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  1. 2161

    Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease by Guoqing Zhang, Hongyan Luo, Xiaohua Lu, Yonghua Liu, Mei Wang, Bo Li, Haixia Lu, Yali Zheng

    Published 2025-01-01
    “…Also, we mapped the ROC curves.Meanwhile, in order to determine the underlying mechanism and relevant biological features of CKD, we conducted differential analysis between high and low risk subgroups in CKD. Moreover,we employed ssGSEA algorithm to evaluate the infiltration abundance of immune cells and calculated the correlation among the immune cells with the key genes. …”
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    Impact of Sex on Infection Risk in Patients with Systemic Lupus Erythematosus by R. Borrelli, S. Nicola, F. Corradi, I. Badiu, L. Lo Sardo, N. Rashidy, A. Quinternetto, M. Mazzola, F. Meli, E. Saracco, I. Vitali, S. Negrini, L. Brussino

    Published 2025-01-01
    “…Lastly, correlation analysis revealed a significant association between SS-A antibodies and infection rate (r = 0.291, <i>p</i> = 0.003). …”
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    CO2 field-flooding devices offer potential value for cardiopulmonary bypass procedures for CHD performed via a right-side small incision approach by Zhangke Guo, Zhimin Li, Song Bai, Feng Tong, Jia Zheng, Nan Ding, Xiaofeng Li

    Published 2025-01-01
    “…Lower 3-day postoperative CRP levels were also observed in the treatment group relative to control (5.77 ± 0.48 vs. 9.45 ± 0.98 mg/L, P < 0.001). The S100ß concentration in the relevant patient cohort increased significantly from the time just after induction, intubation, and installation of the right central venous line to the time of admission to CCU (71.61 ± 11.83 vs. 124.04 ± 38.80, P = 0.01) and at the time of 24 h after operation (71.61 ± 11.83 vs. 101.97 ± 30.31, P = 0.01). …”
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  14. 2174

    Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning by Zuhui Pu, Zuhui Pu, Tony Bowei Wang, Ying Lu, Ying Lu, Zijing Wu, Zijing Wu, Yuxian Chen, Ziqi Luo, Xinyu Wang, Lisha Mou, Lisha Mou

    Published 2025-01-01
    “…We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). Correlations between immune cells and key RMITRGs were investigated, and the interactions among these genes were explored through protein-protein interaction (PPI) network analysis. …”
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  15. 2175

    Dietary Se-enrich Cardamine violifolia supplementation decreases lipid deposition and improves antioxidant status in the liver of aging laying hens by Mengke Yan, Xin Cong, Hui Wang, Kun Qin, Yuhui Tang, Xiao Xu, Dan Wang, Shuiyuan Cheng, Yulan Liu, Huiling Zhu

    Published 2025-01-01
    “…A total of 450 sixty-five-wk-old Roman laying hens were randomly divided into 5 treatments: a basal diet (without Se supplementation, CON) and basal diets supplemented with 0.3 mg/kg Se from sodium selenite (SS), 0.3 mg/kg Se from Se-enriched yeast (SEY), 0.3 mg/kg Se from SEC (SEC), or 0.3 mg/kg Se from SEC and 0.3 mg/kg Se from SEY (SEC + SEY). …”
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    Doctrinal Approaches on determining the Content of Intellectual Property Right by O. V. Pikhurets, A. O. Pikhurets

    Published 2021-07-01
    “…Its exclusіvіty іs the fact thаt only the rіght holder mаy decіde to tаke аррroрrіаte аctіon on the results of іntellectuаl, creаtіve аctіvіtіes аnd objects equаted to them, whіle аll other рersons must obtаіn рermіssіon іn eаch cаse from the rіght holder to use the result of іntellectuаl, creаtіve аctіvіtіes аnd objects equаted to іt. …”
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    Modified Bloom's Taxonomy: Perspective of a Doctor of Philosophy Scholar by Wafa Omer

    Published 2025-01-01
    “… • Gul R, Kanwal S, Khan SS. Preferences of the teachers in employing revised Bloom's taxonomy in their instructions. sjesr. 2020 Jun 25;3(2):258-66…”
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  20. 2180

    Call for Papers: Demography Prize for Young Researches 2016/2017 by IF and FRFG

    Published 2016-12-01
    “…In a 2013 study published by the Bertelsmann Foundation, and led by Pieter Vanhuysse of the UN’s European Centre for Social Welfare Policy and Research, a total of 29 OECD states were compared on the basis of four indicators: public debt per child; the ecological footprint created by all generations currently alive; the ratio of child- to elderly-poverty; and the distribution of social spending among generations (“elderly-bias indicator of social spending”, EBiSS). These measures were then aggregated into the “Intergenerational Justice Index” – the first of its kind. …”
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