Publications by authors named "M Cheriyan"

Extracellular matrix (ECM) viscoelasticity broadly regulates cell behavior. While hydrogels can approximate the viscoelasticity of native ECM, it remains challenging to recapitulate the rapid stress relaxation observed in many tissues without limiting the mechanical stability of the hydrogel. Here, we develop macroporous alginate hydrogels that have an order of magnitude increase in the rate of stress relaxation as compared to bulk hydrogels.

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Carnitine Palmitoyl Transferase Type II (CPT II) deficiency is a disorder of fatty acid beta oxidation that causes decreased adenosine triphosphate (ATP) and ketone production during periods of fasting or high energy requirements. Labor and delivery can precipitate attacks for parturients with this disorder, causing hypoglycemia, muscle weakness, rhabdomyolysis, and kidney failure. Anesthetic management considers the delivery mode and anesthetic medications available to reduce these risks.

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A 33-year-old gravidity three parity three (G3P3) woman at 34 weeks of pregnancy underwent fetal surgery to repair an open lumbosacral myelomeningocele at 22 weeks gestation and experienced preterm premature rupture of membranes as a result. She developed a saddle pulmonary embolus with signs of right heart strain while on prolonged bed rest. She was treated emergently with aspiration thrombectomy and suprarenal inferior vena cava (IVC) filter placement, followed by an uncomplicated cesarean delivery thereafter.

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Stem cell-derived kidney organoids contain nephron segments that recapitulate morphological and functional aspects of the human kidney. However, directed differentiation protocols for kidney organoids are largely conducted using biochemical signals to control differentiation. Here, the hypothesis that mechanical signals regulate nephrogenesis is investigated in 3D culture by encapsulating kidney organoids within viscoelastic alginate hydrogels with varying rates of stress relaxation.

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The development of automated segmentation approaches, which do not suffer from excessive computational burden and intra- and inter-observer variability, is the holy grail of multispectral MR image classification. A new segmentation approach to the data set of MR brain images using a combination of Independent Component Analysis (ICA) with a generalized version of the popular Gaussian Mixture Model (GMM) for unsupervised classification is proposed to be superior to conventional methods in this paper. We propose to optimize the parameters of the mixture model using a meta-heuristic approach like the Particle Swarm Optimization (PSO) to escape the problem of local traps (maxima or minima).

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