Introduction:Hypertensive disorders complicating pregnancy seriously endanger the safety of the mother and fetus during pregnancy. Very few studies have explored hypertensive disorders of pregnancy in India, even though this disease has been associated with adverse maternal and perinatal outcomes. This study aimed to analyze the disease pattern and risk factors associated with the disorder and assess the maternal and fetal outcomes in cases of hypertensive disorders of pregnancy.Subjects and methods:This case-control study was carried out over 1 year from 2011 to 2012 at the Department of Obstetrics and Gynecology, King George’s Medical University, Lucknow, Uttar Pradesh, India. A total of 149 patients were enrolled in the study. As seven were lost to follow-up, analysis was carried out on 142 cases. Patients were further classified according to the National High Blood Pressure Education Program Working Group (2000) as having mild preeclampsia (65 cases), severe preeclampsia (32 cases), or eclampsia (45 cases). Thirty-one healthy pregnant non-hypertensive women were enrolled into the study as controls.Results:The most common manifestation was edema, seen in 90% of cases. Proteinuria was also relatively common, 26.76% of patients with proteinuria of ≥300 mg/24 hours, 47.88% with proteinuria of ≥2 g/24 hours, and 25.35% with a urinary protein excretion of 3–5 g/24 hours. Central nervous system involvement was observed in 42.2% of cases, elevated bilirubin levels in 47.0%, visual symptoms in 6.4%, vaginal bleeding in 11.3%, and HELLP (hemolysis, elevated liver enzymes, and low platelet count) syndrome was reported in 2.80%. Maternal deaths occurred in 2.8% of cases, all of which were from the eclampsia group. Stillbirths occurred in 16.9% of cases, and overall neonatal death observed in 4.23% of cases.Conclusion:Women with hypertensive disorders of pregnancy were more prone to adverse maternal and fetal outcomes than normotensive pregnant women, but we observed a decreasing trend in the present study compared with that reported in other studies, which might be due to the increased number of hospital deliveries that occurred in our study.

BACKGROUND Current guidelines for prostate carcinoma screening rely primarily on the digital rectal examination (DRE) and prostate specific antigen (PSA). Well described patient risk factors for prostate carcinoma also include age, ethnicity, family history, and complexed PSA. However, due to the nonlinear relation of each of these variables with prostate carcinoma, it is difficult to predict reliably each patient's risk based on linear univariate analysis. The authors investigated a neural network to model the risk of prostate carcinoma by seven readily available clinical features. METHODS The database for the current study comprised 3268 men recently evaluated for the early detection of prostate carcinoma. The seven clinical features evaluated included age, race, family history, International Prostate Symptom Score (IPSS), DRE, and total and complexed PSA. Three hundred forty‐eight subjects in the dataset included men with determined prostate biopsy outcomes and for whom at least 6 of 7 features were available. The dataset was divided randomly into a training set (60%) and a test set (40%), with n1/n2 cross‐validation used to evaluate model accuracy, and was modeled with linear and quadratic discriminant function analysis and a neural computational system. After a model with acceptable goodness of fit was achieved, reverse regression analysis using Wilks's generalized likelihood ratio test was performed to evaluate the statistical significance of each input variable. RESULTS The receiving operating characteristic (ROC) area for the neural computational system in the test set was 0.825, whereas total PSA and complexed PSA alone had ROC areas of 0.678 and 0.697, respectively. The ROC area of logistic regression in the test set was 0.510, linear discriminant function analysis was 0.674, and quadratic discriminant function analysis was 0.011. All were significantly less than the ROC area of the neural computational model (all Ps < 0.002). Reverse regression based on Wilks's generalized likelihood ratio test demonstrated each input feature to be highly significant to the model (all Ps ≪ 0.000001). CONCLUSIONS The authors modeled a combination of well described patient risk factors for prostate carcinoma using a neural computational system with acceptable goodness of fit. They demonstrated that each of the seven variates on which the model was based was critically significant to model performance. The authors presented this model for clinical use and suggested that clinicians use it in deciding to perform prostate biopsy. Cancer 2003. © 2003 American Cancer Society.

Laparoscopic TEP performed by experienced surgeons does not alter testicular flow dynamics in early or late postoperative period.

Concomitant PNL and laparoscopic pyeloplasty are feasible and safe for patients with UPJ obstruction complicated by multiple calculi. We did not encounter any intraoperative difficulty during pyeloplasty following PNL.

No abstract