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                "en": "Abstract Background It is known that blood levels of neurofilament light (NF-L) and diffusion-weighted magnetic resonance imaging (DW-MRI) are both associated with outcome of patients with mild traumatic brain injury (mTBI). Here, we sought to examine the association between admission levels of plasma NF-L and white matter (WM) integrity in post-acute stage DW-MRI in patients with mTBI. Methods Ninety-three patients with mTBI (GCS ≥ 13), blood sample for NF-L within 24 h of admission, and DW-MRI ≥ 90 days post-injury (median = 229) were included. Mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated from the skeletonized WM tracts of the whole brain. Outcome was assessed using the Extended Glasgow Outcome Scale (GOSE) at the time of imaging. Patients were divided into CT-positive and -negative, and complete (GOSE = 8) and incomplete recovery (GOSE < 8) groups. Results The levels of NF-L and FA correlated negatively in the whole cohort (p = 0.002), in CT-positive patients (p = 0.016), and in those with incomplete recovery (p = 0.005). The same groups showed a positive correlation with mean MD, AD, and RD (p < 0.001—p = 0.011). In CT-negative patients or in patients with full recovery, significant correlations were not found. Conclusion In patients with mTBI, the significant correlation between NF-L levels at admission and diffusion tensor imaging (DTI) measurements of diffuse axonal injury (DAI) over more than 3 months suggests that the early levels of plasma NF-L may associate with the presence of DAI at a later phase of TBI."
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                "en": "BackgroundAgeing of the population increases the prevalence and coexistence of many chronic diseases; a condition called multimorbidity. In Finland, information on the significance of multimorbidity and its relation to the sustainability of healthcare is scarce.AimTo assess the prevalence of multimorbidity, the transitions between patient groups with and without multiple diseases and the associated healthcare cost in Finland in 2017–2019.MethodsA register-based cohort study covering all adults (n = 3,326,467) who used Finnish primary or specialised healthcare services in 2017. At baseline, patients were classified as ‘non-multimorbid’, ‘multimorbid’ or ‘multimorbid at risk’ based on the recordings of a diagnosis of interest. The costs were calculated using the care-related patient grouping and national standard rates. Transition plots were drawn to observe the transition of patients and costs between groups during the two-year follow-up.ResultsAt baseline, 62% of patients were non-multimorbid, 23% multimorbid and 15% multimorbid at risk. In two years, the proportion of multimorbid patients increased, especially those at risk. Within the multimorbid at-risk group, total healthcare costs were greatest (€5,027 million), accounting for 62% of the total healthcare cost of the overall patient cohort in 2019. Musculoskeletal diseases, cardiometabolic diseases and tumours were the most common and expensive chronic diseases contributing to the onset of multimorbidity.ConclusionMultimorbidity is causing a heavy burden on Finnish healthcare. The estimates of its effect on healthcare usage and costs should be used to guide healthcare planning."
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                "en": "These data refer to the second test campaign of the SHIVER project, conducted in the Ice and Wave tank of Aalto University in spring 2023.Basin tests were performed to gather data on ice action, ice-structure interaction and ice scaling to predict ice-induced vibration of offshore structures (e.g., offshore wind turbines, lighthouses, channel markers).Tests were conducted with a real-time hybrid test setup, which had been used during the first test campaign of the SHIVER project (go to publication, go to data). Small modification of the setup allowed to test substructures with different diameters and shape at the water line and improved data reliability.Detailed explanations of the test setup, test matrix, and test results are provided in a manuscript submitted to the journal Cold Regions Science and Technology. All Metadata will be included upon acceptance of the manuscript. Refer to the README.pdf file for more information about the data uploaded. Contact Tim C. Hammer (t.c.hammer@tudelft.nl) for additional information."
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