Yasmina Molero

The Swedish government has a new national strategy for mental health and suicide prevention, and problem gambling is highlighted as a target area, underscoring the relevance and timeliness of this project. We will examine the relationship between problem gambling, mental health problems and suicide, and how financial debt may affect this relationship, in a nationwide cohort of 125,000 individuals who have self-excluded from all licensed gambling in Sweden via the governmental service www.spelpaus.se. For the first time, this unique cohort will be linked to national patient, medication, death, socioeconomic, demographic, family, and financial debt registers. To strengthen causal inference and uncover underlying mechanisms, we will adjust for familial confounders (e.g. genetic vulnerability and early-life environment) by comparing them to both a matched population cohort and a sibling cohort, applying fixed-effects models. A team of psychologists, psychiatrists, epidemiologists, and public health experts from Karolinska Institutet, University of Helsinki, and University of Oxford will lead the study. Year 1: secure and prepare register data, preliminary analyses. Years 2-3: further statistical modelling, submit manuscripts. Year 4: finalize analyses, submit final manuscript, and dissemination. By partnering with policymakers, suicide prevention coordinators and gambling regulators we aim to inform strategies that reduce psychiatric harms in this identified at-risk population.

Traumatic Brain Injury (TBI) affects over 60 million people annually worldwide, leading to significant disabilities and increasing the risk of dementia. Despite TBI´s heterogeneity and varying outcomes among similarly injured patients, current treatment relies on a uniform, one-size-fits-all approach, often resulting in suboptimal care. This one-size-fits-all approach fails to accommodate the individual variations and complexities inherent in each case of TBI, leading to suboptimal outcomes. Recognizing the need for personalized treatment strategies, this project proposes the integration of clinical variables, head CT-scans, biological, high-resolution physiological data, and genetic variations, cognitive reserve, harnessing comprehensive Swedish and international TBI datasets, bolstered by extensive Swedish linkage registers. Employing machine learning algorithms, this approach strives for customized interventions and accurate prognosis tools, transforming care in neurointensive clinics. Additionally, addressing the gap between animal studies and human trials, we introduce a humanized in vivo model for TBI research, essential for testing neuroprotective drugs and understanding human cell responses. Combining clinical and experimental efforts with advanced data-driven techniques, our project targets the discovery of effective drug targets to improve TBI recovery, underscored by a humanized model for direct relevance to patient care.