Results 71 to 80 of about 2,424,188 (403)
The ability of Hepascore to predict liver fibrosis in chronic liver disease: A meta-analysis [PDF]
, 2016 Background & Aims: Hepascore is a serum model that was developed to assess the severity of liver fibrosis. It has been well validated in common causes of chronic liver disease.Adams, Leon A., Bulsara, Max, Huang, Yi, Jeffrey, Gary P, Joseph, John +4 morecore +2 more sourcesThe progression of liver fibrosis is related with overexpression of the miR-199 and 200 families. [PDF]
PLoS ONE, 2011 BACKGROUND: Chronic hepatitis C (CH) can develop into liver cirrhosis (LC) and hepatocellular carcinoma (HCC). Liver fibrosis and HCC development are strongly correlated, but there is no effective treatment against fibrosis because the critical mechanism Yoshiki Murakami, Hidenori Toyoda, Masami Tanaka, Masahiko Kuroda, Yoshinori Harada, Fumihiko Matsuda, Atsushi Tajima, Nobuyoshi Kosaka, Takahiro Ochiya, Kunitada Shimotohno +9 moredoaj +1 more sourceRecombinant truncated latency-associated peptide alleviates liver fibrosis in vitro and in vivo via inhibition of TGF-β/Smad pathway
Molecular Medicine, 2022 Background Liver fibrosis is a progressive liver injury response. Transforming growth factor β1 (TGF-β1) is oversecreted during liver fibrosis and promotes the development of liver fibrosis.Xudong Song, Jiayi Shi, Jieting Liu, Yong Liu, Yang Yu, Yufei Qiu, Zhiqin Cao, Yu Pan, Xiaohuan Yuan, Yanhui Chu, Dan Wu +10 moredoaj +1 more sourceProbiotic Lactobacillus rhamnosus GG Prevents Liver Fibrosis Through Inhibiting Hepatic Bile Acid Synthesis and Enhancing Bile Acid Excretion in Mice
Hepatology, 2020 Cholestatic liver disease is characterized by gut dysbiosis and excessive toxic hepatic bile acids (BAs). Modification of gut microbiota and repression of BA synthesis are potential strategies for the treatment of cholestatic liver disease.Yunhuan Liu, Ke-fei Chen, Fengyuan Li, Zelin Gu, Qi Liu, Liqing He, Tuo Shao, Qing Song, Fenxia Zhu, Lihua Zhang, Mengwei Jiang, Yun Zhou, S. Barve, Xiang Zhang, C. McClain, Wenke Feng +15 moresemanticscholar +1 more sourcePoly (ADP-ribose) polymerase-1 is a key mediator of liver inflammation and fibrosis. [PDF]
, 2014 Poly (ADP-ribose) polymerase 1 (PARP-1) is a constitutive enzyme, the major isoform of the PARP family, which is involved in the regulation of DNA repair, cell death, metabolism, and inflammatory responses.Boulares, A.H., Cao, Z., Erdelyi, K., Gao, B., Hamdaoui, N., Haskó, G., Holovac, E., Horváth, B., Lafdil, F., Liaudet, L., Mukhopadhyay, P., Pacher, P., Park, O., Rajesh, M., Szabo, C., Wang, H., Wang, Y. +16 morecore +1 more sourceAssociation Between Fibrosis Stage and Outcomes of Patients with Non-Alcoholic Fatty Liver Disease: a Systematic Review and Meta-Analysis.
Gastroenterology, 2020 BACKGROUND & AIMS
Biopsy-confirmed liver fibrosis is a prognostic factor for patients with non-alcoholic fatty liver disease (NAFLD). We performed a systematic review to quantify the prognostic value of fibrosis stage in patients with NAFLD and the ...R. Taylor, R. J. Taylor, S. Bayliss, H. Hagström, P. Nasr, J. Schattenberg, M. Ishigami, H. Toyoda, V. Wai‐Sun Wong, N. Peleg, A. Shlomai, G. Sebastiani, Yuya Seko, N. Bhala, Z. Younossi, Q. Anstee, S. McPherson, P. Newsome +17 moresemanticscholar +1 more sourceCost-effectiveness of non-invasive methods for assessment and monitoring of liver fibrosis and cirrhosis in patients with chronic liver disease: systematic review and economic evaluation [PDF]
, 2015 BACKGROUND: Liver biopsy is the reference standard for diagnosing the extent of fibrosis in chronic liver disease; however, it is invasive, with the potential for serious complications., , , , , , , , , , , , , , , , , , , , , , , Adams, Adams, Afdhal, Aguirre, Ahmad, Aithal, Al-Mohri, Anaparthy, Andersson, Angulo, Arena, Argo, Asbach, Ascher, Aube, Aube, Awaya, Barshop, Barton, Bataller, Beckebaum, Bedossa, Bejarano, Belfort, Bellentani, Ben Jazia, Bennett, Berg, Blomme, Bolondi, Bonkovsky, Boring, Borroni, Bourliere, Boursier, Boursier, Briggs, Brown, Brunetto, Buntinx, Burton, Cales, Cales, Cales, Calvaruso, Calvaruso, Calvaruso, Camma, Cardi, Cardoso, Carlson, Carrion, Carvalho-Filho, Castera, Castera, Castera, Castera, Castera, Castera, Ceriani, Chalasani, Chalasani, Chan, Chang, Chavez-Tapia, Cheinquer, Chen, Chen, Chen, Cheung, Cho, Cholongitas, Chong, Chou, Christensen, Chrysanthos, Cioni, Claxton, Cobbold, Colletta, Colli, Colli, Colombo, Corradi, Crespo, Cross, Crowley, Crowley, Curtis, da Silva, Dakin, Danila, David, Davis, de Franchis, De Freitas, De Jongh, de Ledinghen, De Ledinghen, Degos, Denzer, Detre, Di Bisceglie, Di Marco, Dinesen, Dixon, Dixon, Do, Donnan, Drummond, Dusheiko, D’Amico, D’Onofrio, Ekstedt, Esmat, Esteban, Fabris, Fahmy, Fattovich, Fattovich, Fattovich, Fattovich, Fattovich, Fattovich, Feldstein, Fenwick, Ferral, Fontaine, Fontana, Fontanges, Forestier, Forns, Fraquelli, Fried, Fried, Friedrich-Rust, Fujii, Fujimoto, Fung, Gaia, Gaia, Gaiani, Ganne-Carrie, Gara, Germani, Germani, Giannini, Gierblinski, Gobel, Goodman, Goyal, Grieve, Grishchenko, Guajardo-Salinas, Guechot, Guechot, Guha, Gui, Guo-Qiu, Guzelbulut, Hadziyannis, Halfon, Halfon, Halfon, Hall, Harada, Harrison, Haukeland, Hongbo, Hoofnagle, Hsieh, Hu, Huang, Hui, Huwart, Iacobellis, Ibrahim, Idilman, Imbert-Bismut, Imperiale, Isgro, Ishibashi, Islam, Iushchuk, Jacobson, Jacobson, Jacobson, Janssens, John-Baptiste, Jones, Jones, Joseph, Kalantari, Kamal, Kamphues, Kandemir, Kaneda, Kayadibi, Kelleher, Kelleher, Khan, Khosravi, Kilpe, Kim, Kim, Kim, Kim, Kim, Kim, Kim, Kim, Koda, Koizumi, Kwok, Lackner, Ladenheim, Ladero, Lai, Lampertico, Lau, Lavallard, Lavanchy, Lavine, Lawitz, Lee, Lee, Lee, Leroy, Leroy, Leroy, Lesmana, Levy, Lewin, Li, Liaw, Liaw, Lichtinghagen, Lieber, Lin, Liu, Liu, Liu, Liu, Liu, Liu, Loko, Longworth, Lupsor, Lupsor, Lupsor, Lutz, Lydatakis, Macaskill, Macias, Macias, Mahadeva, Mahady, Mahady, Mallet, Manns, Manns, Manousou, Manousou, Marcellin, Marcellin, Marcellin, Martinez, Martinez, McLernon, McPherson, McPherson, Melin, Miailhes, Mohamadnejad, Moreno-Otero, Morgan, Morikawa, Motosugi, Mueller, Mummadi, Murawaki, Musso, Myers, Myers, Nagata, Nahon, Naveau, Naveau, Nguyen-Khac, Nishiura, Nitta, Nojiri, Numminen, Nunes, Obara, Ogawa, Oliveira, Ong, Orlacchio, Orlacchio, Osakabe, O’Shea, Paggi, Pais, Palmeri, Papalavrentios, Parise, Park, Park, Park, Park, Park, Parkes, Patel, Patel, Pawitpok, Pereira, Petta, Pimentel, Pohl, Poordad, Poynard, Poynard, Poynard, Poynard, Prati, Promrat, Qian, Qiu, Qureshi, Raftopoulos, Rakoski, Ramachandran, Raszeja-Wyszomirska, Ratziu, Ratziu, Ratziu, Ratziua, Realdi, Reedy, Reitsma, Ronot, Rosenberg, Rossi, Rossini, Ruffillo, Rustogi, Saab, Said, Saitou, Sakugawa, Sancheztapias, Sandrasegaran, Santos, Sanvisens, Sanyal, Sanyal, Sanyal, Schiavon, Schiavon, Schneider, Schneider, Schuetz, Sebastian, Sebastiani, Sebastiani, Sebastiani, Sebastiani, Sene, Seto, Sharabash, Shastry, Shen, Shepherd, Shepherd, Shepherd, Sherman, Sheth, Shields, Shiffman, Shimada, Shin, Shinkins, Shiramizu, Siebert, Sinakos, Singal, Sirli, Snyder, Snyder, Sohn, Sohn, Sokucu, Sonnenberg, Sporea, Sporea, Sporea, Sporea, Sporea, Standish, Sterling, Stevenson, Stibbe, Sud, Sumida, Sumida, Suzuki, Sweeting, Takeda, Tassopoulos, Terjung, Testa, Thompson, Thompson, Thompson, Thompson Coon, Thompson Coon, Tillmann, Tome, Toniutto, Tran, Trang, Trifan, Trocme, Tsochatzis, Tsochatzis, Tsochatzis, Tsochatzis, Tsochatzis, Tural, Uygun, Vallet-Pichard, Valva, Vanbiervliet, Varaut, Veenstra, Veenstra, Venkatesh, Vernon, Verrill, Viana, Vigano, Viganò, Wai, Wang, Wang, Ware, Westin, Whiting, Whiting, Whiting, Whiting, Wilby, Wilck, Wilson, Wong, Wong, Wong, Wong, Wong, Wong, Wong, Wong, Woo, Wright, Wu, Xu, Yoneda, Yoneda, Younossi, Younossi, Yuen, Zaman, Zarski, Zarski, Zhang, Zhang, Zhang, Zhu, Zhu, Ziol +503 morecore +2 more sourcesFrom omics to AI—mapping the pathogenic pathways in type 2 diabetes
FEBS Letters, EarlyView.Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.Siobhán O'Sullivan, Lu Qi, Pierre Zalloua +2 morewiley +1 more source