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Sun X, Hu Y, Liu C, Zhang S, Yan S, Liu X, Zhao K. Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms. Foods 2024; 13:1420. [PMID: 38731791 PMCID: PMC11083255 DOI: 10.3390/foods13091420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024] Open
Abstract
Due to the significant price differences among different types of edible oils, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate, and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models all exceeded 95%. Moreover, the contribution rates of the OIRD signal, DC signal, and fundamental frequency signal to the classification results were 45.7%, 34.1%, and 20.2%, respectively. In a quality evaluation experiment on olive oil, the feature importance scores of three signals reached 63.4%, 18.9%, and 17.6%. The results suggested that the feature importance score of the OIRD signal was significantly higher than that of the DC and fundamental frequency signals. The experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.
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Affiliation(s)
- Xiaorong Sun
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Yiran Hu
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Cuiling Liu
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Shanzhe Zhang
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Sining Yan
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Xuecong Liu
- College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China;
| | - Kun Zhao
- College of New Energy and Materials, China University of Petroleum, Beijing 102249, China
- Key Laboratory of Oil and Gas Terahertz Spectroscopy and Photoelectric Detection, Petroleum and Chemical Industry Federation, China University of Petroleum, Beijing 102249, China
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Ding Y, Xing D, Fei Y, Lu B. Emerging degrader technologies engaging lysosomal pathways. Chem Soc Rev 2022; 51:8832-8876. [PMID: 36218065 PMCID: PMC9620493 DOI: 10.1039/d2cs00624c] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Indexed: 08/24/2023]
Abstract
Targeted protein degradation (TPD) provides unprecedented opportunities for drug discovery. While the proteolysis-targeting chimera (PROTAC) technology has already entered clinical trials and changed the landscape of small-molecule drugs, new degrader technologies harnessing alternative degradation machineries, especially lysosomal pathways, have emerged and broadened the spectrum of degradable targets. We have recently proposed the concept of autophagy-tethering compounds (ATTECs) that hijack the autophagy protein microtubule-associated protein 1A/1B light chain 3 (LC3) for targeted degradation. Other groups also reported degrader technologies engaging lysosomal pathways through different mechanisms including AUTACs, AUTOTACs, LYTACs and MoDE-As. In this review, we analyse and discuss ATTECs along with other lysosomal-relevant degrader technologies. Finally, we will briefly summarize the current status of these degrader technologies and envision possible future studies.
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Affiliation(s)
- Yu Ding
- Neurology Department at Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Life Sciences, Fudan University, Shanghai, China.
| | - Dong Xing
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China.
| | - Yiyan Fei
- Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Fudan University, Shanghai, China.
| | - Boxun Lu
- Neurology Department at Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Life Sciences, Fudan University, Shanghai, China.
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Pei J, Wang G, Feng L, Zhang J, Jiang T, Sun Q, Ouyang L. Targeting Lysosomal Degradation Pathways: New Strategies and Techniques for Drug Discovery. J Med Chem 2021; 64:3493-3507. [PMID: 33764774 DOI: 10.1021/acs.jmedchem.0c01689] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
A series of tools for targeted protein degradation are inspiring scientists to develop new drugs with advantages over traditional small-molecule drugs. Among these tools, proteolysis-targeting chimeras (PROTACs) are most representative of the technology based on proteasomes. However, the proteasome has little degradation effect on certain macromolecular proteins or aggregates, extracellular proteins, and organelles, which limits the application of PROTACs. Additionally, lysosomes play an important role in protein degradation. Therefore, lysosome-induced protein degradation drugs can directly regulate protein levels in vivo, achieve the goal of treating diseases, and provide new strategies for drug discovery. Lysosome-based degradation technology has the potential for clinical translation. In this review, strategies targeting lysosomal pathways and lysosome-based degradation techniques are summarized. In addition, lysosome-based degrading drugs are described, and the advantages and challenges are listed. Our efforts will certainly promote the design, discovery, and clinical application of drugs associated with this technology.
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Affiliation(s)
- Junping Pei
- State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu 610041, China
| | - Guan Wang
- State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu 610041, China
| | - Lu Feng
- State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu 610041, China
| | - Jifa Zhang
- State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu 610041, China
| | - Tingting Jiang
- State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu 610041, China
| | - Qiu Sun
- State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu 610041, China
| | - Liang Ouyang
- State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu 610041, China
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Qi H, Wang F, Tao SC. Proteome microarray technology and application: higher, wider, and deeper. Expert Rev Proteomics 2019; 16:815-827. [PMID: 31469014 DOI: 10.1080/14789450.2019.1662303] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray. Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology. Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.
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Affiliation(s)
- Huan Qi
- Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University , Shanghai , China
| | - Fei Wang
- School of Pharmacy, Shanghai Jiao Tong University , Shanghai , China
| | - Sheng-Ce Tao
- Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University , Shanghai , China
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