Use of Inverted Fluoroscope`s C-arm During Endoscopic Treatment of Urinary Tract Obstruction in Pregnancy: A Practicable Solution to Cut Radiation

OBJECTIVES To describe the use of pulsed fluoroscopic guidance, to perform endoscopic procedures in pregnant women, by inverting the fluoroscope`s c-arm using a lead thyroid collar to shield the fetus from the direct X-ray beam. The use of radiation during treatment of pregnant patients with urolithiasis remains a recurring dilemma. METHODS Between May 2006 and December 2008, endoscopic treatment due to ureteral stones was attempted in 8 pregnant women. In all cases, we use an inverted fluoroscope`s c-arm during endoscopic treatment associated with 2 lead neck thyroid collars to shield the uterus, protecting the fetus from direct radiation. Indication for treatment was symptomatic ureteral stones unresponsive to medical treatment in 7 and persistent fever in 1. RESULTS Mean ureteral stone size was 8.1 +/- 4.8 mm, located in the left ureter in 5 (62.5%) cases. Three (37.5%) patients had stone located in the upper ureter, 2 (25%) in the middle ureter, and 3 (37.5) in the distal ureter. In 6 cases, ureteral stones were treated using the semi-rigid ureteroscope, whereas in 1 case a flexible ureteroscope was needed. One woman was treated with insertion of a double-J stent due to associated urinary infection. No women has early delivery related to the endoscopic procedure, and all neonates were perfectly normal. CONCLUSIONS We present a technique for endoscopic procedures in pregnant women inverting the fluoroscope`s c-arm and protecting the fetus from the direct X-ray beam. This practical approach should be specially considered when no portable ultrasound and radiologic assistance in available in the operating room. UROLOGY 75: 1505-1508, 2010. (c) 2010 Published by Elsevier Inc.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.