[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100544681":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":23,"centralContacts":36,"locations":46,"responsibleParty":82,"collaborators":84,"id":93,"slug":94,"hasResults":95,"nctId":96,"briefTitle":97,"officialTitle":98,"acronym":10,"eligibilityCriteria":99,"healthyVolunteers":95,"sex":100,"minAge":101,"maxAge":102,"enrollmentInfo":103,"targetDuration":106,"studyType":107,"phases":10,"briefSummary":108,"conditions":109,"keywords":119,"overallStatus":49,"whyStopped":10,"lastUpdateSubmitDate":124,"lastUpdatePostDateStruct":125,"startDateStruct":128,"completionDateStruct":130,"leadSponsor":132,"locationsCount":133},{"fullName":5,"class":6},"Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"High-performance Italian Air Force Pilots",null,"The primary study cohort is represented by \"super-healthy\" high-performance Italian Air Force Pilots, aged between 26 and 38 years, in active flight service.\n\nIntervention: not applicable",[13],"Other: Biological sample collection",{"label":15,"type":10,"description":16,"interventionNames":17},"Italian Air Force ground staff","This cohort of Italian Air Force ground personnel will be used as a control group to compare data from the pilot cohort.",[13],[19],{"type":6,"name":20,"description":21,"armGroupLabels":22,"otherNames":10},"Biological sample collection","Collection of biological samples (blood, urine, saliva) and clinical data",[9,15],[24,28,32,34],{"name":25,"affiliation":26,"role":27},"Giovanni Marfia, MD, PhD","Fondazione IRCCs Ca' Granda Ospedale MAggiore Policlinico, Italian Air Force","PRINCIPAL_INVESTIGATOR",{"name":29,"affiliation":30,"role":31},"Emanuele Garzia, MD, PhD","Italian Air Force","STUDY_CHAIR",{"name":33,"affiliation":5,"role":31},"Marco Locatelli, MD, PhD",{"name":35,"affiliation":30,"role":31},"Francesco Vestito, PhD",[37,42],{"name":25,"role":38,"phone":39,"phoneExt":40,"email":41},"CONTACT","0256660100","+39","giovanni.marfia@policlinico.mi.it",{"name":43,"role":38,"phone":44,"phoneExt":40,"email":45},"Laura Guarnaccia, PhD","0255034268","laura.guarnaccia@policlinico.mi.it",[47],{"facility":48,"status":49,"city":50,"state":10,"zip":51,"country":52,"countryCode":53,"cosmosGeoPoint":54,"geoPoint":59,"contacts":60},"CeMATA - Joint Center for Aerospace Medicine and Advanced Therapy","RECRUITING","Milan","20139","Italy","IT",{"type":55,"coordinates":56},"Point",[57,58],9.18951,45.46427,{"lat":58,"lon":57},[61,64,65,67,68,70,72,74,76,78,80],{"name":62,"role":38,"phone":39,"phoneExt":40,"email":63},"Stefania E Navone, PhD","stefania.navone@policlinico.mi.it",{"name":25,"role":27,"phone":10,"phoneExt":10,"email":10},{"name":43,"role":66,"phone":10,"phoneExt":10,"email":10},"SUB_INVESTIGATOR",{"name":62,"role":66,"phone":10,"phoneExt":10,"email":10},{"name":69,"role":66,"phone":10,"phoneExt":10,"email":10},"Monica R Miozzo, PhD",{"name":71,"role":66,"phone":10,"phoneExt":10,"email":10},"Orazio Granato, PhD",{"name":73,"role":66,"phone":10,"phoneExt":10,"email":10},"Silvana Pileggi, PhD",{"name":75,"role":66,"phone":10,"phoneExt":10,"email":10},"Luisella Vigna, MD, PhD",{"name":77,"role":66,"phone":10,"phoneExt":10,"email":10},"Matteo Bonzini, MD, PhD",{"name":79,"role":66,"phone":10,"phoneExt":10,"email":10},"Laura Fontana, PhD",{"name":81,"role":66,"phone":10,"phoneExt":10,"email":10},"Laura Begani, MSc",{"type":83,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[85,87,89,91],{"name":86,"class":6},"University of Milan",{"name":30,"class":88},"UNKNOWN",{"name":90,"class":88},"A-Tono",{"name":92,"class":88},"Ministry of Defense, Italy","100544681","tornado-omics-techniques-and-neural-networks-for-the-development-of-predictive-risk-models-100544681",false,"NCT06372054","TORNADO-Omics Techniques and Neural Networks for the Development of Predictive Risk Models","Integration of Omics-based Technologies and Artificial Intelligence to Identify Predictive Risk Models in a Air Force's Pilot Cohort for the Maintenance of Safety, Well-being, Health, and Performance to be Translated to Civil Population","Inclusion Criteria:\n\n* Being part of the Italian Air Force, as in active flight service or ground staff\n* Age between 26 and 38 years\n* Consent to collect biological samples and use the wearable device to monitor exposure parameters\n\nExclusion Criteria:\n\n* Age \\\u003C 25 years and \\> 39 years\n* no signature on informed consent","ALL","26 Years","38 Years",{"count":104,"type":105},200,"ESTIMATED","3 Years","OBSERVATIONAL","The goal of this observational study is to define a personalized risk model in the super healthy and homogeneous population of Italian Air Force high-performance pilots. This peculiar cohort conducts dynamic activities in an extreme environment, compared to a population of military people not involved in flight activity. The study integrates the analyses of biological samples (urine, blood, and saliva), clinical records, and occupational data collected at different time points and analyzed by omic-based approaches supported by Artificial Intelligence. Data resulting from the study will clarify many etiopathological mechanisms of diseases, allowing the creation of a model of analyses that can be extended to the civilian population and patient cohorts for the potentiation of precision and preventive medicine.",[110,111,112,113,114,115,116,117,118],"Oxidative Injury","Stress Physiological","Discogenic Pain","Cardiovascular Risk Factor","Space Maintenance","Epigenetic Changes","LONGEVITY 1","Neuroplasticity","NGS",[120,121,122,123],"pilots","air force","epigenetic change","environmental exposure","2024-04-16",{"date":126,"type":127},"2024-04-17","ACTUAL",{"date":129,"type":127},"2024-02-05",{"date":131,"type":105},"2027-02-05",{"name":5,"class":6},1]