Shea Kornum (cupformat5)

Thus, TNT-based communication is active in different GSLCs derived from the external tumoral areas associated to GBM relapse, and we propose that they participate together with TMs in tumor networking. Genomic data are often produced in batches due to practical restrictions, which may lead to unwanted variation in data caused by discrepancies across batches. Such" batch effects" often have negative impact on downstream biological analysis and need careful consideration. In practice, batch effects are usually addressed by specifically designed software, which merge the data from different batches, then estimate batch effects and remove them from the data. Here we focus on classification and prediction problems, and propose a different strategy based on ensemble learning. We first develop prediction models within each batch, then integrate them through ensemble weighting methods. We provide a systematic comparison between these two strategies using studies targeting diverse populations infected with tuberculosis. In one study, we simulated increasing levels of heterogeneity across random subsets of the study, which we treat as simulated batches. We then use the two methods to develop a genomic classifier for the binary indicator of disease status. We evaluate the accuracy of prediction in another independent study targeting a different population cohort. We observed that in independent validation, while merging followed by batch adjustment provides better discrimination at low level of heterogeneity, our ensemble learning strategy achieves more robust performance, especially at high severity of batch effects. These observations provide practical guidelines for handling batch effects in the development and evaluation of genomic classifiers. Code to reproduce the results in this paper is available at https//github.com/zhangyuqing/bea_ensemble. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online.Arginine methylation is a post-translational modification that is implicated in multiple biological functions including transcriptional regulation. The expression of protein arginine methyltransferases (PRMT) has been shown to be upregulated in various cancers. PRMTs have emerged as attractive targets for the development of new cancer therapies. Here, we describe the identification of a natural compound, licochalcone A, as a novel, reversible and selective inhibitor of PRMT6. Since expression of PRMT6 is upregulated in human breast cancers and is associated with oncogenesis, we used the human breast cancer cell line system to study the effect of licochalcone A treatment on PRMT6 activity, cell viability, cell cycle, and apoptosis. We demonstrated that licochalcone A is a non-S-adenosyl L-methionine (SAM) binding site competitive inhibitor of PRMT6. In MCF-7 cells, it inhibited PRMT6-dependent methylation of histone H3 at arginine 2 (H3R2), which resulted in a significant repression of estrogen receptor activity. Licochalcone A exhibited cytotoxicity towards human MCF-7 breast cancer cells, but not MCF-10A human breast epithelial cells, by upregulating p53 expression and blocking cell cycle progression at G2/M, followed by apoptosis. Thus, licochalcone A has potential for further development as a therapeutic agent against breast cancer.Lidocaine is a commonly used drug to alleviate neuropathic pain (NP). This work aims to investigate the mechanism of lidocaine in alleviating NP. Chronic constriction injury (CCI) rats were established by surgery to induce NP. click here We observed the mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL) of rats. Immunofluorescence staining was performed to determine the LC3/GFAP-positive cells. Rat astrocytes were treated with lipopolysaccharide (LPS) to induce CCI, and then treated with lidocaine or 3-MA (autophagy inhibitor). CCK-8 was performed to detect cell proliferat