Renal arterial branch calcification mimicking renal calculi in ultrasonography

Vijayalakshmidevi, B; Sarala, S; Lakshmi, AY; Sivaramakrishna, G.; Sivakumar, V

doi:10.4103/0971-4065.50681

Cited by 1 publication

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“…Physiologically, the kidney is subjected to various stress and oxidative impairment,[ 1 ] which further leads to various renal diseases leading to chronic kidney diseases. [ 2 3 ] However, permanent failures of both the kidneys need an organ transplant. It is a great challenge for the clinicians and patients to get a matched (human leukocyte antigen [HLA]) donor for kidney transplant.…”

Section: Introductionmentioning

confidence: 99%

Development of an experimental model of a decellularized kidney scaffold by perfusion mode and analyzing the three-dimensional extracellular matrix architecture by edge detection method

2018

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Renal transplant is treatment of choice for the patients with end stage renal disease. The kidney transplants are expensive and there are risks of immunological and infectious complications. We planned to develop an in vitro decellularized kidney scaffold model using sheep kidney. Kidney decellularization was carried out by perfusing chemical detergents such as sodium dodecyl sulfate (SDS), SDS and trypsin, and SDS and ethylenediaminetetraacetic acid solvent solution. Complete kidney was decellularized in 5 days by perfusing various chemical detergents in time-dependent intervals. Histological finding revealed the complete removal of cellular material in various regions of renal corpuscle, distal convoluted tubules, other cortex and medulla region. Details of interlobular veins and arteries were seen through naked eyes after trypan blue dye injection. We used edge detection technique for developing a three-dimensional (3-D) image (Image J software) for nephrological vasculature constructed of decellularized kidney scaffold specimen. This technique opens a gateway for the whole organ decellularization by perfusion technology and further imaging of its 3-D extracellular matrix texture by edge detection technique software.

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Section: Introductionmentioning

confidence: 99%