Relationship between Body Image, Self-Esteem and Academic Behavior of Adolescent Girls in Karachi, Pakistan

Objective: The objective of the study is to explore the relationship between body image, self-esteem and academic behavior amongst adolescent girls living in Karachi, Pakistan and simultaneously explore the mediating role of self-esteem in the relationship of the other two variables. Methods: It was a cross-sectional study design with a sample size of 400 adolescent girls belonging to a selected community of Karachi, Pakistan. Pre-validated tools, Body Shape Questionnaire, Rosenberg Self-Esteem Scale and Academic Behavior Scale were used for data collection of the study. The data was then analyzed using SPSS 17. Results: The results proved significant associations between all three variables with p<0.05. Moreover, a partially mediating role of self-esteem was noticed to be causing 74.8% variation in the relationship between body image and academic behavior. Conclusion: As per the study findings, body image, self-esteem and academic behavior of adolescent girls residing in Karachi, Pakistan are associated with each other. It was also concluded that higher body image dissatisfaction may lead to poor self-esteem which in turn negatively impacts the positive academic behavior of the girls. Hence, representing a mediating role of self-esteem in the relationship.  Overall, parents, educators, academic professionals, counselors and healthcare professionals can benefit from the study findings to positively play their parts in improving the lives of adolescents around them.       

Computer Aided Drug Design and Analysis of Protein Kinases and Other Related Targets

Current Ph.D. dissertation comprises of five chapters. A brief overview of every chapter is presented to provide an outline of the research contribution which is done in this thesis. Chapter 1 provide details regarding the general introduction of Computer-Aided Drug Discovery (CADD). In particular, it focuses on molecular modeling, Structure-based and Ligand-based drug design methods. Chapter 2 is related to the inhibitory studies of CK2 protein. In this study docking, 3DQSAR and MD simulation are reported with an emphasis on how each method is utilized to gain insight at the molecular level. Alignment obtained from the top-ranked conformation of inhibitors was used for developing the statically significant 3D-QSAR model. A further model was validated through the acceptable extrapolative ability to support both training and test set compounds. Structural changes were observed with the help of MD simulation produced by different substitution on inhibitors. Based on QSAR and MD results some new compounds were also designed. Chapter 3 deals with an effort to identify new pharmacological probes with high specificity for EPAC2 inhibition, using various modern computational tools. Initially, a comprehensive assessment of different scoring function and placement methods was conducted, and effective pharmacophore-based virtual screening protocol was set for the screening of EPAC2 inhibitors. The optimal model with the best six features brought forth and used as a 3D query for virtual screening to retrieve potential inhibitors from Maybridge, Cambridge, and NCI database. The screened compounds were subsequently subjected to molecular docking and 2D-QSAR studies. Finally, 22 top scored compounds with different scaffold having interactions with active site residues were predicted as a lead candidate who may become the starting point in the development of novel and potent EPAC2 inhibitor. Chapter 4 comprise of detailed work on Aurora Kinase B inhibitors. The inhibition of Aurora kinase B is necessary for the treatment of cancerous diseases. By utilizing integrated computational techniques, including 3D-QSAR modeling, pharmacophorebased virtual screening, and MD simulation, we proposed some novel compounds as potential Aurora kinase B inhibitors. Additionally, the highly significant 3D-QSAR model was developed using CoMFA and CoMSIA method. Moreover, the obtained best pharmacophore model was used for virtual screening against a database of over 30 million drugs like molecules which were randomly selected from large commercially available databases, i.e. Chembridge, National Cancer Institute database (NCI), Maybridge and ZINC database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMFA model. Visual inspection, docking calculations, and MD simulation revealed that novel leads established better binding affinities with Aurora kinase B. Chapter 5 deals with the antidiabetic potential of naturally occurring flavonoids. In this study, 9 selected flavonoids compounds were evaluated for their binding affinities with PKA via molecular docking study. According to in silico prediction, these compounds are involved in PKA dependent pathway which was further explored by in vitro mice islets.