A key problem in Mental Health is that up to one third of patients suffering from major mental disorders develop resistance against drug therapy. However, patients showing early signs of treatment resistance (TR) do not receive adequate early intensive pharmacological treatment but instead they undergo a stepwise trial-and-error treatment approach. This situation originates from three major knowledge and translation gaps: i.) we lack effective methods to identify individuals at risk for TR early in the disease process, ii.) we lack effective, personalized treatment strategies grounded in insights into the biological basis of TR, and iii.) we lack efficient processes to translate scientific insights about TR into clinical practice, primary care and treatment guidelines. It is the central goal of PSYCH-STRATA to bridge these gaps and pave the way for a shift towards a treatment decision-making process tailored for the individual at risk for TR. To that end, we aim to establish evidence-based criteria to make decisions of early intense treatment in individuals at risk for TR across the major psychiatric disorders of schizophrenia, bipolar disorder and major depression. PSYCH-STRATA will i.) dissect the biological basis of TR and establish criteria to enable early detection of individuals at risk for TR based on the integrated analysis of an unprecedented collection of genetic, biological, digital mental health, and clinical data. ii.) Moreover, we will determine effective treatment strategies of individuals at risk for TR early in the treatment process, based on pan-European clinical trials in SCZ, BD and MDD. These efforts will enable the establishment of novel multimodal machine learning models to predict TR risk and treatment response. Lastly, iii.) we will enable the translation of these findings into clinical practice by prototyping the integration of personalized treatment decision support and patient-oriented decision-making mental health boards.

Heart attacks are common and can leave survivors suffering cardiogenic shock (CGS), with its extremely high risk of early death (42% versus 4% for non-CGS) and high incidence of chronic heart failure, with its associated socio-economic disease burden (ill-health, recurrent heart failure admissions, no return to full time activities, need for expensive therapeutic devices and life-long drugs). Outcomes from CGS are even worse in, females, the elderly (mortality>70%) and high risk sub-groups. The incidence of CGS in Europe alone is >50 000 patients pa. This unacceptably high mortality/morbidity rate represents a true unmet clinical need. No clear strategy exists to improve outcomes, with ad hoc therapies given too late in a spiralling, irrecoverable process. EURO SHOCK aims to improve outcomes for CGS patients. At its core is a robust phase3 randomised trial comparing a novel strategy of very early use of ECMO (Extracorporeal Membrane Oxygenation) to current standard of care. Evidence suggests very early ECMO will halt the spiral of decline and so significantly reduce 12 month death rate and need for heart failure re-admissions. Since both costs of CGS, and ECMO are high a health-economic cost efficacy analysis will be core. A cardiac magnetic resonance imaging sub-study will test novel protocols in sick patients and provide mechanistic data. We will test transfer networks for CGS patients and analyse ECG data to determine which patients benefit most from early transfer. Our multidisciplinary consortium comprises renowned physician-scientists, statisticians, health economists and technology providers, including specialised ECMO SME. EURO SHOCK will impact on heart attack survivors, healthcare providers and Europe`s medical technology sector by 1) reducing healthcare costs associated with CGS 2) provide novel cost effective framework for cardiac interventions 3) delivering innovative healthcare technologies and 4) informing guidelines for effective CGS intervention.

The pathological alterations of neurological function (e.g. stroke, trauma, neurodegeneration, epilepsy, neuropsychiatric diseases, chronic pain) are commonly associated with alterations in brain rhythms and activity patterns. There is an urgent clinical need for treatments that can precisely control and restore neural activity, taking advantage of state-of-the-art technological developments in a variety of fields including nanotechnology, nano- and microelectronics, novel materials, brain science, clinical neurology, and computational modelling. META-BRAIN (MagnetoElectric and Ultrasonic Technology for Advanced BRAIN modulation) brings together seven expert partners in these fields with the aim of achieving precise spatiotemporal control of brain activity using magnetoelectric nanoarchitectures that can be polarized by non-invasive, remote magnetic fields. This novel principle of brain activity control will minimize the amplitude of the required magnetic fields, be wireless, and have enhanced spatial resolution from single neurons to cortical areas. We will develop a model-driven fabrication of the coils and monitor the effects on brain function with arrays of graphene microtransistors that uniquely allow full-band recording, integrating all elements in a closed loop. As an alternative to remote brain stimulation we will also use novel ultrasonic technologies. The META-BRAIN control paradigm will be systematically studied in pre-clinical systems from individual neurons to the full brain. All developments and experiments will be carried out in conjunction with theoretical models that will simulate, quantify, and predict optimal arrangements and patterns for the desired output. Translation to humans will be evaluated with our clinical partners, and a detailed dissemination and exploitation plan will be developed by two expert company partners, one of which has extensive expertise in the fabrication of brain interface devices with a worldwide distribution capability.

European researchers have made leading contributions to the large genomic, transcriptomic and clinical datasets from patients with chronic diseases. Advances in information science provide unprecedented opportunities for using these datasets to elucidate the complex biology of these disorders, its influence by environmental triggers, and to personalise their management. Currently, exploitation of these opportunities is limited by a shortage of researchers with the required informatics skills and knowledge of requisite data protection principles. HELICAL addresses this unmet need by developing a trans-sectoral and interdisciplinary training programme that builds on the expertise and existing collaborations of its partners. It provides 15 early stage researchers with training in analysis of large datasets, using autoimmune vasculitis as a paradigm as it is scalable, and as comprehensive biological and clinical datasets are already available. The HELICAL training program focuses on three complementary areas: application of informatics to such datasets to gain new biological insights; translation of these into practical clinical outputs and management of ethical constraints imposed on such studies. The programme will be delivered through a multidisciplinary, trans-sectoral partnership of Academic and Industry researchers with expertise in basic biomedical research, epidemiology, statistics, machine learning, health data governance and ethics. Therefore, HELICAL exploits recent advances in data science to link research datasets with longitudinal healthcare records, based on the robust ethical foundation required for linkage studies using near-patient data, to address key experimental questions. The results will have obvious potential for transforming healthcare in the field of autoimmune disease. The training provided addresses a key skills gap in the European workforce and should make the ESR eminently employable in academic, industrial and clinical sectors.

Frontotemporal dementia (FTD) has a debilitating effect on patients and their caregivers and leads to substantial economic costs. 15-30% of patients have familial FTD caused by known pathogenetic mutations. For the other 70-85% of patients, termed sporadic FTD, diagnosis is slow (~3.6 years) with frequent misdiagnosis due to clinical, genetic and molecular heterogeneity. Thus, there is great need for biomarkers for early diagnosis of sporadic FTD and its pathological subtypes. In PREDICTFTD, we will validate a set of biomarkers and create a diagnostic tool for early diagnosis of familial and sporadic FTD, which will facilitate tailored support and symptomatic treatments and care. We will apply several new approaches to achieve this: 1) we combine 11 geographically diverse cohorts of sporadic and familial FTD with retrospective and prospective longitudinal liquid biopsy samples and extensive clinical and behavioural data; 2) we are the first to use multimodal clinical and liquid biomarker data to train an AI-algorithm as a diagnostic tool for quick and early clinical FTD diagnosis; and 3) we implement a novel robust two-stage strategy for biomarker and AI algorithm validation, where phase I validates biomarkers and algorithms on a cohort of genetic and autopsied cases and phase II assesses biomarker value for diagnosis of sporadic FTD and at-risk pre-symptomatic mutation carriers. We will apply this two-stage validation strategy to address three critical clinical challenges: i) To distinguish sporadic FTD from (non-) neurodegenerative disorders that show significant clinical/symptomatic overlap, ii) To robustly detect FTD pathological subtypes in sporadic FTD and iii) pre-symptomatic identification of FTD onset. Thus, PREDICTFTD will transform FTD diagnosis, offering potential for early disease confirmation, guiding treatment decisions, facilitating patient recruitment for clinical trials, guidance of patients and caregivers, and enabling preventive measures.