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Metal-Ions Intercalation Mechanism within Padded Anode Through First-Principles Calculations.
The granule mass was evaluated for the bulk density, tapped density, inter-particle porosity, Carr's index, Hausner ratio, angle of repose, and flowability. RESULTS The results showed that all of the formulae passed the requirement of the preformulation test. The bulk density of the granule mass was 0.79 - 0.86 g/ml; the tapped density was 0.88 - 0.90 g/ml; the inter-particle porosity was 0.03 - 0.14; the Carr's index was 2.71 - 11.94%; the Hausner ratio was 1.09-1.12; the angle of repose was 26.10 - 28.90°; and the flowing time was 5.97 - 6.63 seconds. All of the formulae showed good flowability and free-flowing properties. CONCLUSION It is concluded that the obtained formula containing pugun tano ethanolic extract can be formulated into granule mass for the capsule dosage form. Copyright © 2019 Urip Harahap, Marianne Marianne, Yuandani Yuandani, Lia Laila.Circular RNAs (circRNAs) are involved in cancer development via inhibition of miRNAs, which are associated with differentiation, proliferation, migration, and carcinogenicity. Curcumin has antioxidant and anti-cancer properties, and it has also been used as a radiosensitizer. In this study, we explored the potential relationships among curcumin, circRNAs, and nasopharyngeal carcinoma (NPC). selleck chemical We compared the differences in circRNA levels in NPC cell lines after radiotherapy and after treatment with curcumin, using a high-throughput microarray. Further, a circRNA-miRNA-mRNA interaction network between radiation resistance NPC cell lines and tumor stem cells was constructed by applying bioinformatics. Finally, it was demonstrated by reverse transcription-quantitative polymerase chain reaction assay and wound healing assay that curcumin could enhance radiosensitization of NPC cell lines via mediating regulation of tumor stem-like cells by the "hsa_circRNA_102115"-"hsa-miR-335-3p"-"MAPK1" interaction network. © The author(s).Colorectal cancer (CRC) is one of the most common carcinomas and the fourth leading cause of cancer-related death worldwide. One of the obstacles in the successful treatment of CRC is a high rate of recurrence. We aimed to construct weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes in association with recurrence in CRC patients. We firstly used the microarray data, GSE41258, to construct a co-expression network and identify gene modules. Furthermore, protein and protein interaction (PPI) network was also performed to screen hub genes. To validate the hub genes, an independent dataset GSE17536 was used for survival analyses. Additionally, another two databases were also performed to investigate the survival rates and expression levels of hub genes. Gene set enrichment analyses (GSEA) combined with gene ontology (GO) were performed to further explore function and mechanisms. In our study, the midnightblue module was identified to be significant, 15 hub genes were screened, four of which were identified as hub nodes in the PPI network. In the test dataset, we found higher expression of MYL9 and CNN1 were significantly associated with shorter survival time of CRC patients. GO analyses showed that MYL9 and CNN1 were enriched in "muscle system process" and "cytoskeletal protein binding". GSEA found the two hub genes were enriched in "pathways in cancer" and "calcium signaling pathway". In conclusion, our study demonstrated that MYL9 and CNN1 were hub genes associated with the recurrence of CRC, which may contribute to the improvement of recurrence-free survival time of CRC patients. © The author(s).Objective This study aimed to evaluate the therapeutic response of hepatocellular carcinoma (HCC) after transcatheter arterial chemoembolization (TACE) with diffusion kurtosis imaging (DKI). Methods Forty-three patients with fifty-nine hepatic cancer nodules were recruited for this study. All patients were treated by TACE. Magnetic resonance imaging (MRI) and DKI (b=0, 800, 1,500, 2,000mm2/s) were performed before and one month after initiating TACE. Patients were classified as either progressing groups or non-progressing groups. Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values of the tumor tissue were analyzed. Results Twenty-three HCCs were classified as progressing groups, and thirty-six HCCs were non-progressing groups. After TACE, the values of MD and ADC in non-progressing groups (1.92±0.36×10-3mm2/s, 1.36±0.23×10-3mm2/s) were greater than progressing groups (1.44±0.32× 10-3mm2/s, 1.10±0.23×10-3mm2/s), however, the MK values in non-progressing groups (0.47±0.12) were lower than progressing groups (0.72±0.14). The MK values of tumor among non-progressing patients decreased one month after TACE (0.47±0.12) relative to the preoperative values (0.71±0.12) (P0.05). The sensitivity, specificity, and AUC of the ROC curve for the assessment of HCC progress after TACE by MK (85.2%, 97.5%, and 0.95, respectively) were greater than by ADC (78.6%, 66.5%, and 0.75, respectively) and MD (76.2%, 64.3%, and 0.71, respectively). Conclusions DKI for assessing the therapeutic response of TACE in HCC shows great promise. MK is more advantageous in the assessment of HCC progress after TACE. © The author(s).Purpose Radiation pneumonitis (RP) is the most significant dose-limiting toxicity and is one major obstacle for lung cancer radiotherapy. Grade ≥2 RP usually needs clinical interventions and serve RP could be life threatening. Clinically, tissue response could be strikingly different even two similar patients after identical radiotherapy. Previous methods for the RP prediction can hardly distinguish substantial variations among individuals. Reliable predictive factors or methods emphasizing the individual differences are strongly desired by clinical radiation oncologists. The purpose of this study is to develop an approach for the personalized RP risk prediction. Experimental Design One hundred eighteen lung cancer patients who received radiotherapy were enrolled. Seven hundred thousand single-nucleotide polymorphism (SNP) sites were assessed via Generalized Linear Models via Lasso and Elastic-Net Regularization (GLMNET) to determine their synergistic effects on the RP risk prediction. Non-genetic factors including patient's phenotypes and clinical interventional parameters were separately assessed by statistic test.
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