Ajith Madhavan
Assistant Professor, School of Biotechnology, Amrita University
Development of a Phototrophic Microbial Fuel Cell with sacrificial electrodes and a novel proton exchange matrix
If micro organisms can solve Sudoku and possibly have feelings, who is to say that they cannot also solve the planet’s energy crisis? Mr. Madhavan employs micro organisms to produce energy using microbial fuel cell (MFC). Micro organisms go through a series of cycles and pathways in order to survive, including the Electron Transport Pathway (ETP) in which bacteria release electrons which can be tapped as energy. In a two-chambered MFC, micro organisms interact with an anode in one chamber and in the presence of an oxidizing agent in the cathodic chamber scavenges electrons from the cathode. The two chambers are connected by an external circuit and connected to a load. In between the two chambers is a proton exchange membrane (PEM) which transports protons from the second chamber to the first and acts as a barrier for electrons. Therefore, a renewable source of energy can be maintained by just providing your bacterial culture with the proper nutrients to thrive and remain happy and satisfied (assuming they have emotions).
Mr. Madhavan has done extensive work on such MFCs and has experimented with various micro organisms and substrates to achieve high energy production. The phototropic MFC Mr. Madhavan designed using Synechococcus elongates using waste water as a substrate was able to generate approximately 10 mȦ and 1 volt of electricity. Other research in this area has even shown that using human urine can be used as a substrate for certain bacteria to produce enough energy to charge a mobile phone.
Although this microbial technology seems to be the “next big thing” (despite their small size) when it comes to renewable energy sources there is still a lot of work to be done before these bacteria batteries hit the market. As of now the MFCs are still much less efficient than solar cells and the search for the perfect bacteria and substrate continues.
Manzoor K, Ph.D.
Professor, Centre for Nanoscience & Molecular Medicine, Amrita University
Targeting aberrant cancer kinome using rationally designed nano-polypharmaceutics
Manzoor Koyakutty, Archana Ratnakumary, Parwathy Chandran, Anusha Ashokan, and Shanti Nair
`War on Cancer’ was declared nearly 40 years ago. Since then, we made significant progress on fundamental understanding of cancer and developed novel therapeutics to deal with the most complex disease human race ever faced with. However, even today, cancer remains to be the unconquered `emperor of all maladies’. It is well accepted that meaningful progress in the fight against cancer is possible only with in-depth understanding on the molecular mechanisms that drives its swift and dynamic progression. During the last decade, emerging new technologies such as nanomedicine could offer refreshing life to the `war on cancer’ by way of providing novel methods for molecular diagnosis and therapy.
In the present talk, we discuss our approaches to target critically aberrant cancer kinases using rationally designed polymer-protein and protein-protein core-shell nanomedicines. We have used both genomic and proteomic approaches to identify many intimately cross-linked and complex aberrant protein kinases behind the drug resistance and uncontrolled proliferation of refractory leukemic cells derived from patients. Small molecule inhibitors targeted against oncogenic pathways in these cells were found ineffective due to the involvement of alternative survival pathways. This demands simultaneous inhibition more than one oncogenic kinases using poly-pharmaceutics approach. For this, we have rationally designed core-shell nanomedicines that can deliver several small molecules together for targeting multiple cancer signalling. We have also used combination of small molecules and siRNA for combined gene silencing together with protein kinase inhibition in refractory cancer cells. Optimized nanomedicines were successfully tested in patient samples and found enhanced cytotoxicity and molecular specificity in drug resistant cases.
Nano-polypharmaceutics represents a new generation of nanomedicines that can tackle multiple cancer mechanisms simultaneously. Considering the complexity of the disease, such therapeutic approaches are not simply an advantage, but indispensable.
Acknowledgements:
We thank Dept. of Biotechnology and Dept. Of Science and Technology,Govt. of India for the financial support through `Thematic unit of Excellence in Medical NanoBiotechnology’ and `Nanomedicine- RNAi programs’.
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.