With the highest cancer incidence rate, lung cancer, especially non-small cell lung cancer (NSCLC), is the leading cause of cancer death in the world. Tea (leaves of Camellia sinensis) has been widely used as a traditional beverage beneficial to human health, including anti-NSCLC activity. Theabrownin (TB) is one major kind of tea pigment responsible for the beneficial effects of tea liquor. However, its effect on NSCLC is unknown. The aim of the present study was to evaluate anti-proliferative and apoptosis-inducing effect of TB on NSCLC (A549) cells, using MTT assay, morphological observation (DAPI staining), in situ terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, and annexin V/PI flow cytometry. Subsequently, the expression of several genes associated with cell proliferation and apoptosis were detected by real time PCR assay to explore its potential underlying mechanism. TB was revealed to inhibit cell proliferation of A549 cells in a concentration-dependent and time-dependent manner. Morphological observation, TUNEL assay and flow cytometric analysis evidenced an apoptosis-inducing effect of TB on A549 cells in a concentration-dependent manner. The real time PCR assay demonstrated that TB down-regulated the expression of TOPO I, TOPO II, and BCL-2, and up-regulated the expression of E2F1, P53, GADD45, BAX, BIM, and CASP 3,7,8,9, which suggests an activation of P53-mediated apoptotic (caspase-dependent) pathway in response to TB treatment. The western blot analysis showed a similar trend for the corresponding protein expression (P53, Bax, Bcl-2, caspase 3,9, and PARP) and further revealed DNA damage as a trigger of the apoptosis (phosphorylation of histone H2A.X). Accordingly, TB can be speculated as a DNA damage inducer and topoisomerase (Topo I and Topo II) inhibitor that can up-regulate P53 expression and subsequently modulate the expression of the downstream genes to induce cell proliferation inhibition and apoptosis of A549 cells. Our results indicate that TB exhibits its anti-NSCLC activity via a P53-dependent mechanism, which may be a promising candidate of natural product for anti-cancer drug development in the treatment of NSCLC.

Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients’ quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively.

Publishing biological data in XML formats is attractive for organizations who would like to provide their bioinformatics resources in an extensible and machine-readable format. In the era of big data, massive XML-based biological data management is emerged as a challengeable issue. With the continuous growth of the XML-based biological data sets, it is usually frustrating to use traditional declarative query languages to provide efficient query capabilities in terms of processing speed and scale. In this study, we report a novel platform to store and query massive XML-based biological data collections. A prototype tool for constructing HBase tables from XML-based biological data collections is firstly developed, and then a formal approach to transform the XML query model into the MapReduce query model is proposed. Finally, an evaluation of the query performance of the proposed approach on the existing XML-based biological databases is presented, showing that the performance advantages of the proposed solution. The source code of the massive XML-based biological data management platform is freely available at https://github.com/lyotvincent/X2H.

Pancreatic cancer is a highly malignant and metastatic tumor of the digestive system. Even after surgical removal of the tumor, most patients are still at risk of metastasis. Therefore, screening for metastatic biomarkers can identify precise therapeutic intervention targets. In this study, we analyzed 96 pancreatic cancer samples from The Cancer Genome Atlas (TCGA) without metastasis or with metastasis after R0 resection. We also retrieved data from metastatic pancreatic cancer cell lines from Gene Expression Omnibus (GEO), as well as collected sequencing data from our own cell lines, BxPC-3 and BxPC-3-M8. Finally, we analyzed the expression of metastasis-related genes in different datasets by the Limma and edgeR packages in R software, and enrichment analysis of differential gene expression was used to gain insight into the mechanism of pancreatic cancer metastasis. Our analysis identified six genes as risk factors for predicting metastatic status by LASSO regression, including zinc finger BED-Type Containing 2 (ZBED2), S100 calcium-binding protein A2 (S100A2), Jagged canonical Notch ligand 1 (JAG1), laminin subunit gamma 2 (LAMC2), transglutaminase 2 (TGM2), and the transcription factor hepatic leukemia factor (HLF). We used these six EMT-related genes to construct a risk-scoring model. The receiver operating characteristic (ROC) curve showed that the risk score could better predict the risk of metastasis. Univariate and multivariate Cox regression analyses revealed that the risk score was also an important predictor of pancreatic cancer. In conclusion, 6-mRNA expression is a potentially valuable method for predicting pancreatic cancer metastasis, assessing clinical outcomes, and facilitating future personalized treatment for patients with ductal adenocarcinoma of the pancreas (PDAC).

Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets to augment Chinese sentences. Nevertheless, the traditional text data augmentation ignores the semantics between words in sentences, besides, it has limitations in alleviating the problem of the diversity of augmented sentences. In this paper, a novel medical data augmentation (MDA) is proposed for NLP tasks, which combines the medical knowledge graph with text data augmentation to generate augmented data. Experiments on the named entity recognition task and relational classification task demonstrate that the MDA can significantly enhance the efficiency of the deep learning models compared to cases without augmentation.