Effects of Caffeic acid phenethyl ester (CAPE) and/or PD98059 (PD) on the gene expression of Caveolin-1 (CAV1) and reduced glutathione (GSH), malondialdehyde (MDA), copper-zinc superoxide dismutase (CuZn-SOD), and catalase (CAT) enzyme activities were investigated in an experimental chronic renal failure model in rats. Eighty Wistar rats were divided into eight groups for a 28-day study: Control, CsA (Cyclosporine A), CsA-V (CsA solvent), CsA + PD (CsA + PD98059), CsA + PD + CAPE, CsA + CAPE, CAPE-V (CAPE solvent), and PD-V (PD98059 solvent). Serum blood urea nitrogen and creatinine levels, as well as histopathological findings indicated the development of renal failure in the CsA group. Kidney GSH levels decreased while MDA levels, CuZn-SOD, and CAT activities increased significantly in the CsA group compared to control indicating oxidative stress. gene expression significantly decreased in the CsA group compared to the control. PD98059 and CAPE applications made positive improvements in the levels of the parameters investigated. PD98059 and CAPE applications in CsA given animals increased GSH and gene expressions and decreased CuZn-SOD and CAT levels compared to the CsA group. In conclusion, it was shown that PD98059 and CAPE could attenuate the effects of chronic renal failure, and CAV1 is suggested as a therapeutic target and the inhibition of the p44/42 MAPK pathway may be a new approach for the treatment of renal degenerations.
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http://dx.doi.org/10.1080/01480545.2021.2016043 | DOI Listing |
Front Endocrinol (Lausanne)
December 2024
Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Introduction: The Sequential Organ Failure Assessment (SOFA) score is a widely utilized clinical tool for evaluating the severity of organ failure in critically ill patients and assessing their condition and prognosis in the intensive care unit (ICU). Research has demonstrated that higher SOFA scores are associated with poorer outcomes in these patients. However, the predictive value of the SOFA score for acute kidney injury (AKI), a common complication of diabetic ketoacidosis (DKA), remains uncertain.
View Article and Find Full Text PDFBackground: Alport syndrome (AS) is a multifaceted condition that primarily affects the basement membranes of the kidneys, ears, and eyes. AS is considered the second most common cause of hereditary renal failure, exhibiting varied clinical manifestations across different lifespans. The aim of this study is to investigate the clinical features and genetic profile of AS and to elucidate the genotype-phenotype correlation of AS.
View Article and Find Full Text PDFExpert Opin Pharmacother
January 2025
School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
Introduction: Kidney failure is a life-limiting condition that profoundly impacts an individual's quality of life. The significant medication burden on patients required to manage the comorbidities and complications of kidney failure can have implications for patient-reported and clinical outcomes.
Methods: This work systematically reviewed methods used to assess medication regimen complexity amongst adults with kidney failure, the associated patient-reported and clinical outcomes, and the effectiveness of interventions to address regimen complexity.
Nephrology (Carlton)
January 2025
Jawaharlal Institute of Post Graduate Medical Education and Research, Puducherry, India.
Chronic kidney disease (CKD) prevalence varies widely across different regions of India. We aimed to identify the status of CKD in India, by systematically reviewing the published community-based studies between the period of January 2011 to December 2023. PubMed, Scopus, and EMBASE were searched for peer-reviewed evidence.
View Article and Find Full Text PDFNephrology (Carlton)
January 2025
Faculty of Medicine, Dentistry & Health Sciences Melbourne, The University of Melbourne, Melbourne, Victoria, Australia.
Chronic kidney disease is characterised by the progressive loss of kidney function. However, predicting who will progress to kidney failure is difficult. Artificial Intelligence, including Machine Learning, shows promise in this area.
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