Morrison Dillard (yardsoil5)

Although immunotherapy is a potential strategy to resist cancers, due to the inadequate acknowledge, this treatment is not always effective for diffuse large B cell lymphoma (DLBCL) patients. Based on the current situation, it is critical to systematically investigate the immune pattern. According to the result of univariate and multivariate cox proportional hazards, LASSO regression and Kaplan-Meier survival analysis on immune-related genes (IRGs), a prognostic signature, containing 14 IRGs (AQP9, LMBR1L, FGF20, TANK, CRP, ORM1, JAK1, BACH2, MTCP1, IFITM1, TNFSF10, FGF12, RFX5, and LAP3), was built. This model was validated by external data, and performed well. DLBCL patients were divided into low- and high-risk groups, according to risk scores from risk formula. check details The results of CIBERSORT showed that different immune status and infiltration pattern were observed in these two groups. Gene set enrichment analysis (GSEA) indicated 12 signaling pathways were significantly enriched in the high-risk group, such as natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathway, and so on. In summary, 14 clinically significant IRGs were screened to build a risk score formula. This formula was an accurate tool to provide a certain basis for the treatment of DLBCL patients.A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the liver cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to identify stromal-immune score-based potential prognostic biomarkers for hepatocellular carcinoma. Stromal and immune scores were estimated from transcriptomic profiles of a liver cancer cohort from The Cancer Genome Atlas using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select prognostic genes. Favorable overall survivals and progression-free interval were found in patients with high stromal score and immune score, and 828 differentially expressed genes were identified. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. MMP9 (matrix metallopeptidase 9) was identified as a prognostic tumor microenvironment-associated gene by using LASSO and TIMER (Tumor IMmune Estimation Resource) algorithms and was found to be positively correlated with immunosuppressive molecules and drug response.Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development. In this task, some relational facts can only be inferred from the document rather than single sentence. Recently, researchers investigate graph-based approaches to extract relations across sentences. It iteratively combines the information from neighbor nodes to model the interactions in entity mentions that exist in different sentences. Despite their success, one severe li