Comprehensive analysis reveals signal and molecular mechanism of mitochondrial energy metabolism pathway in pancreatic cancer

Pancreatic cancer (PAAD) is one of the most malignant tumors with the worst prognosis. The abnormalities in the mitochondrial energy metabolism pathway are intimately correlated with the occurrence and progression of cancer. For the diagnosis and treatment of pancreatic cancer, abnormal genes in the mitochondrial energy metabolism system may offer new targets and biomarkers. In this study, we compared the dysregulated mitochondrial energy metabolism-associated pathways in PAAD based on pancreatic cancer samples in the Cancer Genome Atlas (TCGA) database and normal pancreas samples from the Genotype Tissue Expression project (GTEx) database. Then identified 32 core genes of mitochondrial energy metabolism pathway-related genes (MMRG) were based on the gene set enrichment analysis (GSEA). We found most of these genes were altered among different clinical characteristic groups, and showed significant prognostic value and association with immune infiltration, suggesting critical roles of MMRG involve tumor genesis of PAAD. Therefore, we constructed a four-gene (LDHA, ALDH3B1, ALDH3A1, and ADH6) prognostic biomarker after eliminating redundant factors, and confirming its efficiency and independence. Further analysis indicated the potential therapeutic compounds based on the mitochondrial energy metabolism-associated prognostic biomarker. All of the above analyses dissected the critical role of mitochondrial energy metabolism signaling in pancreatic cancer and gave a better understanding of the clinical intervention of PAAD

Segmentation of kidney lesions with attention model based on Deeplab

We participate this challenge by developing a hierarchical framework. We build the model from two fully convolutional networks: (1) a simple Unet model to normalize the input iamges, (2) a segmentaion network which is an attention model based on Deeplab model. Two models are connected in tandem and trained end-to-end. To ensure a better results, we use the preprocess method proposed by nnUnet in our experiments

Probabilistic power flow calculation using principal component analysis-based compressive sensing

The increasing scale of the injection of renewable energy has brought about great uncertainty to the operation of power grid. In this situation, probabilistic power flow (PPF) calculation has been introduced to mitigate the low accuracy of traditional deterministic power flow calculation in describing the operation status and power flow distribution of power systems. Polynomial chaotic expansion (PCE) method has become popular in PPF analysis due to its high efficiency and accuracy, and sparse PCE has increased its capability of tackling the issue of dimension disaster. In this paper, we propose a principal component analysis-based compressive sensing (PCA-CS) algorithm solve the PPF problem. The l1-optimization of CS is used to tackle the dimension disaster of sparse PCE, and PCA is included to further increase the sparsity of expansion coefficient matrix. Theoretical and numerical simulation results show that the proposed method can effectively improve the efficiency of PPF calculation in the case of random inputs with higher dimensions

Over-expression, Rapid Preparation and Some Properties of C-terminal BARc Region in PICK1

A DNA fragment encoding C-terminal BARc region (amino acids 128–416) of rat PICK1 (NP_445912 ) was inserted into a modified vector pMAL-s involving human rhinovirus 3C protease cleavage site to produce a recombinant plasmid, pMAL-s-barc. The construct can express the fusion protein, MBP-BARc in the soluble form in E.coli. To remove the MBP tag, MBP-BARc purified from amylose beads was digested with human rhinovirus 3C protease and the cleavage efficiency is about 95% when the ratio of protein / enzyme (w/w) reaches 50:1, as analyzed on SDS-PAGE. The enzymatic reaction mixture was rapidly separated into two parts, MBP in the supernatant and BARc in the precipitate at the concentration of 1 M ammonium sulfate. In such case, the target protein BARc could be economically produced in a soluble state to be as the sample for measuring its biochemical function, for example, protein-protein interaction and protein-lipid combination

A brain-targeting lipidated peptide for neutralizing RNA-mediated toxicity in Polyglutamine Diseases

Abstract Polyglutamine (PolyQ) diseases are progressive neurodegenerative disorders caused by both protein- and RNA-mediated toxicities. We previously showed that a peptidyl inhibitor, P3, which binds directly to expanded CAG RNA can inhibit RNA-induced nucleolar stress and suppress RNA-induced neurotoxicity. Here we report a N-acetylated and C-amidated derivative of P3, P3V8, that showed a more than 20-fold increase in its affinity for expanded CAG RNA. The P3V8 peptide also more potently alleviated expanded RNA-induced cytotoxicity in vitro, and suppressed polyQ neurodegeneration in Drosophila with no observed toxic effects. Further N-palmitoylation of P3V8 (L1P3V8) not only significantly improved its cellular uptake and stability, but also facilitated its systemic exposure and brain uptake in rats via intranasal administration. Our findings demonstrate that concomitant N-acetylation, C-amidation and palmitoylation of P3 significantly improve both its bioactivity and pharmacological profile. L1P3V8 possesses drug/lead-like properties that can be further developed into a lead inhibitor for the treatment of polyQ diseases