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115 | 115 | "objectID": "subsite/projects/AMI-cryoEM.html", |
116 | 116 | "href": "subsite/projects/AMI-cryoEM.html", |
117 | | - "title": "AMI-CryoML", |
| 117 | + "title": "AMI‑CryoML", |
118 | 118 | "section": "", |
119 | | - "text": "Title: Cracking the code of amyloid polymorphism: Integrating cryoEM and machine learning to unravel impact of small aggregation modulators on amyloid fibril polymorphism\nAbstract: Neurodegenerative diseases such as Alzheimer’s (AD), Parkinson’s (PD), and prion-related disorders pose major global health challenges due to their progressive nature and lack of effective treatments. Central to these conditions is the misfolding and aggregation of proteins into amyloid structures, which cause severe cellular damage. Despite advances in understanding amyloid aggregation, the role of structural polymorphism in these processes remains underexplored. This proposal aims to address this gap by investigating amyloid aggregation modulators and their effects on aggregation kinetics and polymorphism. The project has five main objectives: 1. Data Collection on agregation modulators: Systematically gather and curate data on known modulators that impact amyloid aggregation, focusing on their chemical properties and mechanisms. 2. Aggregation kinetics studies: Study the impact of selected modulators on the kinetics of amyloid self-aggregation using ThT assays. This includes analyzing how modulators influence different phases of aggregation and testing their efficacy on a range of amyloids, including prions, CsgA, and α-synuclein. 3. Study structural polymorphisms via CryoEM: Utilize Cryo-Electron Microscopy (CryoEM) to resolve and map the structural polymorphs of amyloids under various conditions. Providing insights into impact of modulators on structural polymorphism and aggregation kinetics. 4. AmyloGraph database enhancement: Develop a new module for the AmyloGraph database to store and analyze data on modulators, aggregation kinetics, and structural polymorphisms. 5. Development of ML models: Leverage machine learning to develop predictive and generative models to forecast amyloid aggregation kinetics and modulator effects, incorporating structural polymorphisms, and generate new modulators targeting specific polymorphic forms.\nPI: Michał\nGrant preparation: Michał, Jarek, Valen\nCall: HORIZON-WIDERA-2024-TALENTS-02\nType of Action: HORIZON-TMA-MSCA-PF-EF\nAcronym: AMI-CryoML\nCurrent Phase: Grant Management\nNumber: 101244706\nDuration: 24 months\nGA based on the: HE Unit MGA — Multi & Mono - 1.2\nStart Date: 01 Feb 2026\nEstimated Project Cost: €0.00\nRequested EU Contribution: €181,136.16" |
| 119 | + "text": "Cracking the code of amyloid polymorphism: integrating cryoEM and machine learning to unravel impact of small aggregation modulators on amyloid fibril polymorphism\nPI: Jarek\nGrant ID: 101244706\nStart Date: 1 Feb 2026\nDuration: 24 months\nEU Contribution: €181136.16\nNeurodegenerative disorders such as Alzheimer’s, Parkinson’s, and prion diseases are marked by protein misfolding into amyloid fibrils. The structural diversity (polymorphism) of these fibrils critically affects their pathology, yet remains largely unexplored. AMI‑CryoML bridges cryo‑EM and machine learning to probe how small molecule modulators influence aggregation kinetics and amyloid structures.\n\n\n\n🔍 Systematic collection of aggregation modulators: chemical and mechanistic profiling\n\n🧪 Thioflavin‑T assays to measure kinetic effects across proteins (prions, CsgA, α‑synuclein)\n\n🧬 Cryo‑EM to resolve fibril polymorphs under varied conditions\n\n💾 Extension of the AmyloGraph database to store modulator‑kinetics‑structure data\n\n🤖 Machine learning models to predict modulator effects\n\n\n\n\n\n\nAmyloGraph database\n\n📥 Contact us to join the AMI‑CryoML collaboration or for data access." |
| 120 | + }, |
| 121 | + { |
| 122 | + "objectID": "subsite/projects/AMI-cryoEM.html#amicryoml-unraveling-amyloid-polymorphism", |
| 123 | + "href": "subsite/projects/AMI-cryoEM.html#amicryoml-unraveling-amyloid-polymorphism", |
| 124 | + "title": "AMI‑CryoML", |
| 125 | + "section": "", |
| 126 | + "text": "Cracking the code of amyloid polymorphism: integrating cryoEM and machine learning to unravel impact of small aggregation modulators on amyloid fibril polymorphism\nPI: Jarek\nGrant ID: 101244706\nStart Date: 1 Feb 2026\nDuration: 24 months\nEU Contribution: €181136.16\nNeurodegenerative disorders such as Alzheimer’s, Parkinson’s, and prion diseases are marked by protein misfolding into amyloid fibrils. The structural diversity (polymorphism) of these fibrils critically affects their pathology, yet remains largely unexplored. AMI‑CryoML bridges cryo‑EM and machine learning to probe how small molecule modulators influence aggregation kinetics and amyloid structures.\n\n\n\n🔍 Systematic collection of aggregation modulators: chemical and mechanistic profiling\n\n🧪 Thioflavin‑T assays to measure kinetic effects across proteins (prions, CsgA, α‑synuclein)\n\n🧬 Cryo‑EM to resolve fibril polymorphs under varied conditions\n\n💾 Extension of the AmyloGraph database to store modulator‑kinetics‑structure data\n\n🤖 Machine learning models to predict modulator effects\n\n\n\n\n\n\nAmyloGraph database\n\n📥 Contact us to join the AMI‑CryoML collaboration or for data access." |
120 | 127 | }, |
121 | 128 | { |
122 | 129 | "objectID": "subsite/posts/2025_07_02.html", |
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1061 | 1068 | "href": "subsite/projects/OneTick.html", |
1062 | 1069 | "title": "OneTick", |
1063 | 1070 | "section": "", |
1064 | | - "text": "Title: OneTick: An Integrated One Health Approach for Prevention, Detection and Treatment of Tick-Borne Diseases in Urban and Peri-Urban Environments\nAbstract: Tick-borne diseases (TBDs), caused by bacteria, viruses, or protozoa, present a significant health and socioeconomic challenge in Europe. Historically centered in Central and Eastern Europe, Lyme disease and tick-borne encephalitis (TBE) are prevalent in Austria, Germany, and the Czech Republic, with their range expanding due to climate change. Reports show a rising incidence of TBDs in Northern Europe, with about 24% of Europeans living in high Lyme borreliosis (LB) areas as of 2022. It is further heightened by the tick collonization of urban and peri-urban environments where the risk of a tick bite is much higher due to the larger population density of humans and pets. TBDs impose a considerable burden on European societies, with the ECDC estimating around 360,000 Lyme cases in 2016, leading to societal costs of approximately 280 M EUR annually. OneTick, under the One Health framework, aims to enhance the prevention, detection, and treatment of tick-borne diseases in urban areas. Work Package 1 investigates urban tick adaptation, assessing their abundance, species diversity, and pathogens. Work Package 2 examines tick-host-pathogen interactions to develop predictive models for disease spread. Work Package 3 aims to enhance diagnostics through the identification of disease biomarkers and the development of predictive models for survival rates. Lastly, Work Package 4 focuses on raising public health awareness through guidelines and educational campaigns to combat misinformation. In OneTick each participating organization contributes unique expertise, forming a radial structure that integrates diverse scientific perspectives and fosters harmonization of methodologies across disciplines and sectors. We plan to implement 90 PM of secondments, incl. 60 intersectoral and 30 interdisciplinary staff exchanges. Through secondments, we will disseminate best practices, enhance interoperability, and establish shared frameworks for tick-borne disease research and surveillance.\nPI: Jarek\nCall: HORIZON-MSCA-2024-SE-01\nType of Action: HORIZON-TMA-MSCA-SE\nAcronym: OneTick\nCurrent Phase: Grant Management\nNumber: 101236599\nDuration: 48 months\nGA based on the: HE Unit MGA — Multi & Mono - 1.2\nStart Date: 01 Jan 2026\nEstimated Project Cost: €0.00\nRequested EU Contribution: €450,900.00" |
| 1071 | + "text": "OneTick: An Integrated One Health Approach for Prevention, Detection and Treatment of Tick‑Borne Diseases in Urban and Peri‑Urban Environments\nPI: Michał Burdukiewicz\nProposal development: Michał Burdukiewicz, Jarosław Chilimoniuk, Valentín Iglesias\nGrant ID: 101236599\nStart Date: 1 Jan 2026\nDuration: 48 months\nEU Contribution: €450900.00\nTick‑borne diseases (TBDs) such as Lyme disease and TBE are on the rise in Europe due to shifts in climate and urbanization. In 2022, some 24% of Europeans resided in high‑risk Lyme areas, and the ECDC estimated ~360,000 cases in 2016, costing about €280M annually. OneTick advances prevention, detection and treatment within a One Health framework, targeting urban and peri‑urban areas.\n\n\n\n🔬 WP1: Urban tick ecology — assess abundance, diversity, and pathogens\n\n📈 WP2: Tick–host–pathogen modeling — predictive frameworks for disease spread\n\n🧪 WP3: Biomarker discovery & diagnostic modeling\n\n📣 WP4: Public outreach — guidelines and educational campaigns\n\n👥 Secondments: 90 PM (60 intersectoral, 30 interdisciplinary) to share best practices\n\nVisit project website for full info. Click on the logo below." |
| 1072 | + }, |
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| 1075 | + "href": "subsite/projects/OneTick.html#onetick-one-health-approach-to-tickborne-diseases", |
| 1076 | + "title": "OneTick", |
| 1077 | + "section": "", |
| 1078 | + "text": "OneTick: An Integrated One Health Approach for Prevention, Detection and Treatment of Tick‑Borne Diseases in Urban and Peri‑Urban Environments\nPI: Michał Burdukiewicz\nProposal development: Michał Burdukiewicz, Jarosław Chilimoniuk, Valentín Iglesias\nGrant ID: 101236599\nStart Date: 1 Jan 2026\nDuration: 48 months\nEU Contribution: €450900.00\nTick‑borne diseases (TBDs) such as Lyme disease and TBE are on the rise in Europe due to shifts in climate and urbanization. In 2022, some 24% of Europeans resided in high‑risk Lyme areas, and the ECDC estimated ~360,000 cases in 2016, costing about €280M annually. OneTick advances prevention, detection and treatment within a One Health framework, targeting urban and peri‑urban areas.\n\n\n\n🔬 WP1: Urban tick ecology — assess abundance, diversity, and pathogens\n\n📈 WP2: Tick–host–pathogen modeling — predictive frameworks for disease spread\n\n🧪 WP3: Biomarker discovery & diagnostic modeling\n\n📣 WP4: Public outreach — guidelines and educational campaigns\n\n👥 Secondments: 90 PM (60 intersectoral, 30 interdisciplinary) to share best practices\n\nVisit project website for full info. Click on the logo below." |
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