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Chen B, Wang L, Wang L, Han Y, Yan G, Chen L, Li C, Zhu Y, Lu J, Han L. A Novel Data Fusion Strategy of GC-MS and 1H NMR Spectra for the Identification of Different Vintages of Maotai-flavor Baijiu. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024. [PMID: 38912709 DOI: 10.1021/acs.jafc.4c00607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.
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Affiliation(s)
- Biying Chen
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, P. R. China
| | - Li Wang
- Guotai Research Academy, Guizhou Guotai Liquor Group Co., Ltd., 1 Tingjiang Road, Tianjin 300410, P. R. China
| | - Liming Wang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, P. R. China
| | - Yueran Han
- Guotai Research Academy, Guizhou Guotai Liquor Group Co., Ltd., 1 Tingjiang Road, Tianjin 300410, P. R. China
| | - Guokai Yan
- Guizhou Guotai Liquor Group Co., Ltd., Renhuai 564500, P. R. China
| | - Liangjie Chen
- Guizhou Guotai Liquor Group Co., Ltd., Renhuai 564500, P. R. China
| | - Changwen Li
- Guotai Research Academy, Guizhou Guotai Liquor Group Co., Ltd., 1 Tingjiang Road, Tianjin 300410, P. R. China
| | - Yu Zhu
- Department of Clinical Laboratory, Nankai University Affiliated Third Central Hospital, Tianjin 300170, P. R. China
- Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center of Tianjin, Tianjin Institute of Hepatobiliary Disease, Tianjin 300170, P. R. China
| | - Jun Lu
- Guotai Research Academy, Guizhou Guotai Liquor Group Co., Ltd., 1 Tingjiang Road, Tianjin 300410, P. R. China
| | - Lifeng Han
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, P. R. China
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Danaeifar M, Najafi A. Artificial Intelligence and Computational Biology in Gene Therapy: A Review. Biochem Genet 2024:10.1007/s10528-024-10799-1. [PMID: 38635012 DOI: 10.1007/s10528-024-10799-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.
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Affiliation(s)
- Mohsen Danaeifar
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran
| | - Ali Najafi
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran.
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Guerrero L, Vindel-Alfageme J, Hierro L, Stark L, Vicent D, Sorzano CÓS, Corrales FJ. Discrimination of Etiologically Different Cholestasis by Modeling Proteomics Datasets. Int J Mol Sci 2024; 25:3684. [PMID: 38612495 PMCID: PMC11011353 DOI: 10.3390/ijms25073684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/22/2024] [Accepted: 03/24/2024] [Indexed: 04/14/2024] Open
Abstract
Cholestasis is characterized by disrupted bile flow from the liver to the small intestine. Although etiologically different cholestasis displays similar symptoms, diverse factors can contribute to the progression of the disease and determine the appropriate therapeutic option. Therefore, stratifying cholestatic patients is essential for the development of tailor-made treatment strategies. Here, we have analyzed the liver proteome from cholestatic patients of different etiology. In total, 7161 proteins were identified and quantified, of which 263 were differentially expressed between control and cholestasis groups. These differential proteins point to deregulated cellular processes that explain part of the molecular framework of cholestasis progression. However, the clustering of different cholestasis types was limited. Therefore, a machine learning pipeline was designed to identify a panel of 20 differential proteins that segregate different cholestasis groups with high accuracy and sensitivity. In summary, proteomics combined with machine learning algorithms provides valuable insights into the molecular mechanisms of cholestasis progression and a panel of proteins to discriminate across different types of cholestasis. This strategy may prove useful in developing precision medicine approaches for patient care.
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Affiliation(s)
- Laura Guerrero
- Centro Nacional de Biotecnología (CNB-CSIC), c/Darwin, 3, 28049 Madrid, Spain; (L.G.); (J.V.-A.); (C.Ó.S.S.)
| | - Jorge Vindel-Alfageme
- Centro Nacional de Biotecnología (CNB-CSIC), c/Darwin, 3, 28049 Madrid, Spain; (L.G.); (J.V.-A.); (C.Ó.S.S.)
| | - Loreto Hierro
- IdiPAZ, Instituto de Investigación Sanitaria (Health Research Institute), Hospital Universitario La Paz, Paseo de la Castellana 261, 28046 Madrid, Spain; (L.H.); (L.S.); (D.V.)
| | - Luiz Stark
- IdiPAZ, Instituto de Investigación Sanitaria (Health Research Institute), Hospital Universitario La Paz, Paseo de la Castellana 261, 28046 Madrid, Spain; (L.H.); (L.S.); (D.V.)
| | - David Vicent
- IdiPAZ, Instituto de Investigación Sanitaria (Health Research Institute), Hospital Universitario La Paz, Paseo de la Castellana 261, 28046 Madrid, Spain; (L.H.); (L.S.); (D.V.)
| | - Carlos Óscar S. Sorzano
- Centro Nacional de Biotecnología (CNB-CSIC), c/Darwin, 3, 28049 Madrid, Spain; (L.G.); (J.V.-A.); (C.Ó.S.S.)
| | - Fernando J. Corrales
- Centro Nacional de Biotecnología (CNB-CSIC), c/Darwin, 3, 28049 Madrid, Spain; (L.G.); (J.V.-A.); (C.Ó.S.S.)
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Ranathunge C, Patel SS, Pinky L, Correll VL, Chen S, Semmes OJ, Armstrong RK, Combs CD, Nyalwidhe JO. promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling. BIOINFORMATICS ADVANCES 2023; 3:vbad025. [PMID: 36922981 PMCID: PMC10010602 DOI: 10.1093/bioadv/vbad025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 02/22/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023]
Abstract
Summary We present promor, a comprehensive, user-friendly R package that streamlines label-free quantification proteomics data analysis and building machine learning-based predictive models with top protein candidates. Availability and implementation promor is freely available as an open source R package on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=promor) and distributed under the Lesser General Public License (version 2.1 or later). Development version of promor is maintained on GitHub (https://github.com/caranathunge/promor) and additional documentation and tutorials are provided on the package website (https://caranathunge.github.io/promor/). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Chathurani Ranathunge
- Eastern Virginia Medical School, School of Health Professions, Norfolk, VA 23501, USA
| | - Sagar S Patel
- Eastern Virginia Medical School, School of Health Professions, Norfolk, VA 23501, USA
| | - Lubna Pinky
- Eastern Virginia Medical School, School of Health Professions, Norfolk, VA 23501, USA
| | - Vanessa L Correll
- The Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA 23501, USA
| | - Shimin Chen
- The Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA 23501, USA
| | - O John Semmes
- The Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA 23501, USA
| | - Robert K Armstrong
- Eastern Virginia Medical School, School of Health Professions, Norfolk, VA 23501, USA.,Sentara Center for Simulation and Immersive Learning, Eastern Virginia Medical School, Norfolk, VA 23501, USA
| | - C Donald Combs
- Eastern Virginia Medical School, School of Health Professions, Norfolk, VA 23501, USA
| | - Julius O Nyalwidhe
- The Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA 23501, USA
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