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Liu L, Shi LS, Hu CY, Gong T, Yang XY, Zhang CQ, Meng YH. Walnut protein isolate based emulsion as a promising delivery system enhanced lutein bioaccessibility. Int J Biol Macromol 2024; 275:133608. [PMID: 38960249 DOI: 10.1016/j.ijbiomac.2024.133608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/14/2024] [Accepted: 06/30/2024] [Indexed: 07/05/2024]
Abstract
Lutein, a natural pigment with multiple beneficial bioactivities, faces limitations in food processing due to its instability. In this study, we constructed four modified walnut protein isolate (WNPI) based emulsions as emulsion-based delivery systems (EBDS) for lutein fortification. The modification treatments enhanced the encapsulation efficiency of the WNPI-based EBDS on lutein. The modified WNPI-based EBDS exhibited improved storage and digestive stability, as well as increased lutein delivery capability in simulated gastrointestinal conditions. After in vitro digestion, the lutein retention in the modified WNPI-based EBDS was higher than in the untreated WNPI-based EBDS, with a maximum retention of 49.67 ± 1.10 % achieved after ultrasonic modification. Furthermore, the modified WNPI-based EBDS exhibited an elevated lutein bioaccessibility, reaching a maximum value of 40.49 ± 1.29 % after ultrasonic modification, nearly twice as high as the untreated WNPI-based EBDS. Molecular docking analysis indicated a robust affinity between WNPI and lutein, involving hydrogen bonds and hydrophobic interactions. Collectively, this study broadens WNPI's application and provides a foundation for fortifying other fat-soluble bioactive substances.
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Affiliation(s)
- Liang Liu
- Engineering Research Center for High-Valued Utilization of Fruit Resources in Western China, Ministry of Education; National Research & Development Center of Apple Processing Technology; College of Food Engineering and Nutritional Science, Shaanxi Normal University, 620 West Changan Avenue, Xian, Shaanxi 710119, PR China.
| | - Lin Shan Shi
- Engineering Research Center for High-Valued Utilization of Fruit Resources in Western China, Ministry of Education; National Research & Development Center of Apple Processing Technology; College of Food Engineering and Nutritional Science, Shaanxi Normal University, 620 West Changan Avenue, Xian, Shaanxi 710119, PR China.
| | - Ching Yuan Hu
- Engineering Research Center for High-Valued Utilization of Fruit Resources in Western China, Ministry of Education; National Research & Development Center of Apple Processing Technology; College of Food Engineering and Nutritional Science, Shaanxi Normal University, 620 West Changan Avenue, Xian, Shaanxi 710119, PR China; Department of Human Nutrition, Food and Animal Sciences, College of Tropical Agriculture and Human Resources, University of Hawaii at Manoa, 1955 East-West Road, AgSci. 415J, Honolulu, HI 96822, USA.
| | - Tian Gong
- Engineering Research Center for High-Valued Utilization of Fruit Resources in Western China, Ministry of Education; National Research & Development Center of Apple Processing Technology; College of Food Engineering and Nutritional Science, Shaanxi Normal University, 620 West Changan Avenue, Xian, Shaanxi 710119, PR China.
| | - Xue Yan Yang
- Engineering Research Center for High-Valued Utilization of Fruit Resources in Western China, Ministry of Education; National Research & Development Center of Apple Processing Technology; College of Food Engineering and Nutritional Science, Shaanxi Normal University, 620 West Changan Avenue, Xian, Shaanxi 710119, PR China.
| | - Chao Qun Zhang
- Engineering Research Center for High-Valued Utilization of Fruit Resources in Western China, Ministry of Education; National Research & Development Center of Apple Processing Technology; College of Food Engineering and Nutritional Science, Shaanxi Normal University, 620 West Changan Avenue, Xian, Shaanxi 710119, PR China.
| | - Yong Hong Meng
- Engineering Research Center for High-Valued Utilization of Fruit Resources in Western China, Ministry of Education; National Research & Development Center of Apple Processing Technology; College of Food Engineering and Nutritional Science, Shaanxi Normal University, 620 West Changan Avenue, Xian, Shaanxi 710119, PR China.
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Shen Y, Huang J, Jia L, Zhang C, Xu J. Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma. Biochem Biophys Rep 2024; 37:101587. [PMID: 38107663 PMCID: PMC10724547 DOI: 10.1016/j.bbrep.2023.101587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/01/2023] [Accepted: 11/17/2023] [Indexed: 12/19/2023] Open
Abstract
Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.
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Affiliation(s)
- Ye Shen
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
| | - Juanjie Huang
- Department of General Surgery, Dongguan Qingxi Hospital, Dongguan, 523660, China
| | - Lei Jia
- International Health Medicine Innovation Center, Shenzhen University, ShenZhen, 518060, China
| | - Chi Zhang
- Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, 528000, China
| | - Jianxing Xu
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
- Department of Radiology, The Wujin Clinical College of Xuzhou Medical University, Changzhou, 213002, China
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