Results 151 to 160 of about 2,605,698 (332)

Modified low density lipoprotein isolated from atherosclerotic lesions does not cause lipid accumulation in aortic smooth muscle cells.

open access: yesJournal of Lipid Research, 1991
Foam cells in atherosclerotic lesions are derived not only from blood monocytes but also from smooth muscle cells (SMC). To better understand the mechanisms by which SMC may become lipid-laden, we have studied the catabolism by cultured rabbit aortic SMC
HF Hoff, JM Pepin, RE Morton
doaj  

Carbon Nanodots Attenuate Lipid Peroxidation in the LDL Receptor Knockout Mouse Brain. [PDF]

open access: yesAntioxidants (Basel), 2023
Erikson KM   +7 more
europepmc   +1 more source

Low density lipoprotein metabolism in the normal to moderately elevated range of plasma cholesterol: comparisons with familial hypercholesterolemia

open access: yesJournal of Lipid Research, 1983
Low density lipoprotein (LDL) metabolism was investigated using a pulse injection of 125I-labeled LDL in 20 subjects who did not have familial hypercholesterolemia (FH) (plasma cholesterol 160-297 mg/dl) and in 9 subjects who did have heterozygous FH ...
L A Simons, S Balasubramaniam, J Holland
doaj  

ANKS1A regulates LDL receptor-related protein 1 (LRP1)-mediated cerebrovascular clearance in brain endothelial cells. [PDF]

open access: yesNat Commun, 2023
Lee J   +8 more
europepmc   +1 more source

Identification of a novel Arg→Cys mutation in the LDL receptor that contributes to spontaneous hypercholesterolemia in pigs

open access: yesJournal of Lipid Research, 1999
We previously carried out genetic and metabolic studies in a partially inbred herd of pigs carrying cholesterol-elevating mutations. Quantitative pedigree analysis indicated that apolipoprotein (apo)B and a second major gene were responsible for the ...
Kurt A.A. Grunwald   +6 more
doaj  

Towards Better Performance in Incomplete LDL: Addressing Data Imbalance [PDF]

open access: yesarXiv
Label Distribution Learning (LDL) is a novel machine learning paradigm that addresses the problem of label ambiguity and has found widespread applications. Obtaining complete label distributions in real-world scenarios is challenging, which has led to the emergence of Incomplete Label Distribution Learning (InLDL).
arxiv  

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