Do future healthcare professionals advocate for pharmacogenomics? A study on medical and health sciences undergraduate students.

Pharmacogenomics (PGx) is a rapidly changing field of genomics in which healthcare professionals play an important role in its implementation in the clinical setting, however PGx level of adoption remains low. This study aims to investigate the attitude, self-confidence, level of knowledge, and their impact on health sciences undergraduate students' intentions to adopt PGx in clinical practice using a questionnaire developed based on the Theory of Planned Behavior (TPB). A model was proposed and a questionnaire was developed that was distributed to 467 undergraduate students of all academic years from four different departments of the University of Sharjah (UoS) including medical, dental, nursing, and pharmacy students from September 2022 to November 2022. Descriptive statistics along with factor analysis and regression analysis were conducted. The proposed model had a good internal consistency and fit. Attitude was the factor with the greatest impact on student's intentions followed by self-confidence and barriers. The level of knowledge had a meaningless impact. The majority of students shared a positive attitude and were aware of PGx benefits. Almost 60% of the respondents showed a high level of knowledge, while 50% of them were confident of implementing PGx in their clinical practice. Many students were prone to adopt PGx in their future careers. PGx testing cost and the lack of reimbursement were the most important barriers. Overall, students shared a positive intention and were prone to adopt PGx. In the future, it would be important to investigate the differences between gender, year of studies, and area of studies studies and their impact on students' intentions.

GRISSOM platform: enabling distributed processing and management of biological data through fusion of grid and web technologies.

Transcriptomic technologies have a critical impact in the revolutionary changes that reshape biological research. Through the recruitment of novel high-throughput instrumentation and advanced computational methodologies, an unprecedented wealth of quantitative data is produced. Microarray experiments are considered high-throughput, both in terms of data volumes (data intensive) and processing complexity (computationally intensive). In this paper, we present grids for in silico systems biology and medicine (GRISSOM), a web-based application that exploits GRID infrastructures for distributed data processing and management, of DNA microarrays (cDNA, Affymetrix, Illumina) through a generic, consistent, computational analysis framework. GRISSOM performs versatile annotation and integrative analysis tasks, through the use of third-party application programming interfaces, delivered as web services. In parallel, by conforming to service-oriented architectures, it can be encapsulated in other biomedical processing workflows, with the help of workflow enacting software, like Taverna Workbench, thus rendering access to its algorithms, transparent and generic. GRISSOM aims to set a generic paradigm of efficient metamining that promotes translational research in biomedicine, through the fusion of grid and semantic web computing technologies.

Enabling distributed processing and management of biological data using the grid and web technologies.

Transcriptomic technologies have dramatically reshaped modern biological research by deriving profiles of genome-wide expression of living organisms, and producing an unprecedented wealth of quantitative data. Given this characteristic, microarray experiments are considered high-throughput both in terms of data (data intensive) and processing (computationally intensive). GRISSOM Web Tools enable the exploitation of GRID resources for DNA microarray data distributed processing and management. It provides experts with a complete web-based solution for managing, searching and disseminating biological knowledge in the context of gene expression patterns on a genomic scale. Through the incorporation of novel workflows and various web services, the platform is gradually transformed to a powerful environment for knowledge discovery in the biomedical research.

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database.

BACKGROUND: The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes. RESULTS: A case study of the integration of six human metabolic pathways from KEGG depicts the ability of KEGGconverter to automatically produce merged and converted to SBML fully functional pathway models, enhanced with default kinetics. The suitability of the developed tool is demonstrated through a comparison with other state-of-the art relevant software tools for the same data fusion and conversion tasks, thus illustrating the problems and the relevant workflows. Moreover, KEGGconverter permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy. These additional reactions are introduced by exploiting relevant semantic information for the elements of the KEGG Pathways database. The architecture and functionalities of the web-based application are presented. CONCLUSION: KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. The tool is available at: http://www.grissom.gr/keggconverter.