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CRISPR-Based Genome Engineering in Human Stem Cells
Published in Deepak A. Lamba, Patient-Specific Stem Cells, 2017
Thelma Garcia, Deepak A. Lamba
A study compared various corrections methods to repair gene defect in Duchenne muscular dystrophy (Li et al., 2015b). Duchenne muscular dystrophy is a severe form of muscle degenerative disease caused by a mutation in the dystrophin gene. The authors tried three correction methods including exon skipping, frameshifting, and exon knockin and reported that exon knockin works best. The group further confirmed that following differentiation, the generated skeletal muscles expressed full-length version of dystrophin protein. A recent publication reported modeling a number of kidney defects in three-dimensional (3D) culture systems following CRISPR-mediated gene knockouts (Freedman et al., 2015). The group reported that the CRISPR–Cas9 knockout of podocalyxin gene in the iPSCs caused junctional organization defects in podocyte-like cells in the 3D kidney cultures, while knocking out of the polycystic kidney disease genes PKD1 or PKD2 induced cyst formation from kidney tubules in these cultures. A new report recently looked at repairing a mutation associated with deafness (Chen et al., 2016). They generated iPSCs from members of a Chinese family carrying MYO15A c.4642G>A and c.8374G>A mutations. These iPSC lines upon differentiation generated hair cells with abnormal morphology. The authors then corrected the mutations in the patient iPSC lines using CRISPR, which resulted in the restoration of hair cell morphology and function in differentiated hair cells.
Paediatrics
Published in David A Lisle, Imaging for Students, 2012
Autosomal recessive polycystic kidney disease (ARPKD) refers to a spectrum of disorders with associated liver disease, with infantile and juvenile forms described. In the infantile form, renal disease tends to be more severe with less hepatic involvement; in older children, liver disease is the dominant feature. US in ARPKD shows symmetrically enlarged hyperechoic kidneys (Fig. 13.22).
Artificial intelligence optimized image segmentation techniques for renal cyst detection
Published in Journal of Medical Engineering & Technology, 2022
Bhawna Dhruv, Neetu Mittal, Megha Modi
Segmentation of the kidney is a challenging task due to incomplete volume issues, high signal to clamour rates and little gradient response. Renal cysts are both clinically and genetically heterogenous in nature. This may turn out to be life-threatening for the patient if not treated well in time. The kidney is an essential organ in the human body to maintain the harmonious functioning of the being. Its functions encompass excretory, hormonal, blood pressure maintenance, production of red blood cells, thyroid hormone regulation and many more. Renal cysts are a fairly known and studied pathology of the kidneys. Situated in the retroperitoneal plane, the kidneys can have a simple singular cyst or polycystic kidney disease which are small pockets of fluid formed on or within the kidneys. There are two types of kidney cysts i.e., simple cyst and Polycystic Kidney Disease (PCKD). Simple cysts generally are not of major concern however in the case of PCKD, it is an inherited condition that may result in serious conditions and damage the kidney as they keep growing on the kidney surface. 3 D CTs are generated to identify the number, position and several other parameters to judge the intensity of the disorder and conclude a diagnosis. However, these images may encompass several complications in the name of contrast or noise and therefore, the diagnosis of these cysts mandates a precise and flawless approach.