BACKGROUND: Pregnancy is a special physiological condition, where drug treatment presents a special concern. AIMS: To evaluate the drug utilization pattern during pregnancy and to evaluate the effect of the educational and economic status on it.. DESIGN: The retrospective cross-sectional study. SETTING: The postgraduate Department of Pharmacology and Therapeutics of a medical college. and the antenatal clinic of the institution. MATERIALS AND METHODS: Medical students filled 405 questionnaires after interviewing pregnant women (243 primigravida and 152 multigravida). All the collected questionnaires were analysed for various study parameters. STATISTICAL ANALYSIS USED: Inter-group comparison was done using chi-square test. P value <0.05 was considered statistically significant. RESULTS: A total of 700, 1086 and 686 drugs, with an average of 1.73, 2.89 and 2.49 drugs per pregnant women, were used during first, second and third trimester of pregnancy, respectively. A majority of the drugs used, were from category-A, followed by category-B and category-D. However, category C and X drugs constituted 2.90 (20) and 5.71% (40) of drugs used during the third trimester and first trimester, respectively. Herbal/ homeopathic drugs constituted 6.42 (45), 3.68 (40) and 1.46% (10) of the drugs used in the first, second and third trimester of pregnancy, respectively (P=649). 33.33% (135) women believed that drug use during pregnancy is dangerous to both mother and child and 37.03% (150) believed that drugs are dangerous throughout pregnancy. 55.55% (225) females advocated the use of iron/folic acid during pregnancy. 24.69% (100) of women had knowledge about barrier contraceptives. Self-medication and homeopathic/ herbal drugs use was found more in graduates than in undergraduates; as well as, it was more in the higher socioeconomic group than the lower socioeconomic group. CONCLUSION: There is a need to educate and counsel women of child-bearing age, regarding the advantages and disadvantages of drug use during pregnancies, with special reference to alternative therapies and self-medication.

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(Wardha) for allowing us to do biochemical investigations in the biochemistry department. We also extend our sincere thanks to the staff for their full cooperation and technical assistance. The authors also wish to acknowledge the central drug store, M.G.I.M.S, Sevagram for providing chemicals.

P-glycoprotein (P-gp) is a 170 kDa membrane-bound protein, an energy-dependent efflux trans porter driven by ATP hydrolysis. It is responsible for multidrug resistance of many drugs. Physi ologically, it is involved in limiting the harmful exposure of toxins, drugs, and xenobiotics to the body by extruding them out of cells. It is increasingly recognized to play an important modulat ing role in the pharmacokinetic properties of many clinically important therapeutic agents and because of its importance in pharmacokinetics, its screening has to be incorporated into the drug discovery process. The modulation of drug transporters through inhibition or induction by various drugs or herbs can lead to significant drug-drug or drug-herb interactions by affecting various pharmacokinetic parameters of the drug. In addition, genetic polymorphism of P-gp has also been reported, which may affect drug disposition, produce variable drug effects, and may change disease risk susceptibility. As drug interactions and genetic polymorphism are important factors to be considered during drug development, P-gp may have an impact on drug develop ment in future.

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ABSTRACT.Purpose: To present clinical results regarding the treatment of patients with agerelated macular degeneration (neovascular form) after the implementation of a 'virtual' type of follow-up in a single retina service centre. Methods: Retrospective study based on the clinical records of the Leicester Royal Infirmary Retina department. Two periods were compared, the 2-year period of 2011-2012 and the following one of 2012-2013 when the 'virtual' clinics model applied in the department. Primary outcomes were as follows: the time between two appointments, follow-up or treatment and the number of patients with significant (>15 letters) improvement of their best corrected distance visual acuity. Secondary parameters of interest were as follows: mean number of injections per patient/year and the average duration of a 'virtual' vs. a regular visit. Results: The mean time interval between two appointments was 5.3 weeks following the implementation of the 'virtual' clinics compared to 6.9 weeks in the previous period of regular appointments. Mean visual acuity improvement >15 letters was achieved in 6.9% of the patients compared to 23.1% of the 'virtual' appointments period. The results regarding injections/patient/year were as follows: 5.6 before the model of 'virtual' appointments and 5.9 after the implementation. The average time a patient spent for a conventional visit was 71.4 AE 24.1 min, and the respective time needed in the virtual clinic was 47.3 AE 18.6 min. Conclusion: The model of 'virtual' (without actual consultation) follow-up appointments assisted our service to contend with the increased number of patient. In general, the specific pattern of patients' management could be widely considered obviously after comprehensive and all-embracing assessment of its safety and efficiency.

Identifying defects and classifying them according to some predefined classes is common in many manufacturing processes. The basis of such approach depends on a set of features extracted from all the classes and using them to train a classifier and then use it to determine the class to which the unseen data belongs to, with a reasonable accuracy. Hence the performance of the classifier depends on the features' ability to discriminate between the good or normal and the defects. Therefore, one way of improving the classifier is to select the most appropriate features from a given feature set for the purpose of training and testing so that, at the end, better results can be achieved overall. In this paper, a novel wrapper-based feature selection approach using Bees Algorithm for the application of wood defect classification is presented. Bees Algorithm is a swarm-based optimisation technique mimicking the foraging behaviour of honey bees found in nature. In order to demonstrate the wrapper-based feature selection procedure a Minimum Distance Classifier (MDC) is used in this study. However, the method can be applied to any application using some other classifier. The study shows that, on average, a 10% improvement is achieved when a reduced sub-set of 12 features selected using the proposed wrapper-based method with Bees Algorithm is used in training and testing the MDC when compared to using the original full set of 17 features. The rejected features correspond to outliers.

Tacrolimus has a good safety profile for long-term use in patients with BRC as a second-line agent enabling steroid sparing and visual function stabilisation or improvement.