Wei Sha
Impact in
- Infectious Diseases top 2%
- Tuberculosis Research and Epidemiology
- Rehabilitation top 2%
- Exercise and Physiological Responses
Papers in ⓘ
-
- Tuberculosis Research and Epidemiology 59
- Epidemiology 58
- Mycobacterium research and diagnosis 43
- Co-authors
- David C. Nieman (19 shared papers)Pedro Mendes (4 shared papers)Antonio D. Lassaletta (1 shared paper)John J. Tyson (1 shared paper)Jonathan D. Moore (1 shared paper)Jill C. Sible (1 shared paper)Katherine Chen (1 shared paper)Dru A. Henson (7 shared papers)
- Journals
- PLoS ONE (6 papers)The FASEB Journal (5 papers)Microbiology Spectrum (5 papers)International Journal of Infectious Diseases (4 papers)BMC Infectious Diseases (4 papers)
- Partner nations
- ChinaUnited StatesGermany
In The Last Decade
Wei Sha
174 papers receiving 3.9k citations
Peers
Comparison fields: 5 of 168
- Infectious Diseases 707
- Rehabilitation 218
- Molecular Biology 1.8k
- Epidemiology 690
- Cell Biology 322
Countries citing papers authored by Wei Sha
This map shows the geographic impact of Wei Sha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wei Sha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei Sha more than expected).
Fields of papers citing papers by Wei Sha
This network shows the impact of papers produced by Wei Sha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wei Sha. The network helps show where Wei Sha may publish in the future.
Co-authors
The 25 scholars most cited alongside Wei Sha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 190 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2002 | 382 | |
| 2 | 2013 | 183 | |
| 3 | 2003 | 140 | |
| 4 | 2010 | 131 | |
| 5 | 2009 | 116 | |
| 6 | 2018 | 106 | |
| 7 | 2012 | 103 | |
| 8 | 2010 | 98 | |
| 9 | 2013 | 96 | |
| 10 | 2010 | 93 | |
| 11 | 2012 | 88 | |
| 12 | 2011 | 82 | |
| 13 | 2013 | 76 | |
| 14 | 2021 | 69 | |
| 15 | 2017 | 69 | |
| 16 | 2019 | 64 | |
| 17 | Diagnostic value of adenosine deaminase in cerebrospinal fluid for tuberculous meningitis: a meta-analysis. | 2010 | 58 |
| 18 | 2014 | 57 | |
| 19 | 2011 | 57 | |
| 20 | 2015 | 55 |
About Wei Sha
Wei Sha is a scholar working on Infectious Diseases, Epidemiology, Molecular Biology, Surgery and Immunology, having authored 190 papers that have together received 3.9k indexed citations. Recurring topics across this work include Tuberculosis Research and Epidemiology (59 papers), Mycobacterium research and diagnosis (43 papers), Bryophyte Studies and Records (13 papers), Infectious Diseases and Tuberculosis (13 papers), Muscle metabolism and nutrition (8 papers), Gut microbiota and health (8 papers), Botany and Plant Ecology Studies (8 papers) and Exercise and Physiological Responses (8 papers). The work is most often cited by research in Infectious Diseases (707 citations), Rehabilitation (218 citations), Molecular Biology (1.8k citations), Epidemiology (690 citations) and Cell Biology (322 citations). Wei Sha has collaborated with scholars based in China, United States and Germany. Frequent co-authors include David C. Nieman, Pedro Mendes, Antonio D. Lassaletta, John J. Tyson, Jonathan D. Moore, Jill C. Sible, Katherine Chen, Dru A. Henson, Anthony A. Fodor and Keying Ye. Their work appears in journals such as PLoS ONE, The FASEB Journal, Microbiology Spectrum, International Journal of Infectious Diseases and BMC Infectious Diseases.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.