Le Klint (fowlspoon89)

Objective Although breastfeeding provides benefits for mothers and infants, multiple factors prevent women from breastfeeding. This article aims to explore the role of mothers' personal and contextual risk factors to breastfeeding rates with a special emphasis on understanding breastfeeding among African American mothers at 6 months postpartum. Design This secondary analysis was capitalizing on previously collected postpartum data from a longitudinal cohort study on the consequences of maternal childhood trauma on mother and infant outcomes. Postpartum mothers (n = 188) completed questionnaires on demographics, childhood trauma history, postpartum depression, social support, and breastfeeding status at 6 months postpartum. Results All risk factors (i.e., demographic and social support risk, childhood trauma history, and postpartum depression) were associated with lower breastfeeding at 6 months postpartum. When risk factors were examined in a single comprehensive model, only cumulative demographic risk emerged as significant. When partialing-out by race, being African American was the only variable associated with lower breastfeeding at 6 months postpartum. Conclusions Our study confirms that African American mothers report lower breastfeeding rates at 6 months postpartum than non-African American mothers. This association held even when controlling for demographic and social support risk, childhood trauma history, and postpartum depression. We discuss our findings from an intergenerational and historical trauma, racism, chronic discrimination perspective that considers the multifactorial nature of past and current impacts on breastfeeding among African American women in the United States.Objective To develop a decision support system (DSS) for the prediction of the postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL) to serve as a promising tool to provide counseling before an operation. Materials and Methods The overall procedure includes data collection and prediction model development. Pre-/postoperative variables of 100 patients with staghorn calculus, who underwent PCNL, were collected. For feature vector, variables and categories including patient history variables, kidney stone parameters, and laboratory data were considered. The prediction model was developed using machine learning techniques, which include dimensionality reduction and supervised classification. Multiple classifier scheme was used for prediction. The derived DSS was evaluated by running the leave-one-patient-out cross-validation approach on the data set. Results The system provided favorable accuracy (81%) in predicting the outcome of a treatment procedure. Performance in predicting the stone-free rate with the Minimum Redundancy Maximum Relevance feature (MRMR) treatment extracting top 3 features using Random Forest (RF) was 67%, with MRMR treatment extracting top 5 features using RF was 63%, and with MRMR treatment extracting top 10 features using Decision Tree was 62%. The statistical significance using standard error between the best area under the curves (AUCs) obtained from the Linear Discriminant Analysis (LDA) and MRMR. The results obtained from the LDA approach (0.81 AUC) was statistically significant (p = 0.027, z = 2.21) from the MRMR (0.64 AUC) (p = 0.05). Conclusion The promising results of the developed DSS could be used in assisting urologists to provide counseling, predict a surgical outcome, and ultimately choose an appropriate surgical treatment for removing kidney stones.Serine protease inhibitor b5 (SERPINB5) is a tumor suppressor gene that plays a critical role in various cellular processes. In gallbladder cancer (GBC), SERPINB5's aberrant expression is reported but its role in genetic predisposition is not known. We enrolled 270 cases and 296 controls and genotyped them for single nucleotide polymorphisms (SNPs) using direct DNA sequencing, followed by genotype-phenotype