 Age Factors
"Age Factors" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.
| Descriptor ID |
D000367
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| MeSH Number(s) |
N05.715.350.075 N06.850.490.250
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| Concept/Terms |
Age Factors- Age Factors
- Age Factor
- Factor, Age
- Factors, Age
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Below are MeSH descriptors whose meaning is more general than "Age Factors".
Below are MeSH descriptors whose meaning is more specific than "Age Factors".
This graph shows the total number of publications written about "Age Factors" by people in this website by year, and whether "Age Factors" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 1980 | 0 | 7 | 7 | | 1981 | 0 | 7 | 7 | | 1982 | 0 | 10 | 10 | | 1983 | 0 | 5 | 5 | | 1984 | 0 | 6 | 6 | | 1985 | 0 | 12 | 12 | | 1986 | 0 | 12 | 12 | | 1987 | 0 | 8 | 8 | | 1988 | 0 | 4 | 4 | | 1989 | 0 | 16 | 16 | | 1990 | 0 | 10 | 10 | | 1991 | 0 | 12 | 12 | | 1992 | 0 | 13 | 13 | | 1993 | 0 | 13 | 13 | | 1994 | 0 | 23 | 23 | | 1995 | 0 | 20 | 20 | | 1996 | 0 | 20 | 20 | | 1997 | 0 | 21 | 21 | | 1998 | 0 | 22 | 22 | | 1999 | 0 | 23 | 23 | | 2000 | 0 | 33 | 33 | | 2001 | 0 | 20 | 20 | | 2002 | 0 | 29 | 29 | | 2003 | 0 | 26 | 26 | | 2004 | 0 | 33 | 33 | | 2005 | 1 | 51 | 52 | | 2006 | 0 | 39 | 39 | | 2007 | 1 | 43 | 44 | | 2008 | 2 | 67 | 69 | | 2009 | 2 | 47 | 49 | | 2010 | 1 | 65 | 66 | | 2011 | 0 | 69 | 69 | | 2012 | 0 | 64 | 64 | | 2013 | 0 | 65 | 65 | | 2014 | 0 | 92 | 92 | | 2015 | 1 | 78 | 79 | | 2016 | 3 | 65 | 68 | | 2017 | 0 | 57 | 57 | | 2018 | 1 | 70 | 71 | | 2019 | 0 | 42 | 42 | | 2020 | 0 | 29 | 29 |
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Below are the most recent publications written about "Age Factors" by people in Profiles.
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Kwan JYY, Famiyeh P, Su J, Xu W, Kwan BYM, Jones JM, Chang E, Yip KW, Liu FF. Development and Validation of a Risk Model for Breast Cancer-Related Lymphedema. JAMA Netw Open. 2020 11 02; 3(11):e2024373.
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Adegunsoye A, Ventura IB, Liarski VM. Association of Black Race with Outcomes in COVID-19 Disease: A Retrospective Cohort Study. Ann Am Thorac Soc. 2020 10; 17(10):1336-1339.
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Chaudhry F, Bulka H, Rathnam AS, Said OM, Lin J, Lorigan H, Bernitsas E, Rube J, Korzeniewski SJ, Memon AB, Levy PD, Schultz L, Javed A, Lisak R, Cerghet M. COVID-19 in multiple sclerosis patients and risk factors for severe infection. J Neurol Sci. 2020 Nov 15; 418:117147.
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Liu D, Zhou D, Sun Y, Zhu J, Ghoneim D, Wu C, Yao Q, Gamazon ER, Cox NJ, Wu L. A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk. Cancer Res. 2020 10 15; 80(20):4346-4354.
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Heald-Sargent T, Muller WJ, Zheng X, Rippe J, Patel AB, Kociolek LK. Age-Related Differences in Nasopharyngeal Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Levels in Patients With Mild to Moderate Coronavirus Disease 2019 (COVID-19). JAMA Pediatr. 2020 09 01; 174(9):902-903.
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Payne AB, Mehal JM, Chapman C, Haberling DL, Richardson LC, Bean CJ, Hooper WC. Trends in Sickle Cell Disease-Related Mortality in the United States, 1979 to 2017. Ann Emerg Med. 2020 09; 76(3S):S28-S36.
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Bhasin A, Nam H, Yeh C, Lee J, Liebovitz D, Achenbach C. Is BMI Higher in Younger Patients with COVID-19? Association Between BMI and COVID-19 Hospitalization by Age. Obesity (Silver Spring). 2020 10; 28(10):1811-1814.
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Wu FL, Strand AI, Cox LA, Ober C, Wall JD, Moorjani P, Przeworski M. A comparison of humans and baboons suggests germline mutation rates do not track cell divisions. PLoS Biol. 2020 08; 18(8):e3000838.
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Layden EA, Li H, Schertz KE, Berman MG, London SE. Experience selectively alters functional connectivity within a neural network to predict learned behavior in juvenile songbirds. Neuroimage. 2020 11 15; 222:117218.
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Toyoshima Y, Nemoto K, Matsumoto S, Nakamura Y, Kiyotani K. SARS-CoV-2 genomic variations associated with mortality rate of COVID-19. J Hum Genet. 2020 Dec; 65(12):1075-1082.
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