Objective: To compare the efficacy and safety of isosorbide mononitrate (IMN) versus misoprostol used to induce labour for overdue pregnancy.
Setting: A prospective randomized clinical study conducted at AL-Elwiya Maternity Teaching Hospital in Baghdad from Jan. 2008 to Dec. 2008.
Method: One hundred and fifty women with over due pregnancy (past date and posterm pregnancy) referred for induction of labour with Bishop scores <_ 5 were randomly allocated to receive either forty mg isosorbide mononitrate (IMN) tablet as a single vaginal dose (n=75) or fifty mcg misoprostol vaginally (n=75) every six hrs for a maximum of three doses. Amniotomy and/or oxytocin infusion is considered when Bishop scores frankly progressed (augmentation) or used when no improvement achieved after 24 hour (induction). Adverse effects of medications, induction - delivery interval, mode of delivery and neonatal outcome were recorded and subjected to statistical analysis.
Results: Isosorbide mononitrate was associated with less adverse effects than misoprostol especially regarding uterine tachysystol (0 with isosorbide mononitrate vs 12% with misoprostol, P<0.01) and hyperstimulation (0 with isosorbide mononitrate vs 16% with misoprostol, p<0.01) but the induction - delivery interval with isosorbide mononitrate group was significantly longer compared with misoprostol (26.3±7.3hrs vs 15.4±5.4 hrs , p<0.01). Oxytocin was added to 70 women (93.3%) used isosorbide mononitrate while to 15 women (20%) used misoprostol (p<0.001). Caesarean rate was not significantly different between the two groups, but the indications were different, dystocia is the major cause (73.3%) with isosorbide mononitrate while persistent non-assuring fetal heart rate pattern (64%) in the misoprostol group.
Conclusion: Cervical ripening and induction of labour using isosorbide mononitrate resulted in fewer adverse effects but it was less effective than misoprostol.
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Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
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Microbial fuel cell is a device that uses the microorganism metabolism for the production of electricity under specific operating conditions. Double chamber microbial fuel cell was tested for the use of two cheap electrode materials copper and aluminum for the production of electricity under different operating conditions. The investigated conditions were concentration of microorganism (yeast) (0.5- 2 g/l), solutions temperature (33-45 oC) and concentration of glucose as a substrate (1.5- 6 g/l). The results demonstrated that copper electrode exhibit good performance while the performance of aluminum is poor. The electricity is generated with and without the addition of substrate. Addition of glucose substrate
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