Sharlene M. Day

12.3k citations
106 papers · 6.5k indexed · 7 hit papers · h-index 40

Sharlene M. Day

105 papers receiving 6.4k citations

Hit Papers

Clinical...162018202620202023200400600

Peers

Sharlene M. Day
Comparison fields: 5 of 137
  • Cardiology and Cardiovascular Medicine 4.8k
  • Developmental Neuroscience 269
  • Internal Medicine 125
  • Hematology 350
  • Molecular Biology 2.0k
Replace Jolanda van der Velden with:
Jolanda van der Velden Netherlands
Michiaki Hiroe Japan
Keiko Yamauchi‐Takihara Japan
Yong‐Jian Geng United States
Harald Tillmanns Germany
Burns C. Blaxall United States
M. Benjamin Perryman United States
David E. Gutstein United States
Toru Oka Japan
Anna Rosell Spain
Sharlene M. Day relative to Jolanda van der Velden Netherlands Jolanda van der Velden's profile →
Citations per field
00.5×7.8×
Jolanda van der Velden · 1×
Citations per year

Countries citing papers authored by Sharlene M. Day

Since Specialization
Citations

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

Fields of papers citing papers by Sharlene M. Day

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sharlene M. Day. 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 Sharlene M. Day. The network helps show where Sharlene M. Day may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Sharlene M. Day, 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 Sharlene M. Day Line = papers co-authored together Sharlene M. Day links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20234
2 20230
3 202127
4 202110
5
Genetic Testing for Inherited Cardiovascular Diseases: A Scientific Statement From the American Heart Associationbreakdown →
2020211
6 202049
7
2020 AHA/ACC Guideline for the Diagnosis and Treatment of Patients With Hypertrophic Cardiomyopathybreakdown →
2020408
8 202018
9 20207
10 201862
11 201733
12 201680
13 20164
14 201618
15 201513
16
Abstract 15498: Selective Inhibition of the Immunoproteasome Attenuates Adverse Left Ventricular Remodeling, Improves Cardiac Function, and Prevents Heart Failure After Myocardial Infarction
20131
17 201331
18 20135
19 200915
20 2005257

About Sharlene M. Day

Sharlene M. Day is a scholar working on Cardiology and Cardiovascular Medicine, Aging and Developmental Neuroscience, having authored 106 papers that have together received 6.5k indexed citations. Recurring topics across this work include Cardiomyopathy and Myosin Studies (75 papers), Cardiovascular Effects of Exercise (38 papers), Cardiovascular Function and Risk Factors (27 papers), Cardiac electrophysiology and arrhythmias (14 papers), Viral Infections and Immunology Research (13 papers), Cardiac pacing and defibrillation studies (11 papers), Congenital heart defects research (11 papers) and Congenital Heart Disease Studies (9 papers). The work is most often cited by research in Cardiology and Cardiovascular Medicine (4.8k citations), Developmental Neuroscience (269 citations) and Internal Medicine (125 citations). Sharlene M. Day has collaborated with scholars based in United States, Italy and United Kingdom. Frequent co-authors include Christopher Semsarian, Perry Elliott, Matthew W. Martinez, Martin S. Maron, Judy Hung, Michael A. Burke, Mark S. Link, M. Kittleson, Lauren L. Evanovich and José A. Joglar. Their work appears in journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

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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