Connie M. Westhoff

6.2k total citations · 1 hit paper
143 papers, 3.8k citations indexed

About

Connie M. Westhoff is a scholar working on Hematology, Physiology and Genetics. According to data from OpenAlex, Connie M. Westhoff has authored 143 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 116 papers in Hematology, 105 papers in Physiology and 60 papers in Genetics. Recurrent topics in Connie M. Westhoff's work include Blood groups and transfusion (115 papers), Erythrocyte Function and Pathophysiology (102 papers) and Hemoglobinopathies and Related Disorders (57 papers). Connie M. Westhoff is often cited by papers focused on Blood groups and transfusion (115 papers), Erythrocyte Function and Pathophysiology (102 papers) and Hemoglobinopathies and Related Disorders (57 papers). Connie M. Westhoff collaborates with scholars based in United States, France and Canada. Connie M. Westhoff's co-authors include Stella T. Chou, Sunitha Vege, David F. Friedman, J. Kevin Foskett, Tannoa Jackson, Marion E. Reid, Kim Smith‐Whitley, Don‐On Daniel Mak, Christine Lomas‐Francis and Shigehisa Hirose and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Connie M. Westhoff

134 papers receiving 3.7k citations

Hit Papers

American Society of Hemat... 2020 2026 2022 2024 2020 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Connie M. Westhoff United States 32 2.5k 2.1k 1.6k 652 510 143 3.8k
Baya Chérif‐Zahar France 28 2.1k 0.9× 1.6k 0.8× 553 0.4× 953 1.5× 585 1.1× 56 3.3k
Isabelle Mouro-Chanteloup France 28 1.3k 0.5× 1.2k 0.6× 369 0.2× 588 0.9× 343 0.7× 57 2.1k
Henri Wajcman France 33 2.5k 1.0× 1.6k 0.8× 3.6k 2.3× 1.4k 2.1× 562 1.1× 275 5.7k
Sabra C. Goff United States 21 1.4k 0.5× 653 0.3× 1.9k 1.2× 1.8k 2.8× 676 1.3× 26 4.0k
R L Nagel United States 27 1.2k 0.5× 764 0.4× 1.8k 1.2× 588 0.9× 257 0.5× 79 2.8k
SH Orkin United States 32 1.7k 0.7× 809 0.4× 1.3k 0.8× 3.4k 5.3× 992 1.9× 50 6.0k
T. H. J. Huisman United States 48 4.1k 1.6× 1.7k 0.8× 6.1k 3.9× 1.6k 2.4× 701 1.4× 361 8.4k
Michaëla Fontenay France 35 2.1k 0.8× 311 0.1× 945 0.6× 2.3k 3.6× 269 0.5× 142 4.5k
Drorit Neumann Israel 31 971 0.4× 479 0.2× 347 0.2× 1.3k 2.0× 308 0.6× 117 3.3k
G. Stamatoyannopoulos United States 36 1.3k 0.5× 831 0.4× 2.0k 1.3× 1.6k 2.5× 585 1.1× 108 3.5k

Countries citing papers authored by Connie M. Westhoff

Since Specialization
Citations

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

Fields of papers citing papers by Connie M. Westhoff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Connie M. Westhoff

This figure shows the co-authorship network connecting the top 25 collaborators of Connie M. Westhoff. A scholar is included among the top collaborators of Connie M. Westhoff based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Connie M. Westhoff. Connie M. Westhoff is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Israelyan, Narek, Sunitha Vege, David F. Friedman, et al.. (2024). RH genotypes and red cell alloimmunization rates in chronically transfused patients with sickle cell disease: A multisite study in the USA. Transfusion. 64(3). 526–535. 6 indexed citations
2.
Lomas‐Francis, Christine, et al.. (2023). A novel high‐prevalence antigen in the Lutheran system, LUGA (LU24), and an updated, full‐length 3D BCAM model. Transfusion. 63(4). 798–807. 4 indexed citations
3.
Takasaki, Kaoru, et al.. (2023). Variant RHD alleles and Rh immunization in patients with sickle cell disease. British Journal of Haematology. 201(6). 1220–1228. 10 indexed citations
5.
Lomas‐Francis, Christine, et al.. (2022). Two new Scianna variants causing loss of high prevalence antigens: ERMAP model and 3D analysis of the antigens. Transfusion. 63(1). 230–238. 3 indexed citations
7.
Thonier, Vincent, France Pirenne, Sunitha Vege, et al.. (2022). First report of a null allele on a GYPB*s background: GYPB*s(37 + 4_8delAGTGA). Transfusion. 62(5). E24–E26.
8.
Vege, Sunitha, et al.. (2021). An intron c.149‐2632T>A change in RHD is associated with aberrant transcription and very weak D phenotype. Transfusion. 62(2). E14–E16. 3 indexed citations
9.
Vege, Sunitha, et al.. (2021). Five novel FY null alleles associated with typing discrepancies. Transfusion. 61(11). E80–E82.
10.
Gowda, Lohith, Sunitha Vege, Debra Kessler, Beth H. Shaz, & Connie M. Westhoff. (2021). Screening of blood donors for sickle cell trait using a DNA‐based approach: Frequency in a multiethnic donor population. Transfusion. 61(7). 2008–2013. 6 indexed citations
11.
Zhang, Zhe, Sunitha Vege, Taishan Hu, et al.. (2021). Accurate long-read sequencing allows assembly of the duplicated RHD and RHCE genes harboring variants relevant to blood transfusion. The American Journal of Human Genetics. 109(1). 180–191. 20 indexed citations
12.
Lomas‐Francis, Christine, et al.. (2021). A novel P1PK allele in two Bangladeshi sisters with a history of spontaneous abortion: A4GALT*02N(951C). Transfusion. 61(10). E71–E72. 2 indexed citations
13.
Vege, Sunitha, et al.. (2021). An insertion/deletion polymorphism in the KLF1 gene resulting in an In(Lu) phenotype. Transfusion. 61(10). E73–E74. 2 indexed citations
14.
Chou, Stella T., Mouaz Alsawas, Ross M. Fasano, et al.. (2020). American Society of Hematology 2020 guidelines for sickle cell disease: transfusion support. Blood Advances. 4(2). 327–355. 268 indexed citations breakdown →
15.
Lane, William J., Nicholas Gleadall, Sunitha Vege, et al.. (2020). Multiple GYPB gene deletions associated with the U− phenotype in those of African ancestry. Transfusion. 60(6). 1294–1307. 10 indexed citations
16.
Westhoff, Connie M.. (2019). Blood group genotyping. Blood. 133(17). 1814–1820. 63 indexed citations
17.
Guo, Yuelong, Michael P. Busch, Mark Seielstad, et al.. (2018). Development and evaluation of a transfusion medicine genome wide genotyping array. Transfusion. 59(1). 101–111. 26 indexed citations
18.
Posocco, David, Jean Ann Maguire, Deborah L. French, et al.. (2017). Customized Induced Pluripotent Stem Cell-Derived Red Cell Reagents. Blood. 130. 3–3. 2 indexed citations
19.
Gruswitz, Franz, Sarika Chaudhary, Joseph D. Ho, et al.. (2010). Function of human Rh based on structure of RhCG at 2.1 Å. Proceedings of the National Academy of Sciences. 107(21). 9638–9643. 163 indexed citations
20.
Mak, Don‐On Daniel, et al.. (2005). Characterization of ammonia transport by the kidney Rh glycoproteins RhBG and RhCG. American Journal of Physiology-Renal Physiology. 290(2). F297–F305. 98 indexed citations

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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