Search

Breast cancer1 (BRCA1) and E2F transcription factor 1 (E2F1) are related tometabolism and cell cycle regulation. However, the corresponding mechanism is not clear in HCC. High BRCA1 direct pathway was constructed with 11 molecules from E2F1 feedbackinteractive network in HCC by GRNInfer based on 39 Pearson mutual positive corelation CC ≥0.25 molecules with E2F1. Integration of GRNInfer with GO, KEGG, BioCarta, GNF_U133A, UNIGENE_EST, Disease, GenMAPP databases by DAVID and MAS 3.0, E2F1 feedback-interactive BRCA1 indirect mitochondrion to cytosol pathway was identified as upstream LAPTM4B activation, feedback UNG, downstream BCAT1-HIST1H2AD-TK1reflecting protein, andDNAbinding with enrichment ofsmall molecule metabolism; The corresponding BRCA1 indirect membrane to cytosol pathway as upstream CCNB2-NUSAP1 activation, feedback TTK-HIST1H2BJ-CENPF, downstream MCM4-TK1 reflecting ATP, and microtubule binding with enrichment ofCD4+T-related cell cycle regulation in HCC. Therefore, we propose that E2F1 interactive with BRCA1 pathway induces HCC two different small molecule metabolism or cell cycle regulation via mitochondrion or CD4+T to cytosol. Knowledge analysis demonstrates our E2F1 feedback-interactive BRCA1 pathway wide disease distribution and reflects a novel common one of tumor and cancer. [ABSTRACT FROM AUTHOR]

Studies were done on the analysis of biological processes in the same high expression (fold change ≥2) PTHLH-activated feedback negative regulation-mediated apoptosis gene ontology (GO) network of human hepatocellular carcinoma (HCC) compared with the corresponding low expression activated GO network of no-tumor hepatitis/cirrhotic tissues [ hepatitis B virus (HBV) or hepatitis C virus (HCV) infection]. We proposed PTHLH-activated network that upstream included the regulation of apoptosis, signal transduction resulting in induction of apoptosis, signal transduction by p53 class mediator resulting in transcription of p21 class mediator, negative regulation of centriole replication, negative regulation of fatty acid biosynthesis, negative regulation of Wnt receptor signaling pathway, anaphase-promoting complex-dependent proteasomal ubiquitin-dependent protein catabolism, apoptosis, induction of apoptosis, and negative regulation of phosphorylation. Downstream-network negative regulation of peptidase activity, anaphase-promoting complex-dependent proteasomal ubiquitin-dependent protein catabolism, apoptosis, induction of apoptosis and negative regulation of phosphorylation, as a result of coupling upstream negative regulation of fatty acid biosynthesis and Wnt receptor signal to downstream peptidase activity-induced apoptosis in HCC. Our hypothesis was verified by the different PTHLH-activated feedback negative regulation-mediated apoptosis GO network of HCC compared with the corresponding inhibited GO network of no-tumor hepatitis/cirrhotic tissues, or the same compared with the corresponding inhibited GO network of HCC. PTHLH coupling upstream negative regulation of fatty acid biosynthesis and Wnt receptor signal to downstream peptidase activity-induced apoptosis network was constructed that upstream BRCA1, DKK1, BUB1B activated PTHLH, and downstream PTHLH-activated CST6, BUB1B, NTN1, PHLDA2 in HCC from GEO data set using gene regulatory network inference method and our programing. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) signals are easily disturbed by internal and external noise, and its morphological characteristics show significant variations for different patients. Even for the same patient, its characteristics are variable under different temporal and physical conditions. Therefore, ECG signal detection and recognition for the heart disease real-time monitoring and diagnosis are still difficult. Based on this, a wavelet self-adaptive threshold denoising combined with deep residual convolutional neural network algorithm was proposed for multiclass arrhythmias recognition. ECG signal filtering was implemented using wavelet adaptive threshold technology. A 20-layer convolutional neural network (CNN) containing multiple residual blocks, namely deep residual convolutional neural network (DR-CNN), was designed for recognition of five types of arrhythmia signals. The DR-CNN constructed by residual block local neural network units alleviated the difficulty of deep network convergence, the difficulty in tuning and so on. It also overcame the degradation problem of the traditional CNN when the network depth was increasing. Furthermore, the batch normalization of each convolution layer improved its convergence. Following the recommendations of the Association for the Advancements of Medical Instrumentation (AAMI), experimental results based on 94 091 2-lead heart beats from the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 99.034 9%, 99.498 0% and 99.334 7% for multiclass classification, ventricular ectopic beat (Veb) and supra-Veb (Sveb) recognition, respectively. Using the same platform and database, experimental results showed that under the comparable network complexity, our proposed method significantly improved the recognition accuracy, sensitivity and specificity compared to the traditional deep learning networks, such as deep Multilayer Perceptron (MLP), CNN, etc. The DR-CNN algorithm improves the accuracy of the arrhythmia intelligent diagnosis. If it is combined with wearable equipment, internet of things and wireless communication technology, the prevention, monitoring and diagnosis of heart disease can be extended to out-of-hospital scenarios, such as families and nursing homes. Therefore, it will improve the cure rate, and effectively save the medical resources.

Studies were done on analysis of biological processes in the same high expression (fold change ≥2) activated PTHLH feedback-mediated cell adhesion gene ontology (GO) network of human hepatocellular carcinoma (HCC) compared with the corresponding low expression activated GO network of no-tumor hepatitis/cirrhotic tissues (HBV or HCV infection). Activated PTHLH feedback-mediated cell adhesion network consisted of anaphase-promoting complex-dependent proteasomal ubiquitin-dependent protein catabolism, cell adhesion, cell differentiation, cell-cell signaling, G-protein-coupled receptor protein signaling pathway, intracellular transport, metabolism, phosphoinositide-mediated signaling, positive regulation of transcription, regulation of cyclin-dependent protein kinase activity, regulation of transcription, signal transduction, transcription, and transport in HCC. We proposed activated PTHLH coupling feedback phosphoinositide to G-protein receptor signal-induced cell adhesion network. Our hypothesis was verified by the different activated PTHLH feedback-mediated cell adhesion GO network of HCC compared with the corresponding inhibited GO network of no-tumor hepatitis/cirrhotic tissues, or the same compared with the corresponding inhibited GO network of HCC. Activated PTHLH coupling feedback phosphoinositide to G-protein receptor signal-induced cell adhesion network included BUB1B, GNG10, PTHR2, GNAZ, RFC4, UBE2C, NRXN3, BAP1, PVRL2, TROAP, and VCAN in HCC from GEO dataset using gene regulatory network inference method and our programming.