Aug
12
Mon
2013
Invited Talk: Nanobioengineering of implant materials for improved cellular response and activity @ Sathyam Hall
Aug 12 @ 2:05 pm – 2:30 pm

deepthyDeepthy Menon, Ph.D.
Associate Professor, Centre for Nanosciences & Molecular Medicine, Health Sciences Campus, Amrita University, Kochi, India


Nanobioengineering of implant materials for improved cellular response and activity

Deepthy Menon, Divyarani V V, Chandini C Mohan, Manitha B Nair, Krishnaprasad C & Shantikumar V Nair

Abstract

Current trends in biomaterials research and development include the use of surfaces with topographical features at the nanoscale (dimensions < 100 nm), which influence biomolecular or cellular level reactions in vitro and in vivo. Progress in nanotechnology now makes it possible to precisely design and modulate the surface properties of materials used for various applications in medicine at the nanoscale. Nanoengineered surfaces, owing to their close resemblance with extracellular matrix, possess the unique capacity to directly affect protein adsorption that ultimately modulates the cellular adhesion and proliferation at the site of implantation. Taking advantage of this exceptional ability, we have nanoengineered metallic surfaces of Titanium (Ti) and its alloys (Nitinol -NiTi), as well as Stainless Steel (SS) by a simple hydrothermal method for generating non-periodic, homogeneous nanostructures. The bio- and hemocompatibility of these nanotextured metallic surfaces suggest their potential use for orthopedic, dental or vascular implants. The applicability of nanotextured Ti implants for orthopedic use was demonstrated in vivo in rat models, wherein early-stage bone formation at the tissue-implant interface without any fibrous tissue intervention was achieved. This nanoscale topography also was found to critically influence bacterial adhesion in vitro, with decreased adherence of staphylococcus aureus. The same surface nanotopography also was found to provide enhanced proliferation and functionality of vascular endothelial cells, suggesting its prospective use for developing an antithrombotic stent surface for coronary applications. Clinical SS & NiTi stents were also modified based on this strategy, which would offer a suitable solution to reduce the probability of late stent thrombosis associated with bare metallic stents. Thus, we demonstrate that nanotopography on implant surfaces has a critical influence on the fate of cells, which in turn dictates the long term success of the implant.

Acknowledgement: Authors gratefully acknowledge the financial support from Department of Biotechnology, Government of India through the Bioengineering program.

Deepthy

Aug
13
Tue
2013
Plenary Talk: Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law @ Sathyam Hall
Aug 13 @ 1:20 pm – 2:00 pm

karmeshuKarmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.


Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law

The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal  ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate  the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory  rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal  ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits  power law.