Effectiveness of structured teaching programme on knowledge of selected post-operative self care for primi mothers undergoing elective caesarean section in Institute of Obstetrics and Gynaecology, and Government Hospital for Women and Children, Chennai

INTRODUCTION: In Asia, postpartum care is considered to be a very importance stage of a woman’s life and this belief and practice is passed down through many generations. Experts refer postpartum periods as the first six weeks after childbirth. Primi mothers admitted at IOG expecting normal vaginal delivery but half of them undergoing caesarean section. They have not expected this operative procedure and were unable to perform self care practices during post operative period in regard to perineal care, breast care, newborn care, postnatal exercise, temporary contraceptive methods. So the investigator needs to teach postoperative self care during the antenatal period. So the study was conducted to assess the effectiveness of structured teaching programme on knowledge of selected post-operative self care for primi mothers undergoing elective caesarean section in Institute of Obstetrics and Gynaecology, and Govt. Hospital for Women and Children, Egmore, Chennai-8.” OBJECTIVE: 1. To assess the level of knowledge on post operative self care among primi mothers undergoing elective caesarean section. 2. To identify the effectiveness of structured teaching programme on selected post operative self care among primi mothers undergoing elective caesarean section. 3. To find the association between post test knowledge on post operative self care among primi mothers undergoing elective caesarean section with their selected demographic variables. MATERIALS AND METHODS: A Pre-experimental, one group Pretest, Posttest design was conducted. A total of 60 samples were selected by purposive sampling technique. Data were collected from the primi gravida mothers undergoing elective cesarean section using a semi - structured questionnaire before and after the implementation of the structured teaching program. The data were tabulated and analyzed by descriptive and inferential statistics. RESULTS: The study result shows, there was a significant difference between the pre-test and post-test level of knowledge regarding post operative self care from 12.58 to 23.47 after the administration of structured teaching programme. Considering overall knowledge score, in pretest primi gravida mothers are having 12.58 score where as in post test they are having 23.47, so the difference were 10.89.The difference between pre - test and post-test score is large and it is statistically significant. CONCLUSION: Hence, the study concluded the structured teaching programme was effective, appropriate and feasible. It helps the primi mother’s to practice self care after caesarean section themselves

The read-across hypothesis and environmental risk assessment of pharmaceuticals

This article is made available through the Brunel Open Access Publishing Fund. Copyright © 2013 American Chemical Society.Pharmaceuticals in the environment have received increased attention over the past decade, as they are ubiquitous in rivers and waterways. Concentrations are in sub-ng to low μg/L, well below acute toxic levels, but there are uncertainties regarding the effects of chronic exposures and there is a need to prioritise which pharmaceuticals may be of concern. The read-across hypothesis stipulates that a drug will have an effect in non-target organisms only if the molecular targets such as receptors and enzymes have been conserved, resulting in a (specific) pharmacological effect only if plasma concentrations are similar to human therapeutic concentrations. If this holds true for different classes of pharmaceuticals, it should be possible to predict the potential environmental impact from information obtained during the drug development process. This paper critically reviews the evidence for read-across, and finds that few studies include plasma concentrations and mode of action based effects. Thus, despite a large number of apparently relevant papers and a general acceptance of the hypothesis, there is an absence of documented evidence. There is a need for large-scale studies to generate robust data for testing the read-across hypothesis and developing predictive models, the only feasible approach to protecting the environment.BBSRC Industrial Partnership Award BB/ I00646X/1 and BBSRC Industrial CASE Partnership Studentship BB/I53257X/1 with AstraZeneca Safety Health and Environment Research Programme

Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

[Image: see text] Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon–oxygen bonds. Epoxides are metabolites often formed by cytochromes P450 acting on aromatic or double bonds. The specific location on a molecule that undergoes epoxidation is its site of epoxidation (SOE). Identifying a molecule’s SOE can aid in interpreting adverse events related to reactive metabolites and direct modification to prevent epoxidation for safer drugs. This study utilized a database of 702 epoxidation reactions to build a model that accurately predicted sites of epoxidation. The foundation for this model was an algorithm originally designed to model sites of cytochromes P450 metabolism (called XenoSite) that was recently applied to model the intrinsic reactivity of diverse molecules with glutathione. This modeling algorithm systematically and quantitatively summarizes the knowledge from hundreds of epoxidation reactions with a deep convolution network. This network makes predictions at both an atom and molecule level. The final epoxidation model constructed with this approach identified SOEs with 94.9% area under the curve (AUC) performance and separated epoxidized and non-epoxidized molecules with 79.3% AUC. Moreover, within epoxidized molecules, the model separated aromatic or double bond SOEs from all other aromatic or double bonds with AUCs of 92.5% and 95.1%, respectively. Finally, the model separated SOEs from sites of sp(2) hydroxylation with 83.2% AUC. Our model is the first of its kind and may be useful for the development of safer drugs. The epoxidation model is available at http://swami.wustl.edu/xenosite