| 1. |
- Pulli, E. P., et al.
(författare)
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Prenatal exposures and infant brain: Review of magnetic resonance imaging studies and a population description analysis
- 2019
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Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 40:6, s. 1987-2000
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Tidskriftsartikel (refereegranskat)abstract
- Brain development is most rapid during the fetal period and the first years of life. This process can be affected by many in utero factors, such as chemical exposures and maternal health characteristics. The goal of this review is twofold: to review the most recent findings on the effects of these prenatal factors on the developing brain and to qualitatively assess how those factors were generally reported in studies on infants up to 2 years of age. To capture the latest findings in the field, we searched articles from PubMed 2012 onward with search terms referring to magnetic resonance imaging (MRI), brain development, and infancy. We identified 19 MRI studies focusing on the effects of prenatal environment and summarized them to highlight the recent advances in the field. We assessed population descriptions in a representative sample of 67 studies and conclude that prenatal factors that have been shown to affect brain metrics are not generally reported comprehensively. Based on our findings, we propose some improvements for population descriptions to account for plausible confounders and in time enable reliable meta-analyses to be performed. This could help the pediatric neuroimaging field move toward more reliable identification of biomarkers for developmental outcomes and to better decipher the nuances of normal and abnormal brain development.
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| 2. |
- Raj, R, et al.
(författare)
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Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm
- 2022
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Ingår i: NPJ digital medicine. - : Springer Science and Business Media LLC. - 2398-6352. ; 5:1, s. 96-
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Tidskriftsartikel (refereegranskat)abstract
- Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.
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| 3. |
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| 4. |
- Bock G., Roberts R.G., Kissling E., Achauer A., Alingahi J., Bruneton M., Friedrich W., Grad M. Guterch A., Hjelt S-E., Hyvönen T., Ikonen J-P., Komminaho K., Korja A, Heikkinen P., Kozolovaskaya E., Nevsky M.V., Pavlenkova N., Pedersen H., Plomerova J.
(författare)
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Seismic probing of Archean and Proterozoic Lithosphere in Fennoscandia.
- 2001
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Ingår i: EOS Transactions American Geophysical Union. - : American Geophysical Union. ; 82, s. 621,628-629
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Tidskriftsartikel (refereegranskat)
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| 5. |
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