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为开发具有更高储存稳定性和生物利用度的负载DHA藻油的脂质体粉末，以DHA藻油为原料，首先利用大豆卵磷脂制备负载DHA藻油的纳米脂质体悬液，继而分别使用3种壁材(麦芽糊精、羟丙基-β-环糊精及二者质量比为1∶ 1的混合物)对脂质体悬液包埋后进行喷雾干燥，制得DHA藻油纳米脂质体粉末，最后比较了3种壁材对脂质体粉末氧化稳定性、体外消化和细胞摄取的影响。结果表明：DHA藻油脂质体悬液制备的最佳条件为大豆卵磷脂与DHA藻油质量比3∶ 1、超声时间1 min、旋蒸温度45 ℃、PBS的pH 7.0、PBS浓度0.02 mol/L、PBS用量10 mL（大豆卵磷脂与藻油总质量0.4 g时），在此条件下DHA藻油脂质体悬液的包封率为88.01%；3种脂质体粉末粒径范围在（253.87±1.96）~（408.80±1.23）nm之间，Zeta电位在 （- 40.80±0.78）~（-34.87±0.25） mV之间；羟丙基-β-环糊精与麦芽糊精混合物为壁材时，DHA藻油纳米脂质体粉末具有更高的氧化稳定性、更低的水分含量和更慢的脂肪酸体外消化释放率； 3种脂质体粉末使Caco-2细胞中SLC27 A4、FABP4和CD36基因表达增加，说明脂质体粉末均具有良好的细胞摄取。综上，基于大豆卵磷脂制备的DHA藻油纳米脂质体及喷雾干燥技术有效改善了DHA藻油的氧化稳定性。In order to develop DHA algal oil-loaded liposome powder with higher storage stability and bioavailability, the liposome suspension loaded with DHA algal oil was prepared by using DHA algal oil and soy lecithin as raw materials, and then three kinds of wall materials (maltodextrin, hydroxypropyl-β-cyclodextrin and their mixture with a mass ratio of 1∶ 1) were used to embed the liposome suspension and spray drying to obtain powdered-nanoliposome loaded with DHA algal oil. In addition, the effects of the three wall materials on the oxidative stability, in vitro digestibility and cellular uptake of the liposome powder were compared. The results showed that the optimal conditions for the preparation of DHA algal liposome suspension were as follows: mass ratio of soy lecithin to algal oil 3∶ 1, ultrasonic time 1 min, evaporation temperature 45 ℃, PBS pH 7.0, PBS concentration 0.02 mol/L, PBS dosage 10 mL (mass of soy lecithin and DHA algal oil 0.4 g). Under the optimal conditions, the encapsulation rate of DHA algal oil liposome suspension was 8801%. The particle size range of the three liposome powders was between (253.87±1.96) - (408.80±1.23) nm, and the Zeta potential was (-40.80±0.78)-(-34.87±0.25) mV. When the mixture of hydroxypropyl-β-cyclodextrin and maltodextrin was used as wall material, powdered-nanoliposome loaded with DHA algal oil had higher oxidation stability, lower water content and slower in vitro digestion release rate of fatty acids. The three liposome powder increased the expression of SLC27 A4, FABP4 and CD36 genes in Caco-2 cells, indicating that the liposome powders had good cell uptake. In conclusion, DHA algal oil nanoliposomes prepared based on soy lecithin and spray drying technology can effectively improve the oxidative stability of DHA algal oil.

摘要：金属有机骨架（MOF）具有多样性、高比表面积、化学稳定性和易于后修饰等优势，是固定化酶的优良载体。为了促进生物柴油的发展，针对MOF固定化脂肪酶的方法及其在生物柴油生产中的应用进展进行了综述。MOF固定化脂肪酶的方法有物理吸附法、共价结合法、原位包埋法等，采用MOF固定脂肪酶生产生物柴油，在一定程度上降低了生产成本和提高了产率。开发新型的脂肪酶固定化方法及寻求合适的固定化载体是生物柴油发展的关键。 Metal organic framework (MOF) is an excellent carrier for immobilized enzymes due to its advantages of diversity, high specific surface area, chemical stability and easy post-modification. In order to promote the development of biodiesel, the methods of immobilizing lipase on MOF and its application progress on biodiesel preparation process was reviewed. The methods of immobilizing lipase on MOF include physical adsorption, covalent binding, in-situ embedding, etc. The use of MOF to immobilize lipase for biodiesel production has reduced the production cost and improved the yield to a certain extent. The development of novel immobilization methods for lipase and the search for suitable immobilization carriers have become the key to the development of biodiesel.

Osteoporosis is a major health concern worldwide. The present study aimed to identify effective biomarkers for osteoporosis detection. In osteoporosis, 559 differentially expressed genes (DEGs) were enriched in PI3K-Akt signaling pathway and Foxo signaling pathway. Weighted gene co-expression network analysis showed that green, pink, and tan modules were clinically significant modules, and that six genes (VEGFA, DDX5, SOD2, HNRNPD, EIF5B, and HSP90B1) were identified as “real” hub genes in the protein–protein interaction network, co-expression network, and 559 DEGs. The sensitivity and specificity of the support vector machine (SVM) for identifying patients with osteoporosis was 100%, with an area under curve of 1 in both training and validation datasets. Our results indicated that the current system using the SVM method could identify patients with osteoporosis.