Martin Kaase

4.9k total citations
88 papers, 2.3k citations indexed

About

Martin Kaase is a scholar working on Molecular Medicine, Clinical Biochemistry and Epidemiology. According to data from OpenAlex, Martin Kaase has authored 88 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Molecular Medicine, 30 papers in Clinical Biochemistry and 27 papers in Epidemiology. Recurrent topics in Martin Kaase's work include Antibiotic Resistance in Bacteria (57 papers), Bacterial Identification and Susceptibility Testing (30 papers) and Antimicrobial Resistance in Staphylococcus (15 papers). Martin Kaase is often cited by papers focused on Antibiotic Resistance in Bacteria (57 papers), Bacterial Identification and Susceptibility Testing (30 papers) and Antimicrobial Resistance in Staphylococcus (15 papers). Martin Kaase collaborates with scholars based in Germany, Sweden and France. Martin Kaase's co-authors include Sören Gatermann, Florian Szabados, Laurent Poirel, Patrice Nordmann, Rémy A. Bonnin, Agnes Anders, Thomas A. Wichelhaus, Anne Boulanger, Jacques Schrenzel and Niels Pfennigwerth and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied and Environmental Microbiology and Journal of Clinical Microbiology.

In The Last Decade

Martin Kaase

87 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Martin Kaase Germany 28 1.5k 710 616 608 515 88 2.3k
Annarita Mazzariol Italy 22 1.4k 0.9× 598 0.8× 529 0.9× 262 0.4× 555 1.1× 67 2.0k
Georgia Vrioni Greece 28 1.2k 0.8× 523 0.7× 695 1.1× 378 0.6× 287 0.6× 77 2.3k
Paul‐Louis Woerther France 23 1.4k 0.9× 706 1.0× 646 1.0× 308 0.5× 562 1.1× 88 2.5k
Yuxing Ni China 30 1.2k 0.8× 530 0.7× 908 1.5× 385 0.6× 476 0.9× 75 2.3k
M. Warner United Kingdom 26 1.8k 1.2× 579 0.8× 652 1.1× 483 0.8× 453 0.9× 38 2.5k
Maurine A. Leverstein‐van Hall Netherlands 30 2.0k 1.3× 1.1k 1.5× 593 1.0× 376 0.6× 404 0.8× 53 2.9k
Beatriz Meurer Moreira Brazil 28 1.0k 0.7× 555 0.8× 404 0.7× 410 0.7× 605 1.2× 75 2.1k
Pierre Bogaerts Belgium 37 2.3k 1.5× 1.1k 1.6× 638 1.0× 963 1.6× 763 1.5× 111 3.4k
Kang Liao China 24 1.1k 0.7× 442 0.6× 668 1.1× 318 0.5× 348 0.7× 77 2.0k
Te-Li Chen Taiwan 32 2.0k 1.3× 1.0k 1.4× 766 1.2× 239 0.4× 702 1.4× 101 3.0k

Countries citing papers authored by Martin Kaase

Since Specialization
Citations

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

Fields of papers citing papers by Martin Kaase

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Martin Kaase. 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 Martin Kaase. The network helps show where Martin Kaase may publish in the future.

Co-authorship network of co-authors of Martin Kaase

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Kaase. A scholar is included among the top collaborators of Martin Kaase 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 Martin Kaase. Martin Kaase 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.
Kaase, Martin, et al.. (2023). Central venous catheter contamination rate in suspected sepsis patients: an observational clinical study. Journal of Hospital Infection. 135. 98–105. 3 indexed citations
2.
Yao, Yancheng, Linda Falgenhauer, Jane Falgenhauer, et al.. (2023). Predominant transmission of KPC-2 carbapenemase in Germany by a unique IncN plasmid variant harboring a novel non-transposable element (NTE KPC -Y). Microbiology Spectrum. 12(1). e0256423–e0256423. 9 indexed citations
3.
Xanthopoulou, Kyriaki, et al.. (2021). Novel multiplex PCRs for detection of the most prevalent carbapenemase genes in Gram-negative bacteria within Germany. Journal of Medical Microbiology. 70(3). 22 indexed citations
4.
Becker, Laura, Stephan Fuchs, Yvonne Pfeifer, et al.. (2018). Whole Genome Sequence Analysis of CTX-M-15 Producing Klebsiella Isolates Allowed Dissecting a Polyclonal Outbreak Scenario. Frontiers in Microbiology. 9. 322–322. 38 indexed citations
5.
Pfennigwerth, Niels, et al.. (2018). Characterization of mutations inEscherichia coliPBP2 leading to increased carbapenem MICs. Journal of Antimicrobial Chemotherapy. 74(3). 571–576. 12 indexed citations
6.
Memish, Ziad A., Abdullah M. Assiri, Malak Almasri, et al.. (2015). Molecular Characterization of Carbapenemase Production Among Gram-Negative Bacteria in Saudi Arabia. Microbial Drug Resistance. 21(3). 307–314. 76 indexed citations
7.
Voss, A., Robin Köck, Bhanu Sinha, et al.. (2015). How to deal with multidrug-resistant Gram-negative microorganisms in cross-border healthcare? Comparison of Dutch and German guidelines.. Antimicrobial Resistance and Infection Control. 4. 1–7. 2 indexed citations
8.
9.
Kola, Axel, Brar Piening, Ulrich‐Frank Pape, et al.. (2015). An outbreak of carbapenem-resistant OXA-48 – producing Klebsiella pneumonia associated to duodenoscopy. Antimicrobial Resistance and Infection Control. 4(1). 8–8. 105 indexed citations
10.
Mischnik, Alexander, et al.. (2014). Detection of Carbapenemases by Real-Time PCR and Melt Curve Analysis on the BD Max System. Journal of Clinical Microbiology. 52(5). 1701–1704. 25 indexed citations
11.
Kaase, Martin, Florian Szabados, Niels Pfennigwerth, et al.. (2013). Description of the metallo-β-lactamase GIM-1 in Acinetobacter pittii. Journal of Antimicrobial Chemotherapy. 69(1). 81–84. 30 indexed citations
12.
Kaase, Martin. (2012). Carbapenemasen bei gramnegativen Erregern in Deutschland: Daten des Nationalen Referenzzentrums für gramnegative Krankenhauserreger. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 55. 1401–1404. 2 indexed citations
13.
Hamprecht, Axel, Laurent Poirel, Stephan Göttig, et al.. (2012). Detection of the carbapenemase GIM-1 in Enterobacter cloacae in Germany. Journal of Antimicrobial Chemotherapy. 68(3). 558–561. 40 indexed citations
14.
Kaase, Martin, et al.. (2011). NDM-2 carbapenemase in Acinetobacter baumannii from Egypt. Journal of Antimicrobial Chemotherapy. 66(6). 1260–1262. 179 indexed citations
15.
16.
Wagenlehner, Flo ri an, Udo B. Hoyme, Martin Kaase, et al.. (2011). Uncomplicated Urinary Tract Infections. Deutsches Ärzteblatt international. 108(24). 415–23. 82 indexed citations
17.
Szabados, Florian, et al.. (2011). Fbl is not involved in the invasion of eukaryotic epithelial and endothelial cells by Staphylococcus lugdunensis. FEMS Microbiology Letters. 324(1). 48–55. 9 indexed citations
18.
Steinmann, Jörg, Martin Kaase, Sören Gatermann, et al.. (2011). Outbreak due to a Klebsiella pneumoniae strain harbouring KPC-2 and VIM-1 in a German university hospital, July 2010 to January 2011. Eurosurveillance. 16(33). 76 indexed citations
19.
Szabados, Florian, et al.. (2010). False-negative test results in the Slidex Staph Plus (bioMérieux) agglutination test are mainly caused by spa-type t001 and t001-related strains. European Journal of Clinical Microbiology & Infectious Diseases. 30(2). 201–208. 6 indexed citations
20.

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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