RESUMO
Three new caryophyllene-type sesquiterpenoid glycosides were isolated from Biebersteinia heterostemon. Their structures were elucidated by comprehensive analysis of NMR and MS spectroscopic data. All the isolated compounds were evaluated for MCF-7/TAM cytotoxic activity. The results indicated that compound 1 was found to exhibit the weak cytotoxicity against MCF-7/TAM with the IC50 value of 106.4 ± 0.04 µM.
RESUMO
Two undescribed steroids, named (15 R)-2,15-dihydroxypregna-1,4-dien-3,16-dione (1) and 2,15-dihydroxypregna-1,4,14-trien-3,16-dione (2), were isolated from the aerial parts of Munronia pinnata (Wall.) W. Theob. The structure elucidation of two compounds was performed by using spectroscopic methods and comparing the literature. Compound 2 exhibited inhibitory effect against PTP-1B with an IC50 value of 152.07 ± 3.33 µM, and compound 1 was inactive.
RESUMO
Two new norcassane-type diterpenoids, named 6ß-hydroxy-bisnorcass-13-en-12-one (1) and 6ß-hydroxy-bisnorcassan-12-one (2), were isolated from the seeds of Mezonevron sinense Hemsl. The structures of compounds 1-2 were determined by extensive spectroscopic analysis. Two compounds exhibited immunosuppressive activity with the IC50 values of 19.35 ± 0.87 µM and 18.69 ± 0.88 µM in the ConA induced T cell model and 65.04 ± 0.83 µM and 48.06 ± 0.76 µM in LPS induced B cell model, respectively.
RESUMO
As a typical knowledge-intensive industry, the medical field uses knowledge graph technology to construct causal inference calculations, such as "symptom-disease", "laboratory examination/imaging examination-disease", and "disease-treatment method". The continuous expansion of large electronic clinical records provides an opportunity to learn medical knowledge by machine learning. In this process, how to extract entities with a medical logic structure and how to make entity extraction more consistent with the logic of the text content in electronic clinical records are two issues that have become key in building a high-quality, medical knowledge graph. In this work, we describe a method for extracting medical entities using real Chinese clinical electronic clinical records. We define a computational architecture named MLEE to extract object-level entities with "object-attribute" dependencies. We conducted experiments based on randomly selected electronic clinical records of 1,000 patients from Shengjing Hospital of China Medical University to verify the effectiveness of the method.
