Helmsjames1463

High BIRC5 expression was responsible for shorter relapse-free survival, worse overall survival, reduced distant metastasis free survival, and increased risk of metastatic relapse event. BIRC5-drug interaction network indicated that several common drugs could modulate BIRC5 expression. MEK inhibitor Furthermore, a positive correlation between BIRC5 andcell-division cycle protein 20 (CDC20) gene was confirmed. CONCLUSION BIRC5 may be adopted as a promising predictive marker and potential therapeutic target in breast cancer. Further large-scale studies are needed to more precisely confirm the value of BIRC5 in treatment of breast cancer. © 2020 The Author(s).Drug repositioning can drastically decrease the cost and duration taken by traditional drug research and development while avoiding the occurrence of unforeseen adverse events. With the rapid advancement of high-throughput technologies and the explosion of various biological data and medical data, computational drug repositioning methods have been appealing and powerful techniques to systematically identify potential drug-target interactions and drug-disease interactions. In this review, we first summarize the available biomedical data and public databases related to drugs, diseases and targets. Then, we discuss existing drug repositioning approaches and group them based on their underlying computational models consisting of classical machine learning, network propagation, matrix factorization and completion, and deep learning based models. We also comprehensively analyze common standard data sets and evaluation metrics used in drug repositioning, and give a brief comparison of various prediction methods on the gold standard data sets. Finally, we conclude our review with a brief discussion on challenges in computational drug repositioning, which includes the problem of reducing the noise and incompleteness of biomedical data, the ensemble of various computation drug repositioning methods, the importance of designing reliable negative samples selection methods, new techniques dealing with the data sparseness problem, the construction of large-scale and comprehensive benchmark data sets and the analysis and explanation of the underlying mechanisms of predicted interactions. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.BACKGROUND Collagen type X alpha 1 (COL10A1) is overexpressed in diverse tumors and displays vital roles in tumorigenesis. However, the prognostic value of COL10A1 in breast cancer remains unclear. METHODS The expression of COL10A1 was analyzed by the Oncomine database and UALCAN cancer database. The relationship between COL10A1 expression level and clinical indicators including prognostic data in breast cancer were analyzed by the Kaplan-Meier Plotter, PrognoScan, and Breast Cancer Gene-Expression Miner (bc-GenExMiner) databases. RESULTS COL10A1 was up-regulated in different subtypes of breast cancer. Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2) status and nodal status were positively correlated with COL10A1 expression. Conversely, age, the Scarff-Bloom-Richardson (SBR) grade, basal-like status, and triple-negative status were negatively related to COL10A1 level in breast cancer samples compared with normal tissues. Patients with increased COL10A1 expression level showed worse overall survival (OS), relapse-free survival (RFS), distant metastasis-free survival (DMFS) and disease-free survival (DFS). COL10A1 was positively correlated with metastatic relapse-free survival. GSEA analysis revealed that enrichment of TGF-β signaling pathway. 15-leucine-rich repeat containing membrane protein (LRRC15) is a correlated gene of COL10A1. CONCLUSION Bioinformatics analysis revealed that COL10A1 might be considered as a predictive biomarker for prognosis of breast cancer. Further experiments and clinical trials are essential to elucidate the value of COL10A1 in breast cancer treatment. © 2020 The Author(s).BACKGROUND Longitudinal evidence of poor visual acuity associating with higher risk of incident dementia is mixed. This study aimed to examine if poor visual acuity was associated with higher dementia incidence in a large community cohort of older adults, independent of the possible biases relating to misclassification error, reverse causality, and confounding effects due to health problems and behaviours. METHODS A total of 15,576 community-living older adults without dementia at baseline were followed for 6 years to the outcome of incident dementia, which was diagnosed according to the ICD-10 or a Clinical Dementia Rating of 1 to 3. Visual acuity was assessed using the Snellen's chart at baseline and follow-up. Important variables including demographics (age, sex, education, and socioeconomic status), physical and psychiatric comorbidities (cardiovascular risks, ophthalmological conditions, hearing impairment, poor mobility, and depression), and lifestyle behaviours (smoking, diet, physical, intellectual, and social activities) were also assessed. RESULTS Over 68,904 person-years of follow-up, 1,349 participants developed dementia. Poorer visual acuity at baseline was associated with higher dementia incidence in 6 years, even after adjusting for demographics, health problems, and lifestyle behaviours, and excluding those who developed dementia within 3 years after baseline. Compared with normal vision, the hazard ratio of dementia was 1.19 (p=0.31), 2.09 (p less then 0.001), and 8.66 (p less then 0.001) for mild, moderate, and severe visual impairment, respectively. CONCLUSIONS Moderate-to-severe visual impairment could be a potential predictor and possibly a risk factor for dementia. From a clinical perspective, older adults with poor visual acuity might warrant further risk assessment for dementia. © The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America.Biofilms that form on reusable medical devices are a cause of hospital acquired infections; however, sanitization of biofilms is a challenge due to their dense extracellular matrix. This work presents an innovative strategy using biocide-loaded iron oxide nanoparticles transported within the matrix via a magnetic field to eradicate biofilms. Results show that the active delivery of the biocide to underlying cells effectively penetrates the extracellular matrix and inactivates Methicillin resistant Staphylococcus aureus (MRSA) biofilms (responsible for several difficult-to-treat infections in humans). To optimize this treatment, the loading of spherical, cubic and tetrapod-shaped nanoparticles with a model biocide, CTAB (cetyltrimethylammonium bromide) was studied. Biocide loading was determined to be dependent on the shapes' surface charge density instead of the surface area, meaning that biocide attachment is greater for nanoparticles with sharp edges (e.g. cubes and tetrapods). These results can be used to optimize treatment efficacy, and help further understanding of biofilm and nanoparticle surface zeta potentials, and the nanoparticle-biofilm interactions.