Effects of Sevelamer Hydrochloride and Calcium Carbonate on Renal Osteodystrophy in Hemodialysis Patients

Disturbances in mineral metabolism play a central role in the development of renal bone disease. In a 54-wk, randomized, open-label study, 119 hemodialysis patients were enrolled to compare the effects of sevelamer hydrochloride and calcium carbonate on bone. Biopsy-proven adynamic bone disease was the most frequent bone abnormality at baseline (59%). Serum phosphorus, calcium, and intact parathyroid hormone were well controlled in both groups, although calcium was consistently lower and intact parathyroid hormone higher among patients who were randomly assigned to sevelamer. Compared with baseline values, there were no changes in mineralization lag time or measures of bone turnover (e.g., activation frequency) after 1 yr in either group. Osteoid thickness significantly increased in both groups, but there was no significant difference between them. Bone formation rate per bone surface, however, significantly increased from baseline only in the sevelamer group (P = 0.019). In addition, of those with abnormal microarchitecture at baseline (i.e., trabecular separation), seven of 10 in the sevelamer group normalized after 1 yr compared with zero of three in the calcium group. In summary, sevelamer resulted in no statistically significant changes in bone turnover or mineralization compared with calcium carbonate, but bone formation increased and trabecular architecture improved with sevelamer. Further studies are required to assess whether these changes affect clinical outcomes, such as rates of fracture.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.