Egidio D’Angelo, MD, Ph.D.
Full Professor of Physiology & Director, Brain Connectivity Center, University of Pavia, Italy
Realistic modeling: new insight into the functions of the cerebellar network
Realistic modeling is an approach based on the careful reconstruction of neurons synapses starting from biological details at the molecular and cellular level. This technique, combined with the connection topologies derived from histological measurements, allows the reconstruction of precise neuronal networks. Finally, the advent of specific software platforms (PYTHON-NEURON) and of super-computers allows large-scale network simulation to be performed in reasonable time. This approach inverts the logics of older theoretical models, which anticipated an intuition on how the network might work. In realistic modeling, network properties “emerge” from the numerous biological properties embedded into the model.
This approach is illustrated here through an outstanding application of realistic modeling to the cerebellar cortex network. The neurons (over 105) are reproduced at a high level of detail generating non-linear network effects like population oscillations and resonance, phase-reset, bursting, rebounds, short-term and long-term plasticity, spatiotemporal redistrbution of input patterns. The model is currently being used in the context of he HUMAN BRAIN PROJECT to investigate the cerebellar network function.
Correspondence should be addressed to
Dr. EgidioD’Angelo,
Laboratory of Neurophysiology
Via Forlanini 6, 27100 Pavia, Italy
Phone: 0039 (0) 382 987606
Fax: 0039 (0) 382 987527
dangelo@unipv.it
Acknowledgments
This work was supported by grants from European Union to ED (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to ED (RF-2009-1475845).
David Ibanez, Laura Dubreuil and Alejandro Rier
Neurofeedback (NF) is a type of biofeedback that uses real time display of electroencephalography to illustrate brain activity. EEG features are extracted and displayed allowing the user to, with practice, modulate their temporal evolution. Neurofeedback training has many therapeutic applications such as attention deficit hyperactivity disorder (ADHD), migraine, depression or conduct disorders. This document presents NeuroSurfer, a novel general-purpose tool for neurofeedback training with a use case of attention deficit hyperactivity disorder (ADHD) treatment.
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.
Nitish Sathyanrayanan, Sandesh Ganji and Holenarsipur Gundurao Nagendra.
Insilico Analysis of hypothetical proteins from Leishmania donovani: A Case study of a membrane protein of the MFS class reveals their plausible roles in drug resistance
Kala-azar or visceral leishmaniais (VL), caused by protozoan parasite Leishmania donovani, is one of the leading causes of morbidity and mortality in Bihar, India (Guerin et al. 2002; Mubayi et al. 2010). The disease is transmitted to the humans mainly by the vector, Phlebotmus argentipes, commonly known as Sand fly. The majority of VL (> 90%) occurs in only six countries: Bangladesh, India, Nepal, Sudan, Ethiopia and Brazil (Chappuis et al. 2007). In the Indian subcontinent, about 200 million people are estimated to be at risk of developing VL and this region harbors an estimated 67% of the global VL disease burden. The Bihar state only has captured almost 50% cases out of total cases in Indian sub-continent (Bhunia et al. 2013). ‘Conserved hypothetical’ proteins pose a challenge not just to functional genomics, but also to biology in general (Galperin and Koonin 2004). Leishmania donovani (strain BPK282A1) genome consists of a staggering ∼65% of hypothetical proteins. These uncharacterized proteins may enable better appreciation of signalling pathways, general metabolism, stress response and even drug resistance.
Rustom Mody, Ph.D.
Head R & D Lupin Ltd., Pune
Biosimilars are follow-on biologics also known as Similar Biologics – terms used to describe officially approved subsequent versions of innovator biopharmaceutical products made by rDNA technology when made by a different sponsor following patent expiry on the innovator product. These products are drawing global attention as a large number of block buster biopharmaceuticals have expired and many will soon seize to have patent protection in the coming years, opening the doors for the entry of biosimilars. However, the regulatory landscape is getting complex across the globe. The talk focuses on opportunities and challenges in the field of biosimilars and the future of biosimilar companies in India.
Aswath Balakrishnan, Kapaettu Satyamoorthy and Manjunath B Joshi
Introduction
Insulin resistance is a hall mark of metabolic disorders such as diabetes. Reduced insulin response in vasculature leads to disruption of IR/Akt/eNOS signaling pathway resulting in vasoconstriction and subsequently to cardiovascular diseases. Recent studies have demonstrated that inflammatory regulator interleukin-6 (IL-6), as one of the potential mediators that can link chronic inflammation with insulin resistance. Accumulating evidences suggest a significant role of epigenetic mechanisms such as DNA methylation in progression of metabolic disorders. Hence the present study aimed to understand the role of epigenetic mechanisms involved during IL-6 induced vascular insulin resistance and its consequences in cardiovascular diseases.
Materials and Methods
Human umbilical vein endothelial cells (HUVEC) and Human dermal microvascular endothelial cells (HDMEC) were used for this study. Endothelial cells were treated in presence or absence of IL-6 (20ng/ml) for 36 hours and followed by insulin (100nM) stimulation for 15 minutes. Using immunoblotting, cell lysates were stained for phosphor- and total Akt levels to measure insulin resistance. To investigate changes in DNA methylation, cells were treated with or without neutrophil conditioned medium (NCM) as a physiological source of inflammation or IL-6 (at various concentrations) for 36 hours. Genomic DNA was processed for HPLC analysis for methyl cytosine content and cell lysates were analyzed for DNMT1 (DNA (cytosine-5)-methyltransferase 1) and DNMT3A (DNA (cytosine-5)-methyltransferase 3A) levels using immunoblotting.
Results
Endothelial cells stimulated with insulin exhibited an increase in phosphorylation of Aktser 473 in serum free conditions but such insulin response was not observed in cells treated with IL-6, suggesting chronic exposure of endothelial cells to IL-6 leads to insulin resistance. HPLC analysis for global DNA methylation resulted in decreased levels of 5-methyl cytosine in cells treated with pro-inflammatory molecules (both by NCM and IL-6) as compared to untreated controls. Subsequently, analysis in cells treated with IL-6 showed a significant decrease in DNMT1 levels but not in DNMT3A. Other pro-inflammatory marker such as TNF-α did not exhibit such changes.
Conclusion
Our study suggests: a) Chronic treatment of endothelial cells with IL-6 results in insulin resistance b) Neutrophil conditioned medium and IL-6 decreases methyl cytosine levels c) DNMT1 but not DNMT3a levels are reduced in cells treated with IL-6.