Serrano Acosta (dimpleflesh7)

The TGF-β receptor kinase inhibitors (TRKI) have been reported to inhibit tumorigenicity in colon cancer. However, there is no direct evidence showing that these inhibitors function through inhibiting the TGF-β- mediated tumor-promoting effects in vivo. We established a TGF-β inducible reporter system by inserting a luciferase reporter gene to the vector downstream of TGF-β-inducible promoter elements, and transfected it into colon cancer cell lines. TRKIs SB431542 and LY2109761 were used to treat TGF-β inducible cells in vitro and in vivo. The luciferase activity was induced 5.24-fold by TGF-β in CT26 inducible cells, while it was marginally changed in MC38 inducible cells lacking Smad4 expression. Temporary treatment of mice with SB431542 inhibited the TGF-β pathway and TGF-β induced bioluminescence activity in vivo. Long-term treatment with LY2109761 inhibited tumorigenicity and liver metastasis in vivo in concomitant with reduced luciferase activity in the tumor. In this study, we established a model to monitor the TGF-β pathway in vivo and to compare the antitumor effects of TRKIs. Based on this novel experimental tool, we provided direct evidences that LY2109761 inhibits tumorigenicity and liver metastasis by blocking the pro-oncogenic functions of TGF-β in vivo. Unsupervised learning approaches are frequently employed to stratify patients into clinically relevant subgroups and to identify biomarkers such as disease-associated genes. However, clustering and biclustering techniques are oblivious to the functional relationship of genes and are thus not ideally suited to pinpoint molecular mechanisms along with patient subgroups. We developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. This allows BiCoN to simultaneously pinpoint molecular mechanisms responsible for the patient grouping. Network-constrained clustering of genes makes BiCoN more robust to noise and batch effects than typical clustering and biclustering methods. BiCoN can faithfully reproduce known disease subtypes as well as novel, clinically relevant patient subgroups, as we could demonstrate using breast and lung cancer datasets. In summary, BiCoN is a novel systems medicine tool that combines several heuristic optimization strategies for robust disease mechanism extraction. BiCoN is well-documented and freely available as a python package or a web interface. PyPI package https//pypi.org/project/bicon. https//exbio.wzw.tum.de/bicon. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online.Non-communicable diseases are increasing in developing countries and control of diabetes and hypertension is needed to reduce rates of the leading causes of morbidity and mortality, stroke and ischaemic heart disease. We evaluated a programme in Cambodia, financed by a revolving drug fund, which utilizes Peer Educators to manage diabetes and hypertension in the community. We assessed clinical outcomes and retention in the programme. For all people enrolled in the programme between 2007 and 2016, the average change in blood pressure (BP) and percentage with controlled hypertension (BP less then 140/ less then 90 mmHg) or diabetes (fasting blood glucose (BG) less then 7mg/dl, post-prandial BG less then 130 mg/dl, or HBA1C less then 7%) was calculated every 6 months from enrolment. Attrition rate in the nth year of enrolment was calculated; associations with loss to follow-up were explored using cox regression. A total of 9139 patients enrolled between January 2007 and March 2016. For all people with hypertension, mean change in systolic and diastolic BP within the first year was -15.1 mmHg (SD 23.6, P less then 0.0001) a