Aug
13
Tue
2013
Plenary Talk: Biosensor and Single Cell Manipulation using Nanopipettes @ Amriteshwari Hall
Aug 13 @ 10:06 am – 10:49 am

NaderNader Pourmand, Ph.D.
Director, UCSC Genome Technology Center,University of California, Santa Cruz


Biosensor and Single Cell Manipulation using Nanopipettes

Approaching sub-cellular biological problems from an engineering perspective begs for the incorporation of electronic readouts. With their high sensitivity and low invasiveness, nanotechnology-based tools hold great promise for biochemical sensing and single-cell manipulation. During my talk I will discuss the incorporation of electrical measurements into nanopipette technology and present results showing the rapid and reversible response of these subcellular sensors  to different analytes such as antigens, ions and carbohydrates. In addition, I will present the development of a single-cell manipulation platform that uses a nanopipette in a scanning ion-conductive microscopy technique. We use this newly developed technology to position the nanopipette with nanoscale precision, and to inject and/or aspirate a minute amount of material to and from individual cells or organelle without comprising cell viability. Furthermore, if time permits, I will show our strategy for a new, single-cell DNA/ RNA sequencing technology that will potentially use nanopipette technology to analyze the minute amount of aspirated cellular material.

Invited Talk: Applying Machine learning for Automated Identification of Patient Cohorts @ Sathyam Hall
Aug 13 @ 2:40 pm – 3:05 pm

SriSairamSrisairam 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.

Aug
14
Wed
2013
Delegate Talk: Development of Supercritical Fluid Chromatography methods for the replacement of existing USP Normal phase liquid chromatography methods @ Amriteshwari Hall
Aug 14 @ 12:01 pm – 12:11 pm
Delegate Talk: Development of Supercritical Fluid Chromatography methods for the replacement of existing USP Normal phase liquid chromatography methods @ Amriteshwari Hall | Vallikavu | Kerala | India

Syed Salman Lateef and Vinayak A K


Development of Supercritical Fluid Chromatography methods for the replacement of existing USP Normal phase liquid chromatography methods

Normal phase liquid chromatography methods often have long run times and involve environmentally toxic/costly solvents. Supercritical chromatography methods on the other hand are faster, inexpensive, and eco-friendly. The low viscous supercritical carbon dioxide operates at high flow rates compared to LC without losing separation efficiency. In this work, SFC methods are developed to replace three United States Pharmacopeial (USP) normal phase achiral methods – prednisolone, tolazamide and cholecalciferol. System suitability parameters of the normal phase method are compared against the SFC method. Precision, linearity and robustness of the new SFC methods are demonstrated. SFC methods were found to be cost effective in terms of analysis time and solvent savings. The SFC method does not require purchase and disposal of expensive environmentally hazardous chemicals. Hence, the newly developed SFC method provides a faster and safer solution.