concerns. 999,978 randomly selected COVID-19 related Weibo posts from 1 January 2020 to 18 February 2020 are analyzed. Specifically, the unsupervised BERT (Bidirectional Encoder Representations from Transformers) model is adopted to classify sentiment categories (positive, neutral, and negative) and TF-IDF (term frequency-inverse document frequency) model is used to summarize the topics of posts. Trend analysis and thematic analysis are conducted to identify characteristics of negative sentiment. In general, the fine-tuned BERT conducts sentiment classification with considerable accuracy. Besides, topics extracted by TF-IDF precisely convey characteristics of posts regarding COVID-19. As a result, we observed that people concern four aspects regarding COVID-19, the virus Origin (Gamey Food, 3.08%; Bat, 2.70%; Conspiracy Theory, 1.43%), Symptom (Fever, 2.13%; Cough, 1.19%), Production Activity (Go to Work, 1.94%; Resume Work, 1.12%; School New Semester Beginning, 1.06%) and Public Health Control (Temperature Taking, 1.39%; Coronavirus Cover-up, 1.26%; City Shutdown, 1.09%). Results from Weibo posts provide constructive instructions on public health responses, that transparent information sharing and scientific guidance might help alleviate public concerns.

Electrocardiogram (ECG) is an efficient and simple method for the diagnosis of cardiovascular diseases and has been widely used in clinical practice. Because of the shortage of professional cardiologists and the popularity of electrocardiograms, accurate and efficient arrhythmia detection has become a hot research topic. In this paper, we propose a new multi-task deep neural network, which includes a shared low-level feature extraction module (i.e., SE-ResNet) and a task-specific classification module. Contextual Transformer (CoT) block is introduced in the classification module to dynamically model the local and global information of ECG feature sequence. The proposed method was evaluated on public CPSC2018 and PTB-XL datasets and achieved an average F1 score of 0.827 on the CPSC2018 dataset and an average F1 score of 0.833 on the PTB-XL dataset.

The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an automatic classification of ECG signals based on a dilated causal convolutional neural network. To solve the problem that the recurrent neural network framework network cannot be accelerated by hardware equipment, the dilated causal convolutional neural network is adopted. Given the features of the same input and output time steps of the recurrent neural network and the nondisclosure of future information, the network is constructed with fully convolutional networks and causal convolution. To reduce the network depth and prevent gradient explosion or gradient disappearance, the dilated factor is introduced into the model, and the residual blocks are introduced into the model according to the shortcut connection idea. The effectiveness of the algorithm is verified in the MIT-BIH Atrial Fibrillation Database (MIT-BIH AFDB). In the experiment of the MIT-BIH AFDB database, the classification accuracy rate is 98.65.

Nano-titanium nitride (Nano-TiN) has strong resistance to wear and corrosion, good biocompatibility, and an attractive metallic luster. Nano-TiN is widely used in medical devices, such as orthopedic implants, syringe needles, coronary stents, and long-term dental implants, and also in imitation gold jewelry. Despite its widespread use, there are few reports describing safety evaluations of Nano-TiN. Here, we exposed healthy zebrafish embryos to different concentrations of Nano-TiN solution for five days, starting at about four hours post fertilization, and found that Nano-TiN caused dose- and time-dependent developmental toxicity. With increasing Nano-TiN concentration and length of exposure, mortality, and deformities gradually increased; body length shortened and hatching rate and motility were significantly reduced. We also found that exposure to Nano-TiN affected development of the heart, liver, nerves, and other organs, and led to elevated levels of reactive oxygen species and reduced antioxidant capacity. Exposure to Nano-TiN resulted in downregulation of expression of antioxidant genes, such as nrf2, gclc, gclm, ho-1, and nqo1. Our results showed that exposure to Nano-TiN caused developmental and organ toxicity in zebrafish embryos and that the toxic effects may be mediated through oxidative stress.

Atrial fibrillation (AF) is one of the most common cardiovascular diseases, with a high disability rate and mortality rate. The early detection and treatment of atrial fibrillation have great clinical significance. In this paper, a multiple feature fusion is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The proposed method uses discriminant canonical correlation analysis (DCCA) feature fusion. It fully takes intraclass correlation and interclass correlation into consideration and solves the problem of computation and information redundancy with simple series or parallel feature fusion. The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. Based on the Cardiology Challenge 2017 dataset, the experiments are designed to verify the effectiveness of the proposed algorithm. In the experiments, the F1 index can reach 88%. The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.