Project acronym 3D-OA-HISTO
Project Development of 3D Histopathological Grading of Osteoarthritis
Researcher (PI) Simo Jaakko Saarakkala
Host Institution (HI) OULUN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2013-StG
Summary "Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Summary
"Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Max ERC Funding
1 500 000 €
Duration
Start date: 2014-02-01, End date: 2019-01-31
Project acronym A-DIET
Project Metabolomics based biomarkers of dietary intake- new tools for nutrition research
Researcher (PI) Lorraine Brennan
Host Institution (HI) UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Call Details Consolidator Grant (CoG), LS7, ERC-2014-CoG
Summary In todays advanced technological world, we can track the exact movement of individuals, analyse their genetic makeup and predict predisposition to certain diseases. However, we are unable to accurately assess an individual’s dietary intake. This is without a doubt one of the main stumbling blocks in assessing the link between diet and disease/health. The present proposal (A-DIET) will address this issue with the overarching objective to develop novel strategies for assessment of dietary intake.
Using approaches to (1) identify biomarkers of specific foods (2) classify people into dietary patterns (nutritypes) and (3) develop a tool for integration of dietary and biomarker data, A-DIET has the potential to dramatically enhance our ability to accurately assess dietary intake. The ultimate output from A-DIET will be a dietary assessment tool which can be used to obtain an accurate assessment of dietary intake by combining dietary and biomarker data which in turn will allow investigations into relationships between diet, health and disease. New biomarkers of specific foods will be identified and validated using intervention studies and metabolomic analyses. Methods will be developed to classify individuals into dietary patterns based on biomarker/metabolomic profiles thus demonstrating the novel concept of nutritypes. Strategies for integration of dietary and biomarker data will be developed and translated into a tool that will be made available to the wider scientific community.
Advances made in A-DIET will enable nutrition epidemiologist’s to properly examine the relationship between diet and disease and develop clear public health messages with regard to diet and health. Additionally results from A-DIET will allow researchers to accurately assess people’s diet and implement health promotion strategies and enable dieticians in a clinical environment to assess compliance to therapeutic diets such as adherence to a high fibre diet or a gluten free diet.
Summary
In todays advanced technological world, we can track the exact movement of individuals, analyse their genetic makeup and predict predisposition to certain diseases. However, we are unable to accurately assess an individual’s dietary intake. This is without a doubt one of the main stumbling blocks in assessing the link between diet and disease/health. The present proposal (A-DIET) will address this issue with the overarching objective to develop novel strategies for assessment of dietary intake.
Using approaches to (1) identify biomarkers of specific foods (2) classify people into dietary patterns (nutritypes) and (3) develop a tool for integration of dietary and biomarker data, A-DIET has the potential to dramatically enhance our ability to accurately assess dietary intake. The ultimate output from A-DIET will be a dietary assessment tool which can be used to obtain an accurate assessment of dietary intake by combining dietary and biomarker data which in turn will allow investigations into relationships between diet, health and disease. New biomarkers of specific foods will be identified and validated using intervention studies and metabolomic analyses. Methods will be developed to classify individuals into dietary patterns based on biomarker/metabolomic profiles thus demonstrating the novel concept of nutritypes. Strategies for integration of dietary and biomarker data will be developed and translated into a tool that will be made available to the wider scientific community.
Advances made in A-DIET will enable nutrition epidemiologist’s to properly examine the relationship between diet and disease and develop clear public health messages with regard to diet and health. Additionally results from A-DIET will allow researchers to accurately assess people’s diet and implement health promotion strategies and enable dieticians in a clinical environment to assess compliance to therapeutic diets such as adherence to a high fibre diet or a gluten free diet.
Max ERC Funding
1 995 548 €
Duration
Start date: 2015-08-01, End date: 2020-07-31
Project acronym ABC
Project Targeting Multidrug Resistant Cancer
Researcher (PI) Gergely Szakacs
Host Institution (HI) MAGYAR TUDOMANYOS AKADEMIA TERMESZETTUDOMANYI KUTATOKOZPONT
Call Details Starting Grant (StG), LS7, ERC-2010-StG_20091118
Summary Despite considerable advances in drug discovery, resistance to anticancer chemotherapy confounds the effective treatment of patients. Cancer cells can acquire broad cross-resistance to mechanistically and structurally unrelated drugs. P-glycoprotein (Pgp) actively extrudes many types of drugs from cancer cells, thereby conferring resistance to those agents. The central tenet of my work is that Pgp, a universally accepted biomarker of drug resistance, should in addition be considered as a molecular target of multidrug-resistant (MDR) cancer cells. Successful targeting of MDR cells would reduce the tumor burden and would also enable the elimination of ABC transporter-overexpressing cancer stem cells that are responsible for the replenishment of tumors. The proposed project is based on the following observations:
- First, by using a pharmacogenomic approach, I have revealed the hidden vulnerability of MDRcells (Szakács et al. 2004, Cancer Cell 6, 129-37);
- Second, I have identified a series of MDR-selective compounds with increased toxicity toPgp-expressing cells
(Turk et al.,Cancer Res, 2009. 69(21));
- Third, I have shown that MDR-selective compounds can be used to prevent theemergence of MDR (Ludwig, Szakács et al. 2006, Cancer Res 66, 4808-15);
- Fourth, we have generated initial pharmacophore models for cytotoxicity and MDR-selectivity (Hall et al. 2009, J Med Chem 52, 3191-3204).
I propose a comprehensive series of studies that will address thefollowing critical questions:
- First, what is the scope of MDR-selective compounds?
- Second, what is their mechanism of action?
- Third, what is the optimal therapeutic modality?
Extensive biological, pharmacological and bioinformatic analyses will be utilized to address four major specific aims. These aims address basic questions concerning the physiology of MDR ABC transporters in determining the mechanism of action of MDR-selective compounds, setting the stage for a fresh therapeutic approach that may eventually translate into improved patient care.
Summary
Despite considerable advances in drug discovery, resistance to anticancer chemotherapy confounds the effective treatment of patients. Cancer cells can acquire broad cross-resistance to mechanistically and structurally unrelated drugs. P-glycoprotein (Pgp) actively extrudes many types of drugs from cancer cells, thereby conferring resistance to those agents. The central tenet of my work is that Pgp, a universally accepted biomarker of drug resistance, should in addition be considered as a molecular target of multidrug-resistant (MDR) cancer cells. Successful targeting of MDR cells would reduce the tumor burden and would also enable the elimination of ABC transporter-overexpressing cancer stem cells that are responsible for the replenishment of tumors. The proposed project is based on the following observations:
- First, by using a pharmacogenomic approach, I have revealed the hidden vulnerability of MDRcells (Szakács et al. 2004, Cancer Cell 6, 129-37);
- Second, I have identified a series of MDR-selective compounds with increased toxicity toPgp-expressing cells
(Turk et al.,Cancer Res, 2009. 69(21));
- Third, I have shown that MDR-selective compounds can be used to prevent theemergence of MDR (Ludwig, Szakács et al. 2006, Cancer Res 66, 4808-15);
- Fourth, we have generated initial pharmacophore models for cytotoxicity and MDR-selectivity (Hall et al. 2009, J Med Chem 52, 3191-3204).
I propose a comprehensive series of studies that will address thefollowing critical questions:
- First, what is the scope of MDR-selective compounds?
- Second, what is their mechanism of action?
- Third, what is the optimal therapeutic modality?
Extensive biological, pharmacological and bioinformatic analyses will be utilized to address four major specific aims. These aims address basic questions concerning the physiology of MDR ABC transporters in determining the mechanism of action of MDR-selective compounds, setting the stage for a fresh therapeutic approach that may eventually translate into improved patient care.
Max ERC Funding
1 499 640 €
Duration
Start date: 2012-01-01, End date: 2016-12-31
Project acronym ABRSEIST
Project Antibiotic Resistance: Socio-Economic Determinants and the Role of Information and Salience in Treatment Choice
Researcher (PI) Hannes ULLRICH
Host Institution (HI) DEUTSCHES INSTITUT FUR WIRTSCHAFTSFORSCHUNG DIW (INSTITUT FUR KONJUNKTURFORSCHUNG) EV
Call Details Starting Grant (StG), SH1, ERC-2018-STG
Summary Antibiotics have contributed to a tremendous increase in human well-being, saving many millions of lives. However, antibiotics become obsolete the more they are used as selection pressure promotes the development of resistant bacteria. The World Health Organization has proclaimed antibiotic resistance as a major global threat to public health. Today, 700,000 deaths per year are due to untreatable infections. To win the battle against antibiotic resistance, new policies affecting the supply and demand of existing and new drugs must be designed. I propose new research to identify and evaluate feasible and effective demand-side policy interventions targeting the relevant decision makers: physicians and patients. ABRSEIST will make use of a broad econometric toolset to identify mechanisms linking antibiotic resistance and consumption exploiting a unique combination of physician-patient-level antibiotic resistance, treatment, and socio-economic data. Using machine learning methods adapted for causal inference, theory-driven structural econometric analysis, and randomization in the field it will provide rigorous evidence on effective intervention designs. This research will improve our understanding of how prescribing, resistance, and the effect of antibiotic use on resistance, are distributed in the general population which has important implications for the design of targeted interventions. It will then estimate a structural model of general practitioners’ acquisition and use of information under uncertainty about resistance in prescription choice, allowing counterfactual analysis of information-improving policies such as mandatory diagnostic testing. The large-scale and structural econometric analyses allow flexible identification of physician heterogeneity, which ABRSEIST will exploit to design and evaluate targeted, randomized information nudges in the field. The result will be improved rational use and a toolset applicable in contexts of antibiotic prescribing.
Summary
Antibiotics have contributed to a tremendous increase in human well-being, saving many millions of lives. However, antibiotics become obsolete the more they are used as selection pressure promotes the development of resistant bacteria. The World Health Organization has proclaimed antibiotic resistance as a major global threat to public health. Today, 700,000 deaths per year are due to untreatable infections. To win the battle against antibiotic resistance, new policies affecting the supply and demand of existing and new drugs must be designed. I propose new research to identify and evaluate feasible and effective demand-side policy interventions targeting the relevant decision makers: physicians and patients. ABRSEIST will make use of a broad econometric toolset to identify mechanisms linking antibiotic resistance and consumption exploiting a unique combination of physician-patient-level antibiotic resistance, treatment, and socio-economic data. Using machine learning methods adapted for causal inference, theory-driven structural econometric analysis, and randomization in the field it will provide rigorous evidence on effective intervention designs. This research will improve our understanding of how prescribing, resistance, and the effect of antibiotic use on resistance, are distributed in the general population which has important implications for the design of targeted interventions. It will then estimate a structural model of general practitioners’ acquisition and use of information under uncertainty about resistance in prescription choice, allowing counterfactual analysis of information-improving policies such as mandatory diagnostic testing. The large-scale and structural econometric analyses allow flexible identification of physician heterogeneity, which ABRSEIST will exploit to design and evaluate targeted, randomized information nudges in the field. The result will be improved rational use and a toolset applicable in contexts of antibiotic prescribing.
Max ERC Funding
1 498 920 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
Project acronym ARMOR-T
Project Armoring multifunctional T cells for cancer therapy
Researcher (PI) Sebastian Kobold
Host Institution (HI) LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN
Call Details Starting Grant (StG), LS7, ERC-2017-STG
Summary Adoptive T cell therapy (ACT) is a powerful approach to treat even advanced cancer diseases where poor prognosis calls for innovative treatments. However ACT is critically limited by insufficient T cell infiltration into the tumor, T cell activation at the tumor site and local T cell suppression. Few advances have been made in the field to tackle these limitations besides increasing T cell activation. My group has focussed on these unaddressed issues but came to realise that tackling these one by one will not be sufficient. I have developed a panel of unpublished chemokine receptors and innovative modular antibody-activated receptors which have the potential to overcome the limitations of ACT against solid tumors. This ground-breaking portfolio places my group in the unique position to address combination of synergistic receptors and enable cellular therapies in previously unsuccessful indications. My project will provide the rationale for provision of an effective cancer treatment. The goal is to develop the next generation of ACT through T cell engineering both by forced expression of migratory and activating receptors and simultaneous deletion of immune suppressive molecules by gene editing. ARMOR-T will provide the basis for further preclinical and clinical development of a pioneering cellular product devoid of the limitations of available products to date. I will prove 1) synergy between migratory and modular activating receptors, 2) feasibility to integrate gene editing into a T cell expansion protocol, 3) synergy between gene editing, migratory and modular receptors and 4) efficacy, safety and mode of action. The main work of the project will be carried out in models of pancreatic cancer. The ARMOR-T platform will subsequently be translated to other cancer entities where response to ACT is likely such as melanoma, breast or colon cancer, providing less toxic and more effective therapies to otherwise untreatable disease.
Summary
Adoptive T cell therapy (ACT) is a powerful approach to treat even advanced cancer diseases where poor prognosis calls for innovative treatments. However ACT is critically limited by insufficient T cell infiltration into the tumor, T cell activation at the tumor site and local T cell suppression. Few advances have been made in the field to tackle these limitations besides increasing T cell activation. My group has focussed on these unaddressed issues but came to realise that tackling these one by one will not be sufficient. I have developed a panel of unpublished chemokine receptors and innovative modular antibody-activated receptors which have the potential to overcome the limitations of ACT against solid tumors. This ground-breaking portfolio places my group in the unique position to address combination of synergistic receptors and enable cellular therapies in previously unsuccessful indications. My project will provide the rationale for provision of an effective cancer treatment. The goal is to develop the next generation of ACT through T cell engineering both by forced expression of migratory and activating receptors and simultaneous deletion of immune suppressive molecules by gene editing. ARMOR-T will provide the basis for further preclinical and clinical development of a pioneering cellular product devoid of the limitations of available products to date. I will prove 1) synergy between migratory and modular activating receptors, 2) feasibility to integrate gene editing into a T cell expansion protocol, 3) synergy between gene editing, migratory and modular receptors and 4) efficacy, safety and mode of action. The main work of the project will be carried out in models of pancreatic cancer. The ARMOR-T platform will subsequently be translated to other cancer entities where response to ACT is likely such as melanoma, breast or colon cancer, providing less toxic and more effective therapies to otherwise untreatable disease.
Max ERC Funding
1 636 710 €
Duration
Start date: 2018-03-01, End date: 2023-02-28
Project acronym BEHAVIORAL THEORY
Project Behavioral Theory and Economic Applications
Researcher (PI) Botond Koszegi
Host Institution (HI) KOZEP-EUROPAI EGYETEM
Call Details Starting Grant (StG), SH1, ERC-2012-StG_20111124
Summary "This proposal outlines projects to develop robust and portable theories studying the impact of psychological phenomena in economic settings. The proposed work falls in three broad research agendas.
My first main agenda is to formally model and economically apply a simple observation: that when people make decisions, they do not focus equally on all attributes of their available options, and overweight the attributes they focus on. I will build a set of portable models of focusing in attribute-based choice and risky choice based on the idea that a person focuses more on attributes in which her options differ more. I will also use the framework to develop novel, focus-based, theories of intertemporal choice and social preferences, as well as analyze the implications of focusing for product design, principal-agent relationships, and other economic questions.
My second main agenda is to explore some implications for market outcomes, welfare, and policy of the possibility that consumers misperceive certain aspects of products. I will investigate the circumstances that facilitate the profitable deception of consumers; firms' incentives for ""innovating"" deceptive products, including novel financial products aimed at exploiting investors; how firms' ability to distinguish naive and sophisticated consumers affects the consequences of deception; whether learning on the part of consumers will help them to avoid making mistakes; and how regulators and other observers can detect consumer mistakes from market data.
Two further projects apply the model of reference-dependent utility I have developed in earlier work to understand the pricing and advertising behavior of firms. I will also aim to disseminate some of my work, along with other cutting-edge research in psychology and economics, in a Journal of Economic Literature survey on ""Behavioral Contract Theory."""
Summary
"This proposal outlines projects to develop robust and portable theories studying the impact of psychological phenomena in economic settings. The proposed work falls in three broad research agendas.
My first main agenda is to formally model and economically apply a simple observation: that when people make decisions, they do not focus equally on all attributes of their available options, and overweight the attributes they focus on. I will build a set of portable models of focusing in attribute-based choice and risky choice based on the idea that a person focuses more on attributes in which her options differ more. I will also use the framework to develop novel, focus-based, theories of intertemporal choice and social preferences, as well as analyze the implications of focusing for product design, principal-agent relationships, and other economic questions.
My second main agenda is to explore some implications for market outcomes, welfare, and policy of the possibility that consumers misperceive certain aspects of products. I will investigate the circumstances that facilitate the profitable deception of consumers; firms' incentives for ""innovating"" deceptive products, including novel financial products aimed at exploiting investors; how firms' ability to distinguish naive and sophisticated consumers affects the consequences of deception; whether learning on the part of consumers will help them to avoid making mistakes; and how regulators and other observers can detect consumer mistakes from market data.
Two further projects apply the model of reference-dependent utility I have developed in earlier work to understand the pricing and advertising behavior of firms. I will also aim to disseminate some of my work, along with other cutting-edge research in psychology and economics, in a Journal of Economic Literature survey on ""Behavioral Contract Theory."""
Max ERC Funding
1 275 448 €
Duration
Start date: 2012-11-01, End date: 2018-10-31
Project acronym BIO-IRT
Project Biologically individualized, model-based radiotherapy on the basis of multi-parametric molecular tumour profiling
Researcher (PI) Daniela Thorwarth
Host Institution (HI) EBERHARD KARLS UNIVERSITAET TUEBINGEN
Call Details Starting Grant (StG), LS7, ERC-2013-StG
Summary High precision radiotherapy (RT) allows extremely flexible tumour treatments achieving highly conformal radiation doses while sparing surrounding organs at risk. Nevertheless, failure rates of up to 50% are reported for head and neck cancer (HNC) due to radiation resistance induced by pathophysiologic factors such as hypoxia and other clinical factors as HPV-status, stage and tumour volume.
This project aims at developing a multi-parametric model for individualized RT (iRT) dose prescriptions in HNC based on biological markers and functional PET/MR imaging. This project goes far beyond current research standards and clinical practice as it aims for establishing hypoxia PET and f-MRI as well as biological markers in HNC as a role model for a novel concept from anatomy-based to biologically iRT.
During this project, a multi-parametric model will be developed on a preclinical basis that combines biological markers such as different oncogenes and hypoxia gene classifier with functional PET/MR imaging, such as FMISO PET in combination with different f-MRI techniques, like DW-, DCE- and BOLD-MRI in addition to MR spectroscopy. The ultimate goal of this project is a multi-parametric model to predict therapy outcome and guide iRT.
In a second part, a clinical study will be carried out to validate the preclinical model in patients. Based on the most informative radiobiological and imaging parameters as identified during the pre-clinical phase, biological markers and advanced PET/MR imaging will be evaluated in terms of their potential for iRT dose prescription.
Successful development of a model for biologically iRT prescription on the basis of multi-parametric molecular profiling would provide a unique basis for personalized cancer treatment. A validated multi-parametric model for RT outcome would represent a paradigm shift from anatomy-based to biologically iRT concepts with the ultimate goal of improving cancer cure rates.
Summary
High precision radiotherapy (RT) allows extremely flexible tumour treatments achieving highly conformal radiation doses while sparing surrounding organs at risk. Nevertheless, failure rates of up to 50% are reported for head and neck cancer (HNC) due to radiation resistance induced by pathophysiologic factors such as hypoxia and other clinical factors as HPV-status, stage and tumour volume.
This project aims at developing a multi-parametric model for individualized RT (iRT) dose prescriptions in HNC based on biological markers and functional PET/MR imaging. This project goes far beyond current research standards and clinical practice as it aims for establishing hypoxia PET and f-MRI as well as biological markers in HNC as a role model for a novel concept from anatomy-based to biologically iRT.
During this project, a multi-parametric model will be developed on a preclinical basis that combines biological markers such as different oncogenes and hypoxia gene classifier with functional PET/MR imaging, such as FMISO PET in combination with different f-MRI techniques, like DW-, DCE- and BOLD-MRI in addition to MR spectroscopy. The ultimate goal of this project is a multi-parametric model to predict therapy outcome and guide iRT.
In a second part, a clinical study will be carried out to validate the preclinical model in patients. Based on the most informative radiobiological and imaging parameters as identified during the pre-clinical phase, biological markers and advanced PET/MR imaging will be evaluated in terms of their potential for iRT dose prescription.
Successful development of a model for biologically iRT prescription on the basis of multi-parametric molecular profiling would provide a unique basis for personalized cancer treatment. A validated multi-parametric model for RT outcome would represent a paradigm shift from anatomy-based to biologically iRT concepts with the ultimate goal of improving cancer cure rates.
Max ERC Funding
1 370 799 €
Duration
Start date: 2014-01-01, End date: 2018-12-31
Project acronym BIOELECPRO
Project Frontier Research on the Dielectric Properties of Biological Tissue
Researcher (PI) Martin James O'Halloran
Host Institution (HI) NATIONAL UNIVERSITY OF IRELAND GALWAY
Call Details Starting Grant (StG), LS7, ERC-2014-STG
Summary The dielectric properties of biological tissues are of fundamental importance to the understanding of the interaction of electromagnetic fields with the human body. These properties are used to determine the safety of electronic devices, and in the design, development and refinement of electromagnetic medical imaging and therapeutic devices. Many historical studies have aimed to establish the dielectric properties of a broad range of tissues. A growing number of recent studies have sought to more accurately estimate these dielectric properties by standardising measurement procedures, and in some cases, measuring the dielectric properties in-vivo. However, these studies have often produced results in direct conflict with historical studies, casting doubt on the accuracy of the currently utilised dielectric properties. At best, this uncertainty could significantly delay the development of electromagnetic imaging or therapeutic medical devices. At worst, the health dangers of electromagnetic radiation could be under-estimated. The applicant will embark upon frontier research to develop improved methods and standards for the measurement of the dielectric properties of biological tissue. The research programme will accelerate the design and development of electromagnetic imaging and therapeutic devices, at a time when the technology is gaining significant momentum. The primary objective of the research is to develop a deep understanding of the fundamental factors which contribute to errors in dielectric property measurement. These factors will include in-vivo/ex-vivo measurements and dielectric measurement method used, amongst many others. Secondly, a new open-access repository of dielectric measurements will be created based on a greatly enhanced understanding of the mechanisms underlying dielectric property measurement. Finally, new electromagnetic-based imaging and therapeutic medical devices will be investigated, based on the solid foundation of dielectric data.
Summary
The dielectric properties of biological tissues are of fundamental importance to the understanding of the interaction of electromagnetic fields with the human body. These properties are used to determine the safety of electronic devices, and in the design, development and refinement of electromagnetic medical imaging and therapeutic devices. Many historical studies have aimed to establish the dielectric properties of a broad range of tissues. A growing number of recent studies have sought to more accurately estimate these dielectric properties by standardising measurement procedures, and in some cases, measuring the dielectric properties in-vivo. However, these studies have often produced results in direct conflict with historical studies, casting doubt on the accuracy of the currently utilised dielectric properties. At best, this uncertainty could significantly delay the development of electromagnetic imaging or therapeutic medical devices. At worst, the health dangers of electromagnetic radiation could be under-estimated. The applicant will embark upon frontier research to develop improved methods and standards for the measurement of the dielectric properties of biological tissue. The research programme will accelerate the design and development of electromagnetic imaging and therapeutic devices, at a time when the technology is gaining significant momentum. The primary objective of the research is to develop a deep understanding of the fundamental factors which contribute to errors in dielectric property measurement. These factors will include in-vivo/ex-vivo measurements and dielectric measurement method used, amongst many others. Secondly, a new open-access repository of dielectric measurements will be created based on a greatly enhanced understanding of the mechanisms underlying dielectric property measurement. Finally, new electromagnetic-based imaging and therapeutic medical devices will be investigated, based on the solid foundation of dielectric data.
Max ERC Funding
1 499 329 €
Duration
Start date: 2015-10-01, End date: 2020-09-30
Project acronym BIOSENSORIMAGING
Project Hyperpolarized Biosensors in Molecular Imaging
Researcher (PI) Leif Schröder
Host Institution (HI) FORSCHUNGSVERBUND BERLIN EV
Call Details Starting Grant (StG), LS7, ERC-2009-StG
Summary Xenon biosensors have an outstanding potential to increase the significance of magnetic resonance imaging (MRI) in molecular imaging and to combine the advantages of MRI with the high sensitivity of hyperpolarized Xe-129 and the specificity of a functionalized contrast agent. Based on new detection schemes (Hyper-CEST method) in Xe MRI, this novel concept in molecular diagnostics will be made available for biomedical applications. The advancement focuses on high-sensitivity in vitro diagnostics for localization of tumour cells in cell cultures and first demonstrations on animal models based on a transferrin-functionalized biosensor. Such a sensor will enable detection of subcutaneous tumours at high sensitivity without any background signal. More detailed work on the different available Hyper-CEST contrast parameters focuses on an absolute quantification of new molecular markers that will improve non-invasive tumour diagnostics significantly. NMR detection of functionalized Xe biosensors have the potential to close the sensitivity gap between modalities of nuclear medicine like PET/SPECT and MRI without using ionizing radiation or making compromises in penetration depth like in optical methods.
Summary
Xenon biosensors have an outstanding potential to increase the significance of magnetic resonance imaging (MRI) in molecular imaging and to combine the advantages of MRI with the high sensitivity of hyperpolarized Xe-129 and the specificity of a functionalized contrast agent. Based on new detection schemes (Hyper-CEST method) in Xe MRI, this novel concept in molecular diagnostics will be made available for biomedical applications. The advancement focuses on high-sensitivity in vitro diagnostics for localization of tumour cells in cell cultures and first demonstrations on animal models based on a transferrin-functionalized biosensor. Such a sensor will enable detection of subcutaneous tumours at high sensitivity without any background signal. More detailed work on the different available Hyper-CEST contrast parameters focuses on an absolute quantification of new molecular markers that will improve non-invasive tumour diagnostics significantly. NMR detection of functionalized Xe biosensors have the potential to close the sensitivity gap between modalities of nuclear medicine like PET/SPECT and MRI without using ionizing radiation or making compromises in penetration depth like in optical methods.
Max ERC Funding
1 848 600 €
Duration
Start date: 2009-12-01, End date: 2014-11-30
Project acronym Boom & Bust Cycles
Project Boom and Bust Cycles in Asset Prices: Real Implications and Monetary Policy Options
Researcher (PI) Klaus Adam
Host Institution (HI) UNIVERSITAET MANNHEIM
Call Details Starting Grant (StG), SH1, ERC-2011-StG_20101124
Summary I seek increasing our understanding of the origin of asset price booms and bust cycles and propose constructing structural dynamic equilibrium models that allow formalizing their interaction with the dynamics of consumption, hours worked, the current account, stock market trading activity, and monetary policy. For this purpose I propose developing macroeconomic models that relax the assumption of common knowledge of beliefs and preferences, incorporating instead subjective beliefs and learning about market behavior. These features allow for sustained deviations of asset prices from fundamentals in a setting where all agents behave individually rational.
The first research project derives the derivative price implications of asset price models with learning agents and determines the limits to arbitrage required so that learning models are consistent with the existence of only weak incentives for improving forecasts and beliefs. The second project introduces housing, collateral constraints and open economy features into existing asset pricing models under learning to explain a range of cross-sectional facts about the behavior of the current account that have been observed in the recent housing boom and bust cycle. The third project constructs quantitatively plausible macro asset pricing models that can explain the dynamics of consumption and hours worked jointly with the occurrence of asset price boom and busts cycles. The forth project develops a set of monetary policy models allowing to study the interaction between monetary policies, the real economy and asset prices, and determines how monetary policy should optimally react to asset price movements. The last project explains the aggregate trading patterns on stock exchanges over boom and bust cycles and improves our understanding of the forces supporting the large cross-sectional heterogeneity in return expectations revealed in survey data.
Summary
I seek increasing our understanding of the origin of asset price booms and bust cycles and propose constructing structural dynamic equilibrium models that allow formalizing their interaction with the dynamics of consumption, hours worked, the current account, stock market trading activity, and monetary policy. For this purpose I propose developing macroeconomic models that relax the assumption of common knowledge of beliefs and preferences, incorporating instead subjective beliefs and learning about market behavior. These features allow for sustained deviations of asset prices from fundamentals in a setting where all agents behave individually rational.
The first research project derives the derivative price implications of asset price models with learning agents and determines the limits to arbitrage required so that learning models are consistent with the existence of only weak incentives for improving forecasts and beliefs. The second project introduces housing, collateral constraints and open economy features into existing asset pricing models under learning to explain a range of cross-sectional facts about the behavior of the current account that have been observed in the recent housing boom and bust cycle. The third project constructs quantitatively plausible macro asset pricing models that can explain the dynamics of consumption and hours worked jointly with the occurrence of asset price boom and busts cycles. The forth project develops a set of monetary policy models allowing to study the interaction between monetary policies, the real economy and asset prices, and determines how monetary policy should optimally react to asset price movements. The last project explains the aggregate trading patterns on stock exchanges over boom and bust cycles and improves our understanding of the forces supporting the large cross-sectional heterogeneity in return expectations revealed in survey data.
Max ERC Funding
769 440 €
Duration
Start date: 2011-09-01, End date: 2017-04-30
