C
Csaba P. Kovesdy
Researcher at University of Tennessee Health Science Center
Publications - 649
Citations - 41377
Csaba P. Kovesdy is an academic researcher from University of Tennessee Health Science Center. The author has contributed to research in topics: Kidney disease & Dialysis. The author has an hindex of 92, co-authored 605 publications receiving 31462 citations. Previous affiliations of Csaba P. Kovesdy include University of California, Irvine & Semmelweis University.
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Cannabinoids and the kidney: effects in health and disease.
TL;DR: It is clear that more research is necessary to clarify the various physiological and pathophysiological effects of cannabis and related analogs on the kidney, which will help limit the deleterious effects of these substances while promoting their potential beneficial impact on renal function in various types of kidney diseases.
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The Role of Fibroblast Growth Factor-23 in Cardiorenal Syndrome
Csaba P. Kovesdy,Leigh Quarles +1 more
TL;DR: Understanding of FGF23's pathophysiology and mechanisms of action responsible for its negative effects will be necessary to develop therapeutic strategies to treat CKD-MBD.
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Insights Into Nutritional and Inflammatory Aspects of Low Parathyroid Hormone in Dialysis Patients
TL;DR: It is suggested that over-interpretation of low serum PTH level as an indicator of low turnover bone disease in patients with chronic kidney disease is avoided, and this association with the MICS is reviewed.
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Novel targets and new potential: developments in the treatment of inflammation in chronic kidney disease.
TL;DR: There are currently no approved pharmacologic anti-inflammatory therapies in CKD but several agents are being studied in early clinical trials, while others could become viable alternatives in the future.
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Predictive Score for Posttransplantation Outcomes.
Miklos Z. Molnar,Danh V. Nguyen,Yanjun Chen,Vanessa A. Ravel,Elani Streja,Mahesh Krishnan,Csaba P. Kovesdy,Csaba P. Kovesdy,Rajnish Mehrotra,Kamyar Kalantar-Zadeh +9 more
TL;DR: A novel score to predict posttransplant outcomes using pretransplant information including routine laboratory data available before or at the time of transplantation, predicts relevant clinical outcomes and may perform better to predict patients' graft survival than currently used tools.