Astaxanthin protects the radiation-induced lung injury in C57BL/6 female mice.

Radiation-induced lung injury (RILI) is one of the common complications of radiotherapy for chest tumors and nuclear radiation accidents. The excessive reactive oxygen species induced by radiation is the main mediator. So far, the effective prevention and treatment for RILI are still lacking. Astaxanthin is a carotenoid that belongs to red natural lutein family and is commonly found in Marine organisms such as shrimp, oysters and salmon. It has been confirmed that astaxanthin has strong antioxidant and anti-inflammatory properties, therefore we speculated that astaxanthin may be a potential treatment for RILI. First, with a mice model of RILI, the protected effects of astaxanthin were observed. Furthermore, the experiments in vitro were performed by detecting apoptosis. As a result, astaxanthin protects the RILI, inhibits the process of pulmonary fibrosis, and reduces the elevation of inflammatory factors. The experiments in vitro demonstrated that astaxanthin could reduce radiation-induced apoptosis and especially inhibit activation of apoptosis pathway. In conclusion, astaxanthin could protect RILI of mice, which is mediated by inhibiting activation of apoptosis pathway.

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019. This dataset covers most types of ECG interpretations, including the normal type, 8 common abnormal types, and the other type which includes both uncommon abnormal and noise signals. Based on the Contest, we systematically assessed and analyzed a set of top-performing methods, most of which are deep neural networks, with both their commonalities and characteristics. This study establishes the benchmarks for computerized interpretation of 12-lead resting electrocardiogram and provides insights for the development of new methods. Graphical Abstract A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Improving Automatic Detection of ECG Abnormality with Less Manual Annotations using Siamese Network.

Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms. Recently many works also focused on the design of automatic ECG abnormality detection algorithms. However, clinical electrocardiogram datasets often suffer from their heavy needs for expert annotations, which are often expensive and hard to obtain. In this work, we proposed a weakly supervised pretraining method based on the Siamese neural network, which utilizes the original diagnostic information written by physicians to produce useful feature representations of the ECG signal which improves performance of ECG abnormality detection algorithms with fewer expert annotations. The experiment showed that with the proposed weekly supervised pretraining, the performance of ECG abnormality detection algorithms that was trained with only 1/8 annotated ECG data outperforms classical models that was trained with fully annotated ECG data, which implies a large proportion of annotation resource could be saved. The proposed technique could be easily extended to other tasks beside abnormality detection provided that the text similarity metric is specifically designed for the given task.Clinical Relevance-This work proposes a novel framework for the automatic detection of cardiovascular disease based on electrocardiogram.

Segment Origin Prediction: A Self-supervised Learning Method for Electrocardiogram Arrhythmia Classification.

The automatic arrhythmia classification system has made a significant contribution to reducing the mortality rate of cardiovascular diseases. Although the current deep-learning-based models have achieved ideal effects in arrhythmia classification, their performance still needs to be further improved due to the small scale of the dataset. In this paper, we propose a novel self-supervised pre-training method called Segment Origin Prediction (SOP) to improve the model's arrhythmia classification performance. We design a data reorganization module, which allows the model to learn ECG features by predicting whether two segments are from the same original signal without using annotations. Further, by adding a feed-forward layer to the pre-training stage, the model can achieve better performance when using labeled data for arrhythmia classification in the downstream stage. We apply the proposed SOP method to six representative models and evaluate the performances on the PhysioNet Challenge 2017 dataset. After using the SOP pre-training method, all baseline models gain significant improvement. The experimental results verify the effectiveness of the proposed SOP method.

Unsupervised Domain Adaptation for ECG Arrhythmia Classification.

Electrocardiograph (ECG) is one of the most critical physiological signals for arrhythmia diagnosis in clinical practice. In recent years, various algorithms based on deep learning have been proposed to solve the heartbeat classification problem and achieved saturated accuracy in intrapatient paradigm, but encountered performance degradation in inter-patient paradigm due to the drastic variation of ECG signals among different individuals. In this paper, we propose a novel unsupervised domain adaptation scheme to address this problem. Specifically, we first propose a robust baseline model called Multi-path Atrous Convolutional Network (MACN) to tackle ECG heartbeat classification. Further, we introduce Cluster-aligning loss and Cluster-separating loss to align the distributions of training and test data and increase the discriminability, respectively. The proposed method requires no expert annotations but a short period of unlabelled data in new records. Experimental results on the MIT-BIH database demonstrate that our scheme effectively intensifies the baseline model and achieves competitive performance with other state-of-the-arts.

Statistical analysis of the patterns and characteristics of connected and autonomous vehicle involved crashes.

INTRODUCTION: This study aimed to investigate the characteristics and patterns of the connected and autonomous vehicle (CAV) involved crashes. METHOD: The crash data were collected from the reports of CAV involved crash submitted to the California Department of Motor Vehicles. The descriptive statistics analysis was employed to investigate the characteristics of CAV involved crashes in terms of crash location, weather conditions, driving mode, vehicle movement before crash occurrence, vehicle speed, collision type, crash severity, and vehicle damage locations. The bootstrap based binary logistic regressions were then developed to investigate the factors contributing to the collision type and severity of CAV involved crashes. RESULTS: The results suggested that the CAV driving mode, collision location, roadside parking, rear-end collision, and one-way road are the main factors contributing to the severity level of CAV involved crashes. The CAV driving mode, CAV stopped or not, CAV turning or not, normal vehicle turning or not, and normal vehicle overtaking or not are the factors affecting the collision type of CAV involved crashes.

Genome-wide DNA methylation analysis identifies candidate epigenetic markers and drivers of hepatocellular carcinoma.

The alteration of DNA methylation landscape is a key epigenetic event in cancer. As the accumulation of large-scale genome-wide DNA methylation data from clinical samples, we are able to characterize the patterns of DNA methylation alterations for identifying candidate epigenetic markers and drivers. In this survey, we take hepatocellular carcinoma (HCC) as an example to show the basic steps of analyzing the DNA methylation patterns in cancer across multiple data sets. We collected three genome-wide DNA methylation data sets with ∼800 clinical samples and the corresponding gene expression data sets. First, by quantitatively analyzing two global methylation alterations, it is found that about 90% tumors acquire either genome-wide DNA hypo-methylation or CpG island methylator phenotype. Second, probe-level analysis identified 267, 228 and 197 hyper-methylated sites in promoter regions for the three data sets, respectively. These local hyper-methylated patterns are highly consistent: 84 sites (from 61 promoters) are hyper-methylated in all the three studied data sets, including many previously reported genes, such as CDKL2, TBX15 and NKX6-2. Then, these hyper-methylated sites were used as candidate markers to classify tumor and non-tumor samples. The classifiers based on only 10 selected probes can achieve high discriminative ability across different data sets. Finally, by integrative analyzing DNA methylation and gene expression data, we identified 222 candidate epigenetic drivers, which are enriched in inflammatory response and multiple metabolic pathways. A set of high-confidence candidates, including SFN, SPP1 and TKT, are significantly associated with patients' overall survivals. In summary, this study systematically characterized the DNA methylation alterations and their impacts on gene expressions in HCCs based on multiple data sets.

Evaluating the molecule-based prediction of clinical drug responses in cancer.

MOTIVATION: Molecule-based prediction of drug response is one major task of precision oncology. Recently, large-scale cancer genomic studies, such as The Cancer Genome Atlas (TCGA), provide the opportunity to evaluate the predictive utility of molecular data for clinical drug responses in multiple cancer types. RESULTS: Here, we first curated the drug treatment information from TCGA. Four chemotherapeutic drugs had more than 180 clinical response records. Then, we developed a computational framework to evaluate the molecule based predictions of clinical responses of the four drugs and to identify the corresponding molecular signatures. Results show that mRNA or miRNA expressions can predict drug responses significantly better than random classifiers in specific cancer types. A few signature genes are involved in drug response related pathways, such as DDB1 in DNA repair pathway and DLL4 in Notch signaling pathway. Finally, we applied the framework to predict responses across multiple cancer types and found that the prediction performances get improved for cisplatin based on miRNA expressions. Integrative analysis of clinical drug response data and molecular data offers opportunities for discovering predictive markers in cancer. This study provides a starting point to objectively evaluate the molecule-based predictions of clinical drug responses. CONTACT: jgu@tsinghua.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs.

SUMMARY: MicroRNAs (miRNAs), a class of small regulatory RNAs, play important roles in cancer initiation, progression and therapy. MiRNAs are found to regulate diverse cancer-related processes by targeting a large set of oncogenic and tumor-suppressive genes. To establish a high-confidence reference resource for studying the miRNA-regulated target genes and cellular processes in cancer, we manually curated 2259 entries of cancer-related miRNA regulations with direct experimental evidence from ∼9000 abstracts, covering more than 300 miRNAs and 829 target genes across 25 cancer tissues. A web-based portal named oncomiRDB, which provides both graphical and text-based interfaces, was developed for easily browsing and searching all the annotations. It should be a useful resource for both the computational analysis and experimental study on miRNA regulatory networks and functions in cancer. AVAILABILITY AND IMPLEMENTATION: http://bioinfo.au.tsinghua.edu.cn/oncomirdb/ CONTACT: jgu@tsinghua.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.