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Articles by J Shin
Total Records ( 2 ) for J Shin
  H Komaba , S Nakanishi , A Fujimori , M Tanaka , J Shin , K Shibuya , M Nishioka , H Hasegawa , T Kurosawa and M. Fukagawa
 

Background and objectives: Cinacalcet is effective in reducing serum parathyroid hormone (PTH) in patients with secondary hyperparathyroidism. However, it has not been proven whether parathyroid gland size predicts response to therapy and whether cinacalcet is capable of inducing a reduction in parathyroid volume.

Design, setting, participants, & measurements: This 52-week, multicenter, open-label study enrolled hemodialysis patients with moderate to severe secondary hyperparathyroidism (intact PTH >300 pg/ml). Doses of cinacalcet were adjusted between 25 and 100 mg to achieve intact PTH <180 pg/ml. Ultrasonography was performed to measure the parathyroid gland size at baseline, week 26, and week 52. Findings were also compared with those of historical controls.

Results: Of the 81 subjects enrolled, 56 had parathyroid glands smaller than 500 mm3 (group S) and 25 had at least one enlarged gland larger than 500 mm3 (group L). Treatment with cinacalcet effectively decreased intact PTH by 55% from baseline in group S and by 58% in group L. A slightly greater proportion of patients in group S versus group L achieved an intact PTH <180 pg/ml (46 versus 32%) and a >30% reduction from baseline (88 versus 78%), but this was not statistically significant. Cinacalcet therapy also resulted in a significant reduction in parathyroid gland volume regardless of pretreatment size, which was in sharp contrast to historical controls (n = 87) where parathyroid gland volume progressively increased with traditional therapy alone.

Conclusions: Cinacalcet effectively decreases serum PTH levels and concomitantly reduces parathyroid gland volume, even in patients with marked parathyroid hyperplasia.

  I Lee , B Lehner , T Vavouri , J Shin , A. G Fraser and E. M. Marcotte
 

Most phenotypes are genetically complex, with contributions from mutations in many different genes. Mutations in more than one gene can combine synergistically to cause phenotypic change, and systematic studies in model organisms show that these genetic interactions are pervasive. However, in human association studies such nonadditive genetic interactions are very difficult to identify because of a lack of statistical power—simply put, the number of potential interactions is too vast. One approach to resolve this is to predict candidate modifier interactions between loci, and then to specifically test these for associations with the phenotype. Here, we describe a general method for predicting genetic interactions based on the use of integrated functional gene networks. We show that in both Saccharomyces cerevisiae and Caenorhabditis elegans a single high-coverage, high-quality functional network can successfully predict genetic modifiers for the majority of genes. For C. elegans we also describe the construction of a new, improved, and expanded functional network, WormNet 2. Using this network we demonstrate how it is possible to rapidly expand the number of modifier loci known for a gene, predicting and validating new genetic interactions for each of three signal transduction genes. We propose that this approach, termed network-guided modifier screening, provides a general strategy for predicting genetic interactions. This work thus suggests that a high-quality integrated human gene network will provide a powerful resource for modifier locus discovery in many different diseases.

 
 
 
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