Mario van der Stelt

9.9k total citations · 2 hit papers
139 papers, 6.2k citations indexed

About

Mario van der Stelt is a scholar working on Pharmacology, Molecular Biology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Mario van der Stelt has authored 139 papers receiving a total of 6.2k indexed citations (citations by other indexed papers that have themselves been cited), including 86 papers in Pharmacology, 58 papers in Molecular Biology and 42 papers in Cellular and Molecular Neuroscience. Recurrent topics in Mario van der Stelt's work include Cannabis and Cannabinoid Research (82 papers), Neuroscience and Neuropharmacology Research (26 papers) and Pancreatic function and diabetes (24 papers). Mario van der Stelt is often cited by papers focused on Cannabis and Cannabinoid Research (82 papers), Neuroscience and Neuropharmacology Research (26 papers) and Pancreatic function and diabetes (24 papers). Mario van der Stelt collaborates with scholars based in Netherlands, United States and Italy. Mario van der Stelt's co-authors include Vincenzo Di Marzo, Mauro Maccarrone, Gerrit A. Veldink, Marc P. Baggelaar, Herman S. Overkleeft, Klaas Nicolay, Wouter B. Veldhuis, P.R. Bär, Beat Lutz and Johannes F.G. Vliegenthart and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Mario van der Stelt

132 papers receiving 6.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

CB1 Cannabinoid Receptors and On-Demand Defense Against Excitotoxicity

2003 978 citations Mario van der Stelt et al. profile →
Goods and Bads of the Endocannabinoid System as a Therapeutic Target: Lessons Learned after 30 Years

2023 84 citations Mauro Maccarrone, Uwe Grether et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mario van der Stelt Netherlands	41	4.0k	2.6k	1.5k	878	779	139	6.2k
Maria Grazia Cascio Italy	36	5.2k 1.3×	3.3k 1.2×	1.0k 0.7×	1.4k 1.6×	641 0.8×	67	6.5k
Patricia H. Reggio United States	44	4.0k 1.0×	2.9k 1.1×	2.6k 1.7×	401 0.5×	320 0.4×	132	5.9k
Mary E. Abood United States	52	6.7k 1.7×	4.5k 1.7×	2.7k 1.8×	1.1k 1.2×	667 0.9×	130	9.1k
Ruth A. Ross United Kingdom	45	7.9k 2.0×	4.5k 1.7×	2.0k 1.3×	1.5k 1.7×	1.1k 1.4×	111	9.8k
Michelle Glass New Zealand	49	6.5k 1.6×	5.0k 1.9×	1.9k 1.3×	1.1k 1.2×	532 0.7×	142	8.9k
Graeme Griffin United States	21	6.3k 1.6×	3.5k 1.3×	1.5k 1.0×	1.3k 1.4×	614 0.8×	28	8.0k
Alice C. Young United States	11	3.8k 1.0×	3.4k 1.3×	2.5k 1.7×	852 1.0×	536 0.7×	11	6.4k
Dana E. Selley United States	45	3.7k 0.9×	4.7k 1.8×	2.9k 2.0×	814 0.9×	1.2k 1.6×	142	7.5k
Takayuki Sugiura Japan	51	6.5k 1.6×	3.0k 1.1×	2.8k 1.9×	1.5k 1.7×	1.2k 1.6×	130	10.2k
Marı́a L. de Ceballos Spain	33	2.0k 0.5×	2.0k 0.8×	1.6k 1.1×	514 0.6×	996 1.3×	88	5.0k

Countries citing papers authored by Mario van der Stelt

Since Specialization

Citations

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

Fields of papers citing papers by Mario van der Stelt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mario van der Stelt. 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 Mario van der Stelt. The network helps show where Mario van der Stelt may publish in the future.

Co-authorship network of co-authors of Mario van der Stelt

This figure shows the co-authorship network connecting the top 25 collaborators of Mario van der Stelt. A scholar is included among the top collaborators of Mario van der Stelt 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 Mario van der Stelt. Mario van der Stelt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Stelt, Mario van der, et al.. (2025). Lipid Metabolism and Immune Function: Chemical Tools for Insights into T‐Cell Biology. ChemBioChem. 26(11). e202400980–e202400980. 1 indexed citations

2.

Sánchez‐López, Elena, Zhixiong Ying, Mariëtte R. Boon, et al.. (2025). Inhibition of endocannabinoid synthesis enzymes DAGL and NAPE-PLD transiently lowers body weight and alters glucose homeostasis during a high-fat diet challenge in mice. European Journal of Endocrinology. 193(5). 518–526.

3.

Wel, Tom van der, et al.. (2024). Previously Published Phosphatase Probes have Limited Utility Due to their Unspecific Reactivity. ChemBioChem. 25(22). e202400333–e202400333.

4.

An, Yu, et al.. (2024). Discovery of a Photoaffinity Probe that Captures the Active Conformation of the Cannabinoid CB₂ Receptor. ChemBioChem. 25(7). e202300785–e202300785. 1 indexed citations

5.

Shukla, Rhythm, Panagiotis I. Koukos, M. van den Nieuwboer, et al.. (2023). A novel antifolate suppresses growth of FPGS-deficient cells and overcomes methotrexate resistance. Life Science Alliance. 6(11). e202302058–e202302058. 3 indexed citations

6.

Petrie, Gavin N., Georgia Balsevich, Tamás Füzesi, et al.. (2023). Disruption of tonic endocannabinoid signalling triggers cellular, behavioural and neuroendocrine responses consistent with a stress response. British Journal of Pharmacology. 180(24). 3146–3159. 10 indexed citations

7.

Li, Xiaoting, Partha Mukhopadhyay, Mohammed Ahmed Mustafa, et al.. (2023). Structural basis of selective cannabinoid CB2 receptor activation. Acta Crystallographica Section A Foundations and Advances. 79(a2). C1059–C1059. 2 indexed citations

8.

Zhong, Zheng, et al.. (2022). Synthetic studies with the brevicidine and laterocidine lipopeptide antibiotics including analogues with enhanced properties and in vivo efficacy. Chemical Science. 13(12). 3563–3570. 23 indexed citations

9.

Colangeli, Roberto, Maria Morena, Roger Thompson, et al.. (2022). 2-AG-Mediated Control of GABAergic Signaling Is Impaired in a Model of Epilepsy. Journal of Neuroscience. 43(4). 571–583. 7 indexed citations

10.

Overkleeft, Herman S., et al.. (2022). Live‐Cell Imaging of Sterculic Acid—a Naturally Occurring 1,2‐Cyclopropene Fatty Acid—by Bioorthogonal Reaction with Turn‐On Tetrazine‐Fluorophore Conjugates**. Angewandte Chemie International Edition. 61(38). e202207640–e202207640. 23 indexed citations

11.

Straub, Verena M., Mariana Ávalos, Richard J. B. H. N. van den Berg, et al.. (2022). Chemical Proteomics Reveals Antibiotic Targets of Oxadiazolones in MRSA. Journal of the American Chemical Society. 145(2). 1136–1143. 19 indexed citations

12.

Baggelaar, Marc P., Hans den Dulk, Bogdan I. Florea, et al.. (2019). ABHD2 Inhibitor Identified by Activity-Based Protein Profiling Reduces Acrosome Reaction. ACS Chemical Biology. 14(10). 2295–2304. 17 indexed citations

13.

Baggelaar, Marc P., Hans den Dulk, Bogdan I. Florea, et al.. (2019). ABHD2 Inhibitor Identified by Activity-Based Protein Profiling Reduces Acrosome Reaction. ACS Chemical Biology. 14(12). 2943–2943. 18 indexed citations

14.

Janssen, Antonius P. A., Ruud H. Wijdeven, Eelke B. Lenselink, et al.. (2018). Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome–Inhibitor Interaction Landscapes. Journal of Chemical Information and Modeling. 59(3). 1221–1229. 54 indexed citations

15.

Soethoudt, Marjolein, Sara Christina Stolze, Matthias V. Westphal, et al.. (2018). Selective Photoaffinity Probe That Enables Assessment of Cannabinoid CB₂ Receptor Expression and Ligand Engagement in Human Cells. Journal of the American Chemical Society. 140(19). 6067–6075. 71 indexed citations

16.

Janssen, Antonius P. A., et al.. (2018). Development of a Multiplexed Activity-Based Protein Profiling Assay to Evaluate Activity of Endocannabinoid Hydrolase Inhibitors. ACS Chemical Biology. 13(9). 2406–2413. 29 indexed citations

17.

Rooden, Eva J. van, Thomas Hansen, Antonius P. A. Janssen, et al.. (2018). Two-step activity-based protein profiling of diacylglycerol lipase. Organic & Biomolecular Chemistry. 16(29). 5250–5253. 6 indexed citations

18.

Liu, Bing, Richard J. B. H. N. van den Berg, Adrianus M. C. H. van den Nieuwendijk, et al.. (2017). A Fluorescence Polarization Activity-Based Protein Profiling Assay in the Discovery of Potent, Selective Inhibitors for Human Nonlysosomal Glucosylceramidase. Journal of the American Chemical Society. 139(40). 14192–14197. 56 indexed citations

19.

Romeo, Elisa, Marc P. Baggelaar, Marta Artola, et al.. (2017). Novel activity-based probes for N-acylethanolamine acid amidase. Chemical Communications. 53(86). 11810–11813. 8 indexed citations

20.

Stelt, Mario van der, Wouter B. Veldhuis, P.R. Bär, et al.. (2001). Neuroprotection by Δ⁹-Tetrahydrocannabinol, the Main Active Compound in Marijuana, against Ouabain-InducedIn VivoExcitotoxicity. Journal of Neuroscience. 21(17). 6475–6479. 154 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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