Background: Optimal pharmacological therapy for pulmonary arterial hypertension (PAH) remains unclear, as pathophysiological heterogeneity may affect therapeutic outcomes. A ranking methodology based on pulmonary vasc...
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PurposeTRP channels have been implicated in cancer progression. Our study seeks to establish a prognostic model for hepatocellular carcinoma (HCC) by utilizing genes related to TRP *** used the TCGA and ICGC databases...
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PurposeTRP channels have been implicated in cancer progression. Our study seeks to establish a prognostic model for hepatocellular carcinoma (HCC) by utilizing genes related to TRP *** used the TCGA and ICGC databases as training and validation cohorts, respectively. We calculated the risk scores using Lasso-Cox regression analysis based on the expression levels of prognostic genes and performed survival analysis to compare overall survival between high- and low-risk groups. Then we compared the clinicopathologic characteristics and conducted biological functional analysis. We also explored immune cell infiltration and compared the drug *** bioinformatics algorithms, we identified 11 TRP-related genes and calculated the risk scores. Patients in the high-risk group demonstrated worse overall survival, as well as more advanced T stage and pathologic stage. The risk score showed a significant association with the cell cycle. The high-risk group had more ICI and RTK targets with elevated expression and showed better therapeutic effect to chemotherapy including 5-fluorouracil, camptothecin, docetaxel, doxorubicin, gemcitabine, and paclitaxel. Overall, an individualized nomogram was constructed by integrating the risk score and requisite clinicopathologic parameters to predict the overall survival of HCC *** successfully established a highly accurate prognostic model for predicting overall survival and therapeutic effects using TRP channel-related genes.
We describe an accurate method for the automatic parallel generation of oligonucleotide probe sets for DNA microarrays. This approach includes a component for high-performance specificity evaluation of designed probes...
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We describe an accurate method for the automatic parallel generation of oligonucleotide probe sets for DNA microarrays. This approach includes a component for high-performance specificity evaluation of designed probes in large data sets. The three main algorithmic components of the method, namely probe preselection, hybridization prediction and probe selection are explained in detail. We introduce new combinatorial techniques for the efficient selection of probe sets of high differentiation capability even from sequence databases of conserved homologous genes. These techniques include the automatic generation of group specific probes as well as the design of excluding probes. A basic prototype has been implemented including a shared memory parallelization. Test runs have been performed on a multiprocessor personal computer with subsets of a small subunit ribosomal ribonucleic acid database, containing very conserved sequence data. The applicability of our program is pointed out by designing a set of oligonucleotide probes that shall allow a comprehensive parallel identification and differentiation of several groups of extremophilic prokaryotes by DNA microarray. The probe set is accessible via the Internet. On applying the parallel version on a dual processor system an efficiency of 80% was achieved. Copyright (C) 2004 John Wiley Sons, Ltd.
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