Call for Abstract
Scientific Program
1st Webinar on Physiotherapy, will be organized around the theme “Exploring Innovations and Knowledge of Physiotherapy”
Webinar on Physiotherapy is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in Webinar on Physiotherapy
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Gene therapy is hoped to cure or improve treatment of genetic disorders by replacing the mutated or malfunctioned gene, manipulating or turning off the gene causing the disease or stimulate other bodily functions to fight the disease. The most common method is replacement of a malfunctioned or sometimes a missed gene with a healthy one. However, gene therapy poses a risk of potentially serious complications, in the first place due to the method that is used to insert the "new" genes - the use of viruses. These have the ability to identify certain cells as well as to transmit the genetic material into the cells containing malfunctioned or missed gene. For that reason modified viruses are used as vectors or carriers of the healthy genes.
Healthcare and medical research are generating more and more complex data encompassing clinical investigations, genomic medical, imaging pharmacokinetics, Metabolomics, epidemiology and beyond. This "Data Science" Can form the basis for precision medicine, approaching disease prevention and treatment by talking into account individual variability in genes, environment, and lifestyle. By deeply profiling individual patients and using this to improve predictive models of pathology in individual patients, advances will be made in elucidating of the drivers of the disease and making precise targeted treatments, providing the right treatment to the right patient at the right time
This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve Physiotherapy care. In addition, to represent meaningful benefits to personalized decision-making, a comparison of current and future applications of clinical decision support to enable individualized medical treatment plans is presented. If clinical decision support tools are to impact outcomes in a clear and positive manner, their development and deployment must therefore consider the needs of the providers, including specific practice needs, information workflow, and practice environment.
Advances in human genome research are opening the door to a new paradigm for practising medicine that promises to transform healthcare. Physiotherapy, the use of marker-assisted diagnosis and targeted therapies derived from an individual's molecular profile, will impact the way drugs are developed and medicine is practiced. Knowledge of the molecular basis of disease will lead to novel target identification, toxic genomic markers to screen compounds and improved selection of clinical trial patients, which will fundamentally change the pharmaceutical industry. The traditional linear process of drug discovery and development will be replaced by an integrated and heuristic approach. In addition, patient care will be revolutionized through the use of novel molecular predisposition, screening, diagnostic, prognostic, pharmacogenomics and monitoring markets. Although numerous challenges will need to be met to make personalized medicine a reality, with time, this approach will replace the traditional trial-and-error practice of medicine.
Since the completion of the Human Genome project at the turn of the Century, there has been an unprecedented proliferation of genomic sequence data. A consequence of this is that the medical discoveries of the future will largely depend on our ability to process and analyse large genomic data sets,which continue to expand as the cost of sequencing decreases. Herein,we provide an overview of cloud computing and big data technologies, and discuss how such expertise can be used to deal with biology's big data sets. In particular, big data technologies such as the Apache Hadoop project, which provides distributed and parallelised data processing and analysis of petabyte (PB) scale data sets, will be discussed,together with an overview of the current usage of Hadoop within the bioinformatics community.