Moa Sa

1.5k citations
8 papers · 765 indexed · 1 hit paper · h-index 6

Impact in

    • COVID-19 Clinical Research Studies
    • SARS-CoV-2 and COVID-19 Research
  • Neurology top 5%
    • Long-Term Effects of COVID-19

Papers in

    • SARS-CoV-2 and COVID-19 Research 3
    • COVID-19 Clinical Research Studies 3
    • Immune Cell Function and Interaction 3
    • T-cell and B-cell Immunology 2

Moa Sa

8 papers receiving 753 citations

Hit Papers

Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19 2020 · 530 citations
5302020202620222024100200300400500

Peers

Moa Sa
Comparison fields: 5 of 73
  • Infectious Diseases 507
  • Neurology 215
  • Immunology 229
  • Modeling and Simulation 22
  • Molecular Biology 229
Replace Rosemary Vergara with:
Rosemary Vergara United States
Hoyoung Lee South Korea
Wenqing Le China
Jiantao Dong China
Lorenzo Salvati Italy
Chikako Nakai-Murakami Japan
Meng‐Wei Zhuang China
Dalila Mele Italy
Simon Danisch Germany
Benjamin J. Meckiff United Kingdom
Moa Sa relative to Rosemary Vergara United States Rosemary Vergara's profile →
Citations per field
00.5×1.6×
Rosemary Vergara · 1×
Citations per year

Countries citing papers authored by Moa Sa

Since Specialization
Citations

This map shows the geographic impact of Moa Sa'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 Moa Sa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Moa Sa more than expected).

Fields of papers citing papers by Moa Sa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Moa Sa. 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 Moa Sa. The network helps show where Moa Sa may publish in the future.

Co-authors

The 25 scholars most cited alongside Moa Sa, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Moa Sa Line = papers co-authored together Moa Sa links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 202211
2 2021161
3 20211
4
Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19
Hit paper breakdown →
2020530
5 20201
6 201945
7 201810
8 20146

About Moa Sa

Moa Sa is a scholar working on Infectious Diseases, Immunology, Obstetrics and Gynecology, Neurology and Epidemiology, having authored 8 papers that have together received 765 indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (3 papers), COVID-19 Clinical Research Studies (3 papers), Immune Cell Function and Interaction (3 papers), Cytomegalovirus and herpesvirus research (2 papers), T-cell and B-cell Immunology (2 papers), RNA modifications and cancer (1 paper), RNA regulation and disease (1 paper) and CAR-T cell therapy research (1 paper). The work is most often cited by research in Infectious Diseases (507 citations), Neurology (215 citations), Immunology (229 citations), Modeling and Simulation (22 citations) and Molecular Biology (229 citations). Moa Sa has collaborated with scholars based in South Korea, Japan and Puerto Rico. Frequent co-authors include Su‐Hyung Park, Eui‐Cheol Shin, Hye Won Jeong, Sung‐Han Kim, Seong-Wan Park, Seong Jin Choi, Su Jin Jeong, Hoyoung Lee, Ji‐Soo Kwon and Jun Yong Choi. Their work appears in journals such as The Journal of Immunology, Immune Network, Science Immunology, Experimental & Molecular Medicine and Nature Communications.

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.

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