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2-Deoxyglucose Therapy for Organophosphate Intoxication
Project Summary The main goal of this project is to determine the therapeutic potential of glycolysis inhibition as an adjunct to midazolam therapy in mitigating the long-term neurological effects from acute organophosphate pesticide and nerve agent (OPNA) exposure. Novel countermeasures are desperately needed for effective mitigation of morbidity and long-term effects of OPNAs. A variety of agents targeting glutamate, GABA and oxidative stress have been proposed, but glycolysis inhibitors have not been widely studied in OPNA intoxication. Dysregulated glucose metabolism plays a key role in seizures and neuronal injury following OPNA exposure. 2-Deoxyglucose (2-DG), a selective glycolysis inhibitor, has anticonvulsant and neuroprotection effects and hence can effectively mitigate acute and long-term OPNA neurotoxicity. In this project, we seek to identify the glycolysis inhibition as novel adjunct neuroprotection to midazolam therapy for OPNA exposure, with the goal of identifying 2-DG or related drugs as medical countermeasures. The glycolytic pathway represents a logical target for such intervention because glycolysis controls seizures and neuronal injury by regulating glucose utilization and activity in neurons and astrocytes in the brain. The proposed therapy is based on the hypothesis that acute OPNA neurotoxicity imparts sustained activation of the glycolysis pathway in the brain and therefore, 2- DG and selective glycolysis inhibitors prevents long-term neuronal damage neurological dysfunction. This hypothesis will be tested by using the FDA-approved (2-DG) or clinical-stage glycolytic inhibitors in two distinct OPNA models in rats: (Aim 1) To investigate the protective efficacy of 2-DG and novel glycolysis inhibitors against DFP-induced acute and long-term neuronal damage and neurological dysfunction. (Aim 2) Aim 2 (Year 2). To determine brain penetration, pilot toxicity and pharmacokinetic of 2-DG or other lead drug in naïve and DFP-exposed animals. Test drugs will be evaluated as per the NIH rigor criteria in a dose-related design in male and female rats and behavior/neuropathology will be checked for 3 months post-exposure. 2-DG and test drugs will be given starting 40-min after exposure to ONAs. Three primary outcome measures will be addressed for therapy effectiveness: (i) acute adjunct neuroprotection; (ii) chronic neuroprotectant efficacy; and (iii) prevention of neurological and behavioral deficits. The primary measures of neuroprotection include longitudinal MRI scanning, and extent of neurodegeneration, neuroinflammation, aberrant neurogenesis, and mossy fiber sprouting. Key neurological outcomes include memory deficits, depression, anxiety behavior, and neurological/motor deficits. The outcome of this project will provide “proof-of-efficacy” of a novel glycolytic therapy with FDA-approvable, repurposed drugs with promising potential to limit long-term effects of OPNAs in humans. Thus, the overall impact of the outcome is enormous for civilians, especially in developing a highly effective and safe post-exposure medical countermeasure for chemical nerve agents.
Data-driven Artificial Social Intelligence: From Social Appropriateness to Fairness
Designing artificially intelligent systems and interfaces with socio-emotional skills is a challenging task. Progress in industry and developments in academia provide us a positive outlook, however, the artificial social and emotional intelligence of the current technology is still limited. My lab’s research has been pushing the state of the art in a wide spectrum of research topics in this area, including the design and creation of new datasets; novel feature representations and learning algorithms for sensing and understanding human nonverbal behaviours in solo, dyadic and group settings; designing longitudinal human-robot interaction studies for wellbeing; and investigating how to mitigate the bias that creeps into these systems. In this talk, I will present some of my research team’s explorations in these areas including social appropriateness of robot actions, virtual reality based cognitive training with affective adaptation, and bias and fairness in data-driven emotionally intelligent systems.
Machine Learning as a tool for positive impact : case studies from climate change
Climate change is one of our generation's greatest challenges, with increasingly severe consequences on global ecosystems and populations. Machine Learning has the potential to address many important challenges in climate change, from both mitigation (reducing its extent) and adaptation (preparing for unavoidable consequences) aspects. To present the extent of these opportunities, I will describe some of the projects that I am involved in, spanning from generative model to computer vision and natural language processing. There are many opportunities for fundamental innovation in this field, advancing the state-of-the-art in Machine Learning while ensuring that this fundamental progress translates into positive real-world impact.
On climate change, multi-agent systems and the behaviour of networked control
Multi-agent reinforcement learning (MARL) has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource (CPR) management. Crucial CPRs include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere, of which proper management is related to some of society’s greatest challenges such as food security, inequality and climate change. This talk will consist of three parts. In the first, we will briefly look at climate change and how it poses a significant threat to life on our planet. In the second, we will consider the potential of multi-agent systems for climate change mitigation and adaptation. And finally, in the third, we will discuss recent research from InstaDeep into better understanding the behaviour of networked MARL systems used for CPR management. More specifically, we will see how the tools from empirical game-theoretic analysis may be harnessed to analyse the differences in networked MARL systems. The results give new insights into the consequences associated with certain design choices and provide an additional dimension of comparison between systems beyond efficiency, robustness, scalability and mean control performance.
Mitigation of pathological tau abnormalities by the natural antioxidant uric acid: Comparison with DOT, a non-antibiotic oxytetracycline derivative
FENS Forum 2024
Mitigation of polyglutamine-induced toxicity through depletion of Trmt2a in an MJD/SCA3 mouse model
FENS Forum 2024
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