Objective Structured Clinical Examinations (OSCE) improved communication skills of student of Pharmacology in Medicine and Podiatry degree.

Objective Structured Clinical Examinations (OSCE) improved communication skills of student of Pharmacology in Medicine and Podiatry degree. Bellido I, Blanco E, Gomez-Luque A. D. Pharmacology and Clinical Therapeutic. Medicine School. University of Malaga. IBIMA. Malaga, Spain. Objective Structured Clinical Examinations (OSCEs) are versatile multipurpose evaluative tools that can be utilized to assess health care professionals in a clinical setting including communication skills and ability to handle unpredictable patient behavior, which usually are not included in the traditional clinical exam. To designee and perform OSCEs by student is a novelty that really like to the students and may improve their arguing and planning capacities and their communication skills. Aim: To evaluate the impact of designing, developing and presenting Objective Structured Clinical Examinations (OSCE) by student in the communication skills development and in the learning of medicines in Medicine and Podiatry undergraduate students. Methods: A one-year study in which students were invited to voluntarily form groups (4 students maximum). Each group has to design and perform an OSCE (10 min maximum) showing a clinical situation/problem in which medicines’ use was needed. A clinical history, camera, a mobile-phone's video editor, photos, actors, dolls, simulators or whatever they may use was allowed. The job of each group was supervised and helped by a teacher. The students were invited to present their work to the rest of the class. After each OSCE performance the students were encouraged to ask questions if they wanted to do it. After all the OSCEs performances the students voluntarily answered a satisfaction survey. Results: Students of Pharmacology of Medicine degree and Podiatry degree, N=80, 53.75% female, 21±2.3 years old were enrolled. 26 OSCEs showing a clinical situation or clinical problem were made. The average time spent by students in making the OSCE was 21.5±9 h. The percentage of students which were satisfied with this way of presentation of the OSCE was 89.7%. Conclusion: Objective Structured Clinical Examinations (OSCE) designed and performed by student of Pharmacology of the Medicine and Podiatry Degree improved their communication skills.

A New Multi-Objective Approach for Molecular Docking Based on RMSD and Binding Energy

Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and RMSD (value lower than 2A for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.

Dynamic Multi-Objective Optimization With jMetal and Spark: a Case Study

Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum. In particular, we make use of the streaming facilities of Spark to feed an optimization problem with data from different sources. We demonstrate the use of our tool by solving a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on near real-time traffic data from New York City, which is updated several times per minute. Our experiment shows that both jMetal and Spark can be integrated providing a software platform to deal with dynamic multi-optimization problems.