Yasmina Molero

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.