Colin Barrow, Ph.D.
Chair in Biotechnology, School of Life & Environmental Sciences, Deakin University, Australia
Nano-biotechnology: Omega-3 Oils and Nanofibres
The health benefits of long-chain omega-3 fatty acids are well established, especially for eicosapentaenoic acid (EPA) and docosapentaenoic acid (DHA) from fish and microbial sources. In fact, a billion dollar market exists for these compounds as nutritional supplements, functional foods and pharmaceuticals. This presentation will describe some aspects of our omega-3 biotechnology research that are at the intersection of Nano-biotechnology and oil chemistry. These include the use of lipases for the concentration of omega-3 fats, through immobilization of these lipases on nanoparticles, and the microencapsulation and stabilization of omega-3 oils for functional foods. I will also describe some of our work on the enzymatic production of resolvins using lipoxygenases, and the fermentation of omega-3 oils from marine micro-organisms. Finally, I will describe some of our work on the formation of amyloid fibrils and graphene for various applications in nano-biotechnology.
Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA
Applying Machine learning for Automated Identification of Patient Cohorts
Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland
Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.
Arathy R and Binoy B Nair
PC based heart sound monitoring system
Heart diseases caused by disorders of the heart and blood vessels, are world’s largest killers. Early detection and monitoring of heart abnormalities is essential for diagnosis and effective treatment of heart diseases. Severalmethodologies are used for screening and diagnosing heart diseases. They are auscultation, electrocardiogram (ECG), echo-cardiogram, ultrasound etc. The effectiveness and applicability of all these diagnostic methods are highly dependent on the equipment cost and size as well as skill of the physician. This paper presents the design and development of a low cost portable wireless/tubeless digital stethoscope which can be used by the physician for monitoring the patient from a distance. The stethoscope system interfaces to a PC and is also capable of analyzing the heart sounds and identifying abnormalities in the heart sound and its classification. Storage of heart sound for later analysis is also possible.This advanced functionality increases the physician’s diagnostic capability, and such a PCG is not still available in most hospitals. Acoustic stethoscope can be changed into a digital stethoscope by inserting an electric capacity microphone into its diaphragm (Wang, Chen and Samjin, 2009).