Zobrazeno 1 - 10
of 609
pro vyhledávání: '"Anders, Eklund"'
Autor:
Lars Willas Dreyer, Anders Eklund, Marie E. Rognes, Jan Malm, Sara Qvarlander, Karen-Helene Støverud, Kent-Andre Mardal, Vegard Vinje
Publikováno v:
Fluids and Barriers of the CNS, Vol 21, Iss 1, Pp 1-22 (2024)
Abstract Background Infusion testing is an established method for assessing CSF resistance in patients with idiopathic normal pressure hydrocephalus (iNPH). To what extent the increased resistance is related to the glymphatic system is an open questi
Externí odkaz:
https://doaj.org/article/e26d5ad6452b443394cb844469d76bdc
Publikováno v:
Fluids and Barriers of the CNS, Vol 21, Iss 1, Pp 1-9 (2024)
Abstract Background Studies indicate that brain clearance via the glymphatic system is impaired in idiopathic normal pressure hydrocephalus (INPH). This has been suggested to result from reduced cerebrospinal fluid (CSF) turnover, which could be caus
Externí odkaz:
https://doaj.org/article/66703bc7773846009fa0713955f9d21b
Autor:
Tomas Vikner, Anders Garpebring, Cecilia Björnfot, Lars Nyberg, Jan Malm, Anders Eklund, Anders Wåhlin
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Blood–brain barrier (BBB) disruption may contribute to cognitive decline, but questions remain whether this association is more pronounced for certain brain regions, such as the hippocampus, or represents a whole-brain mechanism. Further,
Externí odkaz:
https://doaj.org/article/3633223a451448a182f93b94b91daa36
Autor:
Axel Vikström, Petter Holmlund, Madelene Holmgren, Anders Wåhlin, Laleh Zarrinkoob, Jan Malm, Anders Eklund
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Cerebrovascular resistance (CVR) regulates blood flow in the brain, but little is known about the vascular resistances of the individual cerebral territories. We present a method to calculate these resistances and investigate how CVR varies
Externí odkaz:
https://doaj.org/article/3dad305b0bad4a4d9a0c63210e287221
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-17 (2024)
Abstract Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversar
Externí odkaz:
https://doaj.org/article/5be8ad311a284e288cfa346f65747650
Autor:
Ying Xiong, Susanna Kullberg, Lori Garman, Nathan Pezant, David Ellinghaus, Vasiliki Vasila, Anders Eklund, Benjamin A. Rybicki, Michael C. Iannuzzi, Stefan Schreiber, Joachim Müller-Quernheim, Courtney G. Montgomery, Johan Grunewald, Leonid Padyukov, Natalia V. Rivera
Publikováno v:
Frontiers in Medicine, Vol 11 (2024)
Externí odkaz:
https://doaj.org/article/b8003463ca864610be2ddc104fdcdd3d
Autor:
Chiara Trenti, MSc, Deneb Boito, Filip Hammaréus, Anders Eklund, Eva Swahn, MD, Lena Jonasson, MD, Bertil Wegmann, PhD, Petter Dyverfeldt
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 26, Iss , Pp 100612- (2024)
Externí odkaz:
https://doaj.org/article/a7e1ee6408054c3aaf39b9d5b1907cad
Publikováno v:
BMJ Open Respiratory Research, Vol 10, Iss 1 (2023)
Background Early identification of patients at risk for progressive sarcoidosis may improve intervention. High bronchoalveolar lavage fluid (BALF) lymphocytes and peripheral blood (PB) lymphopenia are associated with worse prognosis. The mechanisms b
Externí odkaz:
https://doaj.org/article/f48b1fb93ede4c1db159ef1f17450bd3
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-8 (2022)
Abstract In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive image
Externí odkaz:
https://doaj.org/article/6656aa5538aa4994ab5940befa5e25c1
Autor:
Johan Jönemo, Anders Eklund
Publikováno v:
Journal of Imaging, Vol 9, Iss 12, p 271 (2023)
Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like
Externí odkaz:
https://doaj.org/article/dbb711f67ebb4bd4bfb9b55cda419287