Masood Kamali‐Moghaddam

3.8k total citations
90 papers, 2.8k citations indexed

About

Masood Kamali‐Moghaddam is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Masood Kamali‐Moghaddam has authored 90 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 14 papers in Oncology and 11 papers in Immunology. Recurrent topics in Masood Kamali‐Moghaddam's work include Advanced biosensing and bioanalysis techniques (23 papers), Advanced Biosensing Techniques and Applications (16 papers) and Extracellular vesicles in disease (10 papers). Masood Kamali‐Moghaddam is often cited by papers focused on Advanced biosensing and bioanalysis techniques (23 papers), Advanced Biosensing Techniques and Applications (16 papers) and Extracellular vesicles in disease (10 papers). Masood Kamali‐Moghaddam collaborates with scholars based in Sweden, United States and Austria. Masood Kamali‐Moghaddam's co-authors include Ulf Landegren, Di Wu, Qiujin Shen, Spyros Darmanis, Ola Söderberg, Tim Conze, Junhong Yan, Rachel Yuan Nong, Måns Thulin and Lotta Wik and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Masood Kamali‐Moghaddam

86 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Masood Kamali‐Moghaddam Sweden 31 1.7k 406 370 279 210 90 2.8k
Li Ni China 35 1.4k 0.8× 307 0.8× 244 0.7× 295 1.1× 208 1.0× 137 3.7k
Henrique Girão Portugal 36 2.7k 1.5× 584 1.4× 311 0.8× 183 0.7× 159 0.8× 116 3.8k
Thomas Schubert Germany 32 1.5k 0.9× 199 0.5× 269 0.7× 202 0.7× 166 0.8× 111 2.9k
Santhi Gorantla United States 39 1.7k 1.0× 300 0.7× 497 1.3× 774 2.8× 198 0.9× 96 4.9k
Zhengping Xu China 36 1.9k 1.1× 766 1.9× 497 1.3× 227 0.8× 187 0.9× 105 3.7k
Andrew Lee United States 32 2.0k 1.2× 612 1.5× 257 0.7× 405 1.5× 469 2.2× 98 4.0k
Huajun Wang China 20 1.1k 0.7× 336 0.8× 117 0.3× 299 1.1× 186 0.9× 66 2.3k
Małgorzata Burek Germany 32 1.2k 0.7× 375 0.9× 265 0.7× 320 1.1× 698 3.3× 83 3.3k

Countries citing papers authored by Masood Kamali‐Moghaddam

Since Specialization
Citations

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

Fields of papers citing papers by Masood Kamali‐Moghaddam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Masood Kamali‐Moghaddam. 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 Masood Kamali‐Moghaddam. The network helps show where Masood Kamali‐Moghaddam may publish in the future.

Co-authorship network of co-authors of Masood Kamali‐Moghaddam

This figure shows the co-authorship network connecting the top 25 collaborators of Masood Kamali‐Moghaddam. A scholar is included among the top collaborators of Masood Kamali‐Moghaddam 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 Masood Kamali‐Moghaddam. Masood Kamali‐Moghaddam 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.
Wang, Mengqi, Masood Kamali‐Moghaddam, Liza Löf, et al.. (2024). Monitoring SARS-CoV-2 IgA, IgM and IgG antibodies in dried blood and saliva samples using antibody proximity extension assays (AbPEA). Scientific Reports. 14(1). 21655–21655.
2.
Palviainen, Mari, Johannes A. Eble, Masood Kamali‐Moghaddam, et al.. (2024). Beyond basic characterization and omics: Immunomodulatory roles of platelet‐derived extracellular vesicles unveiled by functional testing. Journal of Extracellular Vesicles. 13(10). e12513–e12513. 6 indexed citations
3.
4.
Neuvonen, Maarit, Erja Kerkelä, Kati Hyvärinen, et al.. (2023). OxLDL sensitizes platelets for increased formation of extracellular vesicles capable of finetuning macrophage gene expression. European Journal of Cell Biology. 102(2). 151311–151311. 10 indexed citations
5.
Johansson, Lars, Eldar Abdurakhmanov, Nils Landegren, et al.. (2022). Monitoring drug–target interactions through target engagement-mediated amplification on arrays and in situ. Nucleic Acids Research. 50(22). e129–e129. 3 indexed citations
6.
Fredolini, Claudia, R Gallini, Liza Löf, et al.. (2022). Surface protein profiling of prostate-derived extracellular vesicles by mass spectrometry and proximity assays. Communications Biology. 5(1). 1402–1402. 15 indexed citations
7.
Shen, Qiujin, Daniel Molin, Eva Freyhult, et al.. (2022). Plasma protein biomarker profiling reveals major differences between acute leukaemia, lymphoma patients and controls. New Biotechnology. 71. 21–29. 5 indexed citations
8.
9.
Shen, Qiujin, Karol Połom, Coralie Williams, et al.. (2019). A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer. EBioMedicine. 44. 322–333. 51 indexed citations
10.
Djureinovic, Dijana, et al.. (2019). Multiplex plasma protein profiling identifies novel markers to discriminate patients with adenocarcinoma of the lung. BMC Cancer. 19(1). 741–741. 11 indexed citations
11.
Shen, Qiujin, Fredrik Clausen, Måns Thulin, et al.. (2019). Monitoring of Protein Biomarkers of Inflammation in Human Traumatic Brain Injury Using Microdialysis and Proximity Extension Assay Technology in Neurointensive Care. Journal of Neurotrauma. 36(20). 2872–2885. 35 indexed citations
12.
Jin, Chunsheng, Johan Bylund, Qiujin Shen, et al.. (2019). Reduced sialyl-Lewisx on salivary MUC7 from patients with burning mouth syndrome. Molecular Omics. 15(5). 331–339. 9 indexed citations
13.
Birgisson, Helgi, Konstantinos Tsimogiannis, Eva Freyhult, & Masood Kamali‐Moghaddam. (2018). Plasma Protein Profiling Reveal Osteoprotegerin as a Marker of Prognostic Impact for Colorectal Cancer. Translational Oncology. 11(4). 1034–1043. 8 indexed citations
14.
Bränn, Emma, Emma Fransson, Richard White, et al.. (2018). Inflammatory markers in women with postpartum depressive symptoms. Journal of Neuroscience Research. 98(7). 1309–1321. 59 indexed citations
15.
Flowers, Sarah A., Agata Zieba, Chunsheng Jin, et al.. (2017). Lubricin binds cartilage proteins, cartilage oligomeric matrix protein, fibronectin and collagen II at the cartilage surface. Scientific Reports. 7(1). 13149–13149. 52 indexed citations
16.
Löf, Liza, Louise Dubois, Lotta Wik, et al.. (2016). Detecting individual extracellular vesicles using a multicolor in situ proximity ligation assay with flow cytometric readout. Scientific Reports. 6(1). 34358–34358. 53 indexed citations
17.
Elfineh, Lioudmila, et al.. (2014). Tyrosine phosphorylation profiling via in situproximity ligation assay. BMC Cancer. 14(1). 435–435. 17 indexed citations
18.
Hammond, Maria, Masood Kamali‐Moghaddam, & Ulf Landegren. (2013). A DNA-mediated search for optimal combinations of protein binders. SSM - Population Health. 3. 803–807. 1 indexed citations
19.
Friedman, Mikaela, Christian Jöst, Kai Johnsson, et al.. (2012). Protein tag-mediated conjugation of oligonucleotides to recombinant affinity binders for proximity ligation. New Biotechnology. 30(2). 144–152. 33 indexed citations
20.
Darmanis, Spyros, Rachel Yuan Nong, Maria Hammond, et al.. (2009). Sensitive Plasma Protein Analysis by Microparticle-based Proximity Ligation Assays. Molecular & Cellular Proteomics. 9(2). 327–335. 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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