D Bitter-Suermann

166 papers receiving 5.4k citations

Peers

D Bitter-Suermann
Comparison fields: 5 of 131
  • Immunology 2.1k
  • Microbiology 573
  • Endocrinology 384
  • Developmental Neuroscience 258
  • Hematology 641
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Citations per year

Countries citing papers authored by D Bitter-Suermann

Since Specialization
Citations

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

Fields of papers citing papers by D Bitter-Suermann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D Bitter-Suermann. 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 D Bitter-Suermann. The network helps show where D Bitter-Suermann may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 173 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1985365
2 1987280
3 1985195
4 1988173
5 1989148
6 1990142
7
Expression of neural cell adhesion molecule-related sialoglycoprotein in small cell lung cancer and neuroblastoma cell lines H69 and CHP-212.
1990140
8
Complement activation by the alternative pathway and macrophage enzyme secretion in the pathogenesis of chronic inflammation.
1977117
9 1974104
10 1986100
11 198795
12
Polysialic acid of the neural cell adhesion molecule distinguishes small cell lung carcinoma from carcinoids.
199194
13 197693
14 197487
15 198183
16 199182
17
Presence of the long chain form of polysialic acid of the neural cell adhesion molecule in Wilms' tumor. Identification of a cell adhesion molecule as an oncodevelopmental antigen and implications for tumor histogenesis.
198879
18 198577
19 198777
20 199175

About D Bitter-Suermann

D Bitter-Suermann is a scholar working on Immunology, Molecular Biology, Hematology, Radiology, Nuclear Medicine and Imaging and Genetics, having authored 173 papers that have together received 6.0k indexed citations. Recurring topics across this work include Complement system in diseases (65 papers), Monoclonal and Polyclonal Antibodies Research (32 papers), Coagulation, Bradykinin, Polyphosphates, and Angioedema (25 papers), Blood groups and transfusion (20 papers), Glycosylation and Glycoproteins Research (16 papers), Blood Coagulation and Thrombosis Mechanisms (15 papers), Bacterial Infections and Vaccines (13 papers) and Escherichia coli research studies (12 papers). The work is most often cited by research in Immunology (2.1k citations), Microbiology (573 citations), Endocrinology (384 citations), Developmental Neuroscience (258 citations) and Hematology (641 citations). D Bitter-Suermann has collaborated with scholars based in Germany, Switzerland and United States. Frequent co-authors include U. Hadding, G J Boulnois, Reinhard Bürger, Matthias Frosch, Jukka Finne, Christo Goridis, Volker Brade, H. U. Schorlemmer, K. N. Timmis and Hans‐Peter Hartung. Their work appears in journals such as The Journal of Immunology, European Journal of Immunology, Journal of Clinical Microbiology, Journal of Bacteriology 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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