Results 151 to 160 of about 188,341 (310)

Acute impact of apheresis on oxidized phospholipids in patients with familial hypercholesterolemia1

open access: yesJournal of Lipid Research, 2012
We measured oxidized phospholipids (OxPL), lipoprotein (a) [Lp(a)], and lipoprotein-associated phospholipase A2 (Lp-PLA2) pre- and postapheresis in 18 patients with familial hypercholesterolemia (FH) and with low(∼10 mg/dl; range 10–11 mg/dl ...
Kiyohito Arai   +8 more
doaj  

THE EFFECT LDL APHERESIS ON PERIPHERAL CIRCULATION

open access: bronze, 2001
Yasunori KUTSUMI   +3 more
openalex   +1 more source

Incremental Label Distribution Learning with Scalable Graph Convolutional Networks [PDF]

open access: yesarXiv
Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and indicate the extent to which each label describes a given sample. Most existing LDL methods consider the number of labels to be static.
arxiv  

LDL-apheresis to decrease sFlt-1 during early severe preeclampsia: Report of two cases from a discontinued phase II trial.

open access: yesEuropean Journal of Obstetrics, Gynecology, and Reproductive Biology, 2018
B. Haddad   +15 more
semanticscholar   +1 more source

Lipoprotein apheresis in the management of severe hypercholesterolemia and of elevation of lipoprotein(a): current perspectives and patient selection

open access: yesMedical Devices: Evidence and Research, 2016
Ulrich Julius Lipidology and Center for Extracorporeal Therapy, Department for Internal Medicine III, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany Abstract: This review reports the current situation ...
Julius U
doaj  

Rethinking Label-specific Features for Label Distribution Learning [PDF]

open access: yesarXiv
Label distribution learning (LDL) is an emerging learning paradigm designed to capture the relative importance of labels for each instance. Label-specific features (LSFs), constructed by LIFT, have proven effective for learning tasks with label ambiguity by leveraging clustering-based prototypes for each label to re-characterize instances.
arxiv  

Changes in Lipoprotein Profile after Selective LDL Apheresis

open access: bronze, 2004
Tomohito Matsunaga   +4 more
openalex   +2 more sources

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