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He Y, Li Z, Niu Y, Duan Y, Wang Q, Liu X, Dong Z, Zheng Y, Chen Y, Wang Y, Zhao D, Sun X, Cai G, Feng Z, Zhang W, Chen X. Progress in the study of aging marker criteria in human populations. Front Public Health 2024; 12:1305303. [PMID: 38327568 PMCID: PMC10847233 DOI: 10.3389/fpubh.2024.1305303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
The use of human aging markers, which are physiological, biochemical and molecular indicators of structural or functional degeneration associated with aging, is the fundamental basis of individualized aging assessments. Identifying methods for selecting markers has become a primary and vital aspect of aging research. However, there is no clear consensus or uniform principle on the criteria for screening aging markers. Therefore, we combine previous research from our center and summarize the criteria for screening aging markers in previous population studies, which are discussed in three aspects: functional perspective, operational implementation perspective and methodological perspective. Finally, an evaluation framework has been established, and the criteria are categorized into three levels based on their importance, which can help assess the extent to which a candidate biomarker may be feasible, valid, and useful for a specific use context.
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Affiliation(s)
- Yan He
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zhe Li
- The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Yue Niu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Yuting Duan
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Qian Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xiaomin Liu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zheyi Dong
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Ying Zheng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Yizhi Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
- Department of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Hainan Province Academician Team Innovation Center, Sanya, China
| | - Yong Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Delong Zhao
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xuefeng Sun
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Guangyan Cai
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zhe Feng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Weiguang Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xiangmei Chen
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
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Basnet S, Mohanty C, Bochkov YA, Brockman-Schneider RA, Kendziorski C, Gern JE. Rhinovirus C causes heterogeneous infection and gene expression in airway epithelial cell subsets. Mucosal Immunol 2023; 16:386-398. [PMID: 36796588 PMCID: PMC10629931 DOI: 10.1016/j.mucimm.2023.01.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 01/27/2023] [Indexed: 02/16/2023]
Abstract
Rhinoviruses infect ciliated airway epithelial cells, and rhinoviruses' nonstructural proteins quickly inhibit and divert cellular processes for viral replication. However, the epithelium can mount a robust innate antiviral immune response. Therefore, we hypothesized that uninfected cells contribute significantly to the antiviral immune response in the airway epithelium. Using single-cell RNA sequencing, we demonstrate that both infected and uninfected cells upregulate antiviral genes (e.g. MX1, IFIT2, IFIH1, and OAS3) with nearly identical kinetics, whereas uninfected non-ciliated cells are the primary source of proinflammatory chemokines. Furthermore, we identified a subset of highly infectable ciliated epithelial cells with minimal interferon responses and determined that interferon responses originate from distinct subsets of ciliated cells with moderate viral replication. These findings suggest that the composition of ciliated airway epithelial cells and coordinated responses of infected and uninfected cells could determine the risk of more severe viral respiratory illnesses in children with asthma, chronic obstructive pulmonary disease, and genetically susceptible individuals.
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Affiliation(s)
- Sarmila Basnet
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
| | - Chitrasen Mohanty
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
| | - Yury A Bochkov
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | | | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
| | - James E Gern
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
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Robson B. Computers and preventative diagnosis. A survey with bioinformatics examples of mitochondrial small open reading frame peptides as portents of a new generation of powerful biomarkers. Comput Biol Med 2022; 140:105116. [PMID: 34896883 DOI: 10.1016/j.compbiomed.2021.105116] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 12/02/2021] [Indexed: 12/27/2022]
Abstract
The present brief survey is to alert developers in datamining, machine learning, inference methods, and other approaches in relation to diagnostic, predictive, and risk assessment medicine about a relatively new class of bioactive messaging peptides in which there is escalating interest. They provide patterns of communication and cross-chatter about states of health and disease within and, importantly, between cells (they also appear extracellularly in biological fluids). This chatter needs to be analyzed somewhat in the manner of the decryption of the Enigma code in the Second World War. It could lead not only to improved diagnosis but to predictive diagnosis, prediction of organ failure, and preventative medicine. This involves peptide products of short reading frames that have been previously somewhat neglected as unlikely gene products, with probably many examples in nuclear DNA, but certainly several known in the mitochondrial DNA. There is a great deal of knowledge now becoming available about the latter and itis believed thatthat the mRNA can be translated both by standard cytosolic and mitochondrial genetic codes, resulting in different peptides, adding a further level of complexity to the applications of bioinformatics and computational biology but a higher level of detail and sophistication to preventative diagnosis. The code to crack could be sophisticated and combinatorically complex to analyze by computers. Mitochondria may have combined with proto-eucaryotic cells some 2 billion years ago, only about a 7th of the age of the universe. Cells appeared some 2 billion years before that, also with possible signaling based on similar ideas. This makes life small in space but huge in time, refinement of which centrally involves these signaling processes.
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Affiliation(s)
- Barry Robson
- Ingine Inc. Viginia, USA and the Dirac Foundation OxfordShire UK.
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