TNFAIP1 promotes macrophage lipid accumulation and accelerates the development of atherosclerosis through the LEENE/FoxO1/ABCA1 pathway.

Macrophage lipid accumulation is a critical contributor to foam cell formation and atherosclerosis. Tumor necrosis factor-α-induced protein 1 (TNFAIP1) is closely associated with cardiovascular disease. However, its role and molecular mechanisms in atherogenesis remain unclear.

LncRNA KCNQ1OT1 knockdown inhibits ox-LDL-induced inflammatory response and oxidative stress in THP-1 macrophages through the miR-137/TNFAIP1 axis.

Both inflammatory response and oxidative stress are regarded as two critical contributors to atherosclerosis. Kcnq1 overlapping transcript 1 (KCNQ1OT1) is an imprinted antisense long non-coding RNA in the kcnq1 locus. Our previous study has demonstrated that KCNQ1OT1 aggravates atherosclerosis by promoting macrophage lipid accumulation.

Correlation between Quality and Geographical Origins of Revealed by Qualitative Fingerprint Profiling and Quantitative Determination of Triterpenoid Acids.

(Schw.) Wolf (PC) is a well-known saprophytic fungus, and its sclerotium without the epidermis (PCS) is widely used in traditional Chinese medicine and as a functional food in many countries. PCS is normally collected from multiple geographical regions, but whether and how the quality of PCS correlates with where it grows have not been determined.

Iridoids from the roots of Valeriana jatamansi Jones.

Five iridoids, named as chlorovaltrate P-T, together with six known analogues, (4β,8β)-8-methoxy-3-methoxy-10-methylene-2,9-dioxatricyclo[4.3.1.

[Studies on chemical constituents of Valeriana plants and their biological activities].

The recent progresses on chemical components and pharmacological activities of the genus Valerianawere summarized.Besides-essential oil, the chemical composition of Valerianais mainly focused on monoterpenoids,sesquiterpenoids,lignans, flavonoids, alkaloids, etc. Iridoids are the main chemical components ofmonoterpenoids.

PPI-IRO: a two-stage method for protein-protein interaction extraction based on interaction relation ontology.

Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identification of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifies and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method.

Interaction relation ontology learning.

Ontology is widely used in semantic computing and reasoning, and various biomedicine ontologies have become institutionalized to make the heterogeneous knowledge computationally amenable. Relation words, especially verbs, play an important role when describing the interaction between biological entities in molecular function, biological process, and cellular component; however, comprehensive research and analysis are still lacking. In this article, we propose an automatic method to build interaction relation ontology by investigating relation verbs, analyzing the syntactic relation of PubMed abstracts to perform relation vocabulary expansion, and integrating WordNet into our method to construct the hierarchy of relation vocabulary.