Notes

Nanoparticle-based medication shipping methods using american platinum eagle drug treatments with regard to overcoming cancer medication level of resistance.
DNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.

In this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected fr us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http//www.sta.cuhk.edu.hk/YWei/PanDM.html .
PanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http//www.sta.cuhk.edu.hk/YWei/PanDM.html .
Tezepelumab is a human monoclonal antibody that blocks the activity of the epithelial cytokine thymic stromal lymphopoietin. The efficacy, safety and oral corticosteroid-sparing potential of tezepelumab are being investigated in two ongoing, phase 3, randomized, double-blind, placebo-controlled studies (NAVIGATOR [NCT03347279] and SOURCE [NCT03406078]). DESTINATION (NCT03706079) is a long-term extension (LTE) of these studies.

DESTINATION is a randomized, double-blind, placebo-controlled LTE study in adults (18-80years old) and adolescents (12-17years old) with severe, uncontrolled asthma who are receiving treatment with medium- or high-dose inhaled corticosteroids plus at least one additional controller medication with or without oral corticosteroids. The study population will comprise patients who complete the 52- and 48-week NAVIGATOR and SOURCE studies, respectively. Patients who were randomized to receive tezepelumab 210mg every 4weeks (Q4W) in either predecessor study will continue to receive this rbility and efficacy of tezepelumab versus placebo with continued dosing for up to 2years. DESTINATION will also evaluate the clinical effect of tezepelumab after treatment cessation. This LTE study aims to elucidate the long-term safety implications of receiving tezepelumab and to assess its potential long-term treatment benefits in patients with severe, uncontrolled asthma.

NCT03706079 (ClinicalTrials.gov). Registered 15 October 2018.
NCT03706079 (ClinicalTrials.gov). Registered 15 October 2018.
Studies have found that miRNAs play an important role in many biological activities involved in human diseases. Revealing the associations between miRNA and disease by biological experiments is time-consuming and expensive. The computational approaches provide a new alternative. However, because of the limited knowledge of the associations between miRNAs and diseases, it is difficult to support the prediction model effectively.

In this work, we propose a model to predict miRNA-disease associations, MDAPCOM, in which protein information associated with miRNAs and diseases is introduced to build a global miRNA-protein-disease network. Subsequently, diffusion features and HeteSim features, extracted from the global network, are combined to train the prediction model by eXtreme Gradient Boosting (XGBoost).

The MDAPCOM model achieves AUC of 0.991 based on 10-fold cross-validation, which is significantly better than that of other two state-of-the-art methods RWRMDA and PRINCE. Furthermore, the model performs well on three unbalanced data sets.

The results suggest that the information behind proteins associated with miRNAs and diseases is crucial to the prediction of the associations between miRNAs and diseases, and the hybrid feature representation in the heterogeneous network is very effective for improving predictive performance.
The results suggest that the information behind proteins associated with miRNAs and diseases is crucial to the prediction of the associations between miRNAs and diseases, and the hybrid feature representation in the heterogeneous network is very effective for improving predictive performance.
Vitamin K antagonist (warfarin) is the most classical and widely used oral anticoagulant with assuring anticoagulant effect, wide clinical indications and low price. Warfarin dosage requirements of different patients vary largely. For warfarin daily dosage prediction, the data imbalance in dataset leads to inaccurate prediction on the patients of rare genotype, who usually have large stable dosage requirement. To balance the dataset of patients treated with warfarin and improve the predictive accuracy, an appropriate partition of majority and minority groups, together with an oversampling method, is required.

To solve the data-imbalance problem mentioned above, we developed a clustering-based oversampling technique denoted as DBCSMOTE, which combines density-based spatial clustering of application with noise (DBCSCAN) and synthetic minority oversampling technique (SMOTE). DBCSMOTE automatically finds the minority groups by acquiring the association between samples in terms of the clinical features/genotyprmance in many cases. In terms of predictive accuracy, RF is not as good as BRT. However, RF still has a powerful ability in generating a highly accurate model as the dataset increases; the software "WarfarinSeer v2.0" is a test version, which packed DBCSMOTE-BRT/RF. selleck screening library It could be a convenient tool for clinical application in warfarin treatment.
Website: https://www.selleckchem.com/