MYCN amplification is the most common genetic alteration in neuroblastoma and plays a critical role in neuroblastoma tumorigenesis. MYCN regulates neuroblastoma cell differentiation, which is one of the mechanisms underlying its oncogenic function. We recently identified a group of differentiation-inducing microRNAs. Given the demonstrated inter-regulation between MYCN and microRNAs, we speculated that MYCN and the differentiation-inducing microRNAs might form an interaction network to control the differentiation of neuroblastoma cells. In this study, we found that eight of the thirteen differentiation-inducing microRNAs, miR-506-3p, miR-124-3p, miR-449a, miR-34a-5p, miR-449b-5p, miR-103a-3p, miR-2110 and miR-34b-5p, inhibit N-Myc expression by either directly targeting the MYCN 3'UTR or through indirect regulations. Further investigation showed that both MYCN-dependent and MYCN-independent pathways play roles in mediating the differentiation-inducing function of miR-506-3p and miR-449a, two microRNAs that dramatically down-regulate MYCN expression. On the other hand, we found that N-Myc inhibits the expression of multiple differentiation-inducing microRNAs, suggesting that these miRNAs play a role in mediating the function of MYCN. In examining the published dataset collected from clinical neuroblastoma specimens, we found that expressions of two miRNAs, miR-137 and miR-2110, were significantly anti-correlated with MYCN mRNA levels, suggesting their interactions with MYCN play a clinically-relevant role in maintaining the MYCN and miRNA expression levels in neuroblastoma. Our findings altogether suggest that MYCN and differentiation-inducing miRNAs form an interaction network that play an important role in neuroblastoma tumorigenesis through regulating cell differentiation.

To determine whether supplementation with turmeric or curcumin extract effects pain and physical function in individuals with knee osteoarthritis (OA). Second, we investigated the therapeutic response (pain and function) of turmeric compared with non-steroidal anti-inflammatory drugs (NSAIDs).

To develop a Gout, Hyperuricaemia and Crystal-Associated Disease Network (G-CAN) common language definition of gout, with the goal of increasing public understanding and awareness, and ensure consistent and understandable messages about gout.

The bacterium Caulobacter crescentus is a popular model for the study of cell cycle regulation and senescence. The large prolate siphophage phiCbK has been an important tool in C. crescentus biology, and has been studied in its own right as a model for viral morphogenesis. Although a system of some interest, to date little genomic information is available on phiCbK or its relatives.

Electrical property tomography (EPT) is a noninvasive method that uses magnetic resonance imaging (MRI) to estimate the conductivity and permittivity of tissues, and hence, can be used as a biomarker. One branch of EPT is based on the correlation of water and relaxation time T1 with the conductivity and permittivity of tissues. This correlation was applied to a curve-fitting function to estimate electrical properties, it was found to have a high correlation between permittivity and T1 however the computation of conductivity based on T1 requires to estimate the water content. In this study, we developed multiple phantoms with several ingredients that modify the conductivity and permittivity and explored the use of machine learning algorithms to have a direct estimation of conductivity and permittivity based on MR images and the relaxation time T1. To train the algorithms, each phantom was measured using a dielectric measurement device to acquire the true conductivity and permittivity. MR images were taken for each phantom, and the T1 values were measured. Then, the acquired data were tested using curve fitting, regression learning, and neural fit models to estimate the conductivity and permittivity values based on the T1 values. In particular, the regression learning algorithm based on Gaussian process regression showed high accuracy with a coefficient of determination R2 of 0.96 and 0.99 for permittivity and conductivity, respectively. The estimation of permittivity using regression learning demonstrated a lower mean error of 0.66% compared to the curve fitting method, which resulted in a mean error of 3.6%. The estimation of conductivity also showed that the regression learning approach had a lower mean error of 0.49%, whereas the curve fitting method resulted in a mean error of 6%. The findings suggest that utilizing regression learning models, specifically Gaussian process regression, can result in more accurate estimations for both permittivity and conductivity compared to other methods.