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Prognostic significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in patients with stage Ⅲ and Ⅳcolorectal cancer

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World Journal of Gastroenterology 2017年第3期23卷 505-515页

作者： Jae Hyun Kim jun yeop lee Hae Koo Kim Jin Wook lee Sung Gyu jung Kyoungwon jung Sung Eun Kim Won Moon Moo In Park Seun Ja Park Division of Gastroenterology Department of Internal MedicineKosin University College of MedicineBusan 602-702South Korea

AIM To evaluate the prognostic value of the neutrophil-tolymphocyte ratio(NLR) and platelet-to-lymphocyte ratio(PLR) in patients with colorectal cancer(CRC).METHODS Between April 1996 and December 2010, medical records from a total of 1868 patients with CRC were retrospectively reviewed. The values of simple inflammatory markers including NLR and PLR in predicting the long-term outcomes of these patients were evaluated using Kaplan-Meier curves and Cox regression *** The median follow-up duration was 46 mo(interquartile range, 22-73). The estimation of NLR and PLR was based on the time of diagnosis. In multivariate Cox regression analysis, high NLR (≥ 3.0) and high PLR(≥ 160) were independent risk factors predicting poor long-term outcomes in patients with stage Ⅲ and Ⅳ CRC. However, high NLR and high PLR were not prognostic factors in patients with stage Ⅰ and Ⅱ *** In this study, we identified that high NLR (≥ 3.0) and high PLR (≥ 160) are useful prognostic factors to predict long-term outcomes in patients with stage Ⅲ and Ⅳ CRC.

关键词： Colorectal cancer Neutrophil Lymphocyte Platelet Prognosis

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Machine Learning-Assisted Fabrication of PCBM-Perovskite Solar Cells with Nanopatterned TiO2 Layer

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Energy & Environmental Materials 2024年第4期7卷 223-230页

作者： Siti Norhasanah Sanimu Hwa-Young Yang Jeevan Kandel Ye-Chong Moon Gangasagar Sharma Gaudel Seung-Ju Yu Yong Ju Kim Sejung Kim Bong-Hyun jun Won-yeop Rho Graduate School of Integrated Energy-AI Jeonbuk National University School of Energy and Chemical Engineering Ulsan National Institute of Science and Technology (UNIST) Department of Herbal Medicine Resources College of Environmental and Bioresource Sciences Jeonbuk National University School of Chemical Engineering Jeonbuk National University Department of Bioscience and Biotechnology Konkuk University School of International Engineering and Science Jeonbuk National University

To unlock the full potential of PSCs,machine learning （ML） was implemented in this research to predict the optimal combination of mesoporous-titanium dioxide （mp-TiO2） and weight percentage （wt%） of phenyl-C61-butyric acid methyl ester （PCBM）,along with the current density(Jsc),open-circuit voltage (Voc),fill factor （ff）,and energy conversion efficiency （ECE）.

关键词： machine learning nanopatterning PCBM perovskite solar cells prediction

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