AIM： To investigate the role of small intestinal carcinoid tumor-derived fibrotic mediators, TGFβ1 and CTGF, in the mediation of fibrosis via activation of an ＂intestinal＂ stellate cell. METHODS： GI carcinoid tum...AIM： To investigate the role of small intestinal carcinoid tumor-derived fibrotic mediators, TGFβ1 and CTGF, in the mediation of fibrosis via activation of an ＂intestinal＂ stellate cell. METHODS： GI carcinoid tumors were collected for Q RT-PCR analysis of CTGF and TGFβ1. Markers of stellate cell desmoplasia were identified in peritoneal fibrosis by immunohistochemistry and stellate cells cultured from fresh resected fibrotic tissue. CTGF and TGFβ1 were evaluated using quantitative tissue array profiling （AQUA analysis） in a GI carcinoid tissue microarray （TMA） with immunostaining and correlated with clinical and histologically documented fibrosis. Serum CTGF was analyzed using a sandwich ELISA assay. RESULTS： Message levels of both CTGF and TGFβ1 in SI carcinoid tumors were significantly increased （〉 2-fold, P 〈 0.05） versus normal mucosa and gastric （non-fibrotic） carcinoids. Activated stellate cells and markers of stellate cell-mediated fibrosis （vimentin, desmin） were identified in histological fibrosis. An intestinal stellate cell was immunocytochemically and biochemically characterized and its TGFβ1 （10-7M） initiated CTGF transcription response （〉 3-fold, P 〈 0.05） demonstrated. In SI carcinoid tumor patients with documented fibrosis, TMA analysis demonstrated higher CTGF immunostaining （AQUA Score： 92 ± 8, P 〈0.05）, as well as elevated TGFβ1 （90.6 ± 4.4, P 〈 0.05）. Plasma CTGF （normal 12.5 ± 2.6 ng/mL） was increased in SI carcinoid tumor patients （31 ± 10 ng/mL, P 〈 0.05） compared to non-fibrotic GI carcinoids （〈 15 ng/mL） CONCLUSION： SI carcinoid tumor fibrosis is a CTGF/ TGFβl-mediated stellate cell-driven fibrotic response. The delineation of the biology of fibrosis will facilitate diagnosis and enable development of agents to obviate its local and systemic complications.展开更多
The ensemble technique has been widely used in numerical weather prediction and extended-range forecasting.Current approaches to evaluate the predictability using the ensemble technique can be divided into two major g...The ensemble technique has been widely used in numerical weather prediction and extended-range forecasting.Current approaches to evaluate the predictability using the ensemble technique can be divided into two major groups.One is dynamical,including generating Lyapunov vectors,bred vectors,and singular vectors,sampling the fastest error-growing directions of the phase space,and examining the dependence of prediction efficiency on ensemble size.The other is statistical,including distributional analysis and quantifying prediction utility by the Shannon entropy and the relative entropy.Currently,with simple models,one could choose as many ensembles as possible,with each ensemble containing a large number of members.When the forecast models become increasingly complicated,however,one would only be able to afford a small number of ensembles,each with limited number of members,thus sacrificing estimation accuracy of the forecast errors.To uncover connections between different information theoretic approaches and between dynamical and statistical approaches,we propose an (∈;T)-entropy and scale-dependent Lyapunov exponent——based general theoretical framework to quantify information loss in ensemble forecasting.More importantly,to tremendously expedite computations,reduce data storage,and improve forecasting accuracy,we propose a technique for constructing a large number of "pseudo" ensembles from one single solution or scalar dataset.This pseudo-ensemble technique appears to be applicable under rather general conditions,one important situation being that observational data are available but the exact dynamical model is unknown.展开更多
基金Supported in part by the Bruggeman Medical Foundation
文摘AIM： To investigate the role of small intestinal carcinoid tumor-derived fibrotic mediators, TGFβ1 and CTGF, in the mediation of fibrosis via activation of an ＂intestinal＂ stellate cell. METHODS： GI carcinoid tumors were collected for Q RT-PCR analysis of CTGF and TGFβ1. Markers of stellate cell desmoplasia were identified in peritoneal fibrosis by immunohistochemistry and stellate cells cultured from fresh resected fibrotic tissue. CTGF and TGFβ1 were evaluated using quantitative tissue array profiling （AQUA analysis） in a GI carcinoid tissue microarray （TMA） with immunostaining and correlated with clinical and histologically documented fibrosis. Serum CTGF was analyzed using a sandwich ELISA assay. RESULTS： Message levels of both CTGF and TGFβ1 in SI carcinoid tumors were significantly increased （〉 2-fold, P 〈 0.05） versus normal mucosa and gastric （non-fibrotic） carcinoids. Activated stellate cells and markers of stellate cell-mediated fibrosis （vimentin, desmin） were identified in histological fibrosis. An intestinal stellate cell was immunocytochemically and biochemically characterized and its TGFβ1 （10-7M） initiated CTGF transcription response （〉 3-fold, P 〈 0.05） demonstrated. In SI carcinoid tumor patients with documented fibrosis, TMA analysis demonstrated higher CTGF immunostaining （AQUA Score： 92 ± 8, P 〈0.05）, as well as elevated TGFβ1 （90.6 ± 4.4, P 〈 0.05）. Plasma CTGF （normal 12.5 ± 2.6 ng/mL） was increased in SI carcinoid tumor patients （31 ± 10 ng/mL, P 〈 0.05） compared to non-fibrotic GI carcinoids （〈 15 ng/mL） CONCLUSION： SI carcinoid tumor fibrosis is a CTGF/ TGFβl-mediated stellate cell-driven fibrotic response. The delineation of the biology of fibrosis will facilitate diagnosis and enable development of agents to obviate its local and systemic complications.
基金Project supported by the National Science Foundation (Nos.CMMI-0825311,CMMI-0826119)
文摘The ensemble technique has been widely used in numerical weather prediction and extended-range forecasting.Current approaches to evaluate the predictability using the ensemble technique can be divided into two major groups.One is dynamical,including generating Lyapunov vectors,bred vectors,and singular vectors,sampling the fastest error-growing directions of the phase space,and examining the dependence of prediction efficiency on ensemble size.The other is statistical,including distributional analysis and quantifying prediction utility by the Shannon entropy and the relative entropy.Currently,with simple models,one could choose as many ensembles as possible,with each ensemble containing a large number of members.When the forecast models become increasingly complicated,however,one would only be able to afford a small number of ensembles,each with limited number of members,thus sacrificing estimation accuracy of the forecast errors.To uncover connections between different information theoretic approaches and between dynamical and statistical approaches,we propose an (∈;T)-entropy and scale-dependent Lyapunov exponent——based general theoretical framework to quantify information loss in ensemble forecasting.More importantly,to tremendously expedite computations,reduce data storage,and improve forecasting accuracy,we propose a technique for constructing a large number of "pseudo" ensembles from one single solution or scalar dataset.This pseudo-ensemble technique appears to be applicable under rather general conditions,one important situation being that observational data are available but the exact dynamical model is unknown.
