ECG signal classification based on deep CNN and BiLSTM.

BACKGROUND: Currently, cardiovascular disease has become a major disease endangering human health, and the number of such patients is growing. Electrocardiogram (ECG) is an important basis for {medical doctors to diagnose the cardiovascular disease, which can truly reflect the health of the heart. In this context, the contradiction between the lack of medical resources and the surge in the number of patients has become increasingly prominent. The use of computer-aided diagnosis of cardiovascular disease has become particularly important, so the study of ECG automatic classification method has a strong practical significance. METHODS: This article proposes a new method for automatic identification and classification of ECG.We have developed a dense heart rhythm network that combines a 24-layer Deep Convolutional Neural Network (DCNN) and Bidirectional Long Short-Term Memory (BiLSTM) to deeply mine the hierarchical and time-sensitive features of ECG data. Three different sizes of convolution kernels (32, 64 and 128) are used to mine the detailed features of the ECG signal, and the original ECG is filtered using a combination of wavelet transform and median filtering to eliminate the influence of noise on the signal. A new loss function is proposed to control the fluctuation of loss during the training process, and convergence mapping of the tan function in the range of 0-1 is employed to better reflect the model training loss and correct the optimization direction in time. RESULTS: We applied the dataset provided by the 2017 PhysioNet/CINC challenge for verification. The experiment adopted ten-fold cross validation,and obtained an accuracy rate of 89.3[Formula: see text] and an F1 score of 0.891. CONCLUSIONS: This article proposes its own method in the aspects of ECG data preprocessing, feature extraction and loss function design. Compared with the existing methods, this method improves the accuracy of automatic ECG classification and is helpful for clinical diagnosis and self-monitoring of atrial fibrillation.

CtBP1 promotes tumour-associated macrophage infiltration and progression in non-small-cell lung cancer.

The progression of lung cancer is majorly facilitated by TAMs (tumour-associated macrophages). However, how the TAMs infiltrate the NSCLC microenvironment and the associated biochemical are not fully elaborated. Research has revealed that changes in CtBP1 modulates innate immunity. Here, we investigated if CtBP1 facilitates infiltration of TAM and the subsequent progression of NSCLC. Immunohistochemical analysis was carried out in 96 NSCLC patients to estimate the clinicopathological importance of CtBP1 in the disease. CtBP1 overexpression and knockdown were carried out to assess the activity of CtBP1 in NSCLC cells. Elevated expression of CtBP1 correlated positively with TAMs infiltration into NSCLC tissues, induced EMT (epithelial-mesenchymal transition) in NSCLC cells and modulated the activated NF-κB signalling pathway leading to increase in CCL2 secretion from NSCLC cells, thus promoting TAM recruitment and polarization. TAM induction and polarization reduced significantly on exhausting p65 in NSCLC cells with CtBP1. Moreover, infiltration of TMAs was reduced remarkably on antagonist-mediated blocking of CCR2 and impeded the progression of NSCLC in a mouse model. These findings thus show a novel insight into the process of CtBP1-regulated TAM infiltration in NSCLC.

The clinical and prognostic impact of aldehyde dehydrogenase 1 in non-small cell lung cancer: a meta-analysis.

BACKGROUND: Aldehyde dehydrogenase 1 (ALDH1) has been identified as a cancer stem cell marker. However, the clinical role of ALDH1 in non-small cell lung cancer (NSCLC) remains conflicting. This study was conducted to investigate the correlation of ALDH1 with NSCLC patients' clinicopathological characteristics and prognosis. METHODS: The electronic databases were searched for the available literature. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) or hazard ratios (HRs: multivariate Cox analysis) with 95% CIs were used to evaluate the impact of ALDH1 on NSCLC. RESULTS: Final 13 eligible studies with 2,407 patients published between 2009 and 2019 were identified. ALDH1 expression was not correlated with age, gender, smoking behavior, clinical stage, histological grade, lymph node metastasis, and distal metastasis, but the results demonstrated a positive association of ALDH1 expression with recurrence (yes vs. no: OR =2.82, 95% CI, 1.17-6.80, P=0.021) and a negative association of ALDH1 expression with vascular invasion (positive vs. negative: OR =0.63, 95% CI, 0.41-0.98, P=0.04). ALDH1 expression was significantly lower in adenocarcinoma (AD) than in squamous cell carcinoma (SCC) (OR =0.39, 95% CI, 0.30-0.51, P<0.001). Multivariate Cox analysis showed that ALDH1 expression was not associated with overall survival (OS) and disease-free survival (DFS), but was correlated with improved disease-specific survival (DSS) (HR =0.47, 95% CI, 0.22-0.98, P=0.043). CONCLUSIONS: ALDH1 expression may be an independent favorable prognostic marker for DSS in NSCLC.

Silencing NID2 by DNA Hypermethylation Promotes Lung Cancer.

To characterize the DNA methylation as well as exploring the relationship between NID2 methylation and the lung cancer development. Collecting chip data of 9 lung cancer samples and 11 adjacent normal samples from the Gene Expression Omnibus database. Tissues and cells NID2 gene methylation level was measured by methylation-specific PCR. NID2 mRNA level and protein level were validated by Real-Time PCR and Western blot separately. Functional study of lung cancer cells was performed with Cell Counting Kit-8 assay. Colony formation assay, transwell assay, wound healing assay and low cytometry were performed. Finally, NID2 tumorigenesis in vivo was tested in nude mice xenograft models. Microarray analysis outcome present NID2 hypermethylation status in lung cancer tissues. High methylation and low mRNA expression levels of NID2 were detected. After NID2 demethylation or overexpression in cancer cells, cell viability, proliferation, migration as well as invasion ability decreased. Nevertheless, a significant enhancement in apoptosis rate were observed. Overexpressing NID2 or demethylation in lung cancer cells inhibited the tumorigenesis of lung cancer in nude mice. The mRNA and protein level of NID2 in tumors obtained from nude mice xenograft were unanimous with the in vitro assays' outcome, which significantly decreased after overexpressing NID2 or demethylation. NID2 methylation reduces its expression level and promotes the development of lung cancer.