J.A. Verschoor

2.1k total citations
72 papers, 1.5k citations indexed

About

J.A. Verschoor is a scholar working on Molecular Biology, Epidemiology and Infectious Diseases. According to data from OpenAlex, J.A. Verschoor has authored 72 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 19 papers in Epidemiology and 18 papers in Infectious Diseases. Recurrent topics in J.A. Verschoor's work include Monoclonal and Polyclonal Antibodies Research (15 papers), Tuberculosis Research and Epidemiology (15 papers) and Mycobacterium research and diagnosis (12 papers). J.A. Verschoor is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (15 papers), Tuberculosis Research and Epidemiology (15 papers) and Mycobacterium research and diagnosis (12 papers). J.A. Verschoor collaborates with scholars based in South Africa, United Kingdom and Netherlands. J.A. Verschoor's co-authors include Hulda Swai, Lonji Kalombo, Yolandy Lemmer, Lebogang Katata, Johan Grooten, Boitumelo Semete‐Makokotlela, Mark S. Baird, Lindi M. Coetzee, R.H. Veltman and Anton Stoltz and has published in prestigious journals such as PLoS ONE, American Journal of Respiratory and Critical Care Medicine and Analytical Biochemistry.

In The Last Decade

J.A. Verschoor

71 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J.A. Verschoor South Africa 21 392 347 288 261 228 72 1.5k
Young Bong Kim South Korea 25 755 1.9× 293 0.8× 208 0.7× 105 0.4× 261 1.1× 111 1.9k
Douglas W. Lowman United States 24 436 1.1× 658 1.9× 473 1.6× 470 1.8× 158 0.7× 47 1.7k
Yutaro Kaneko Japan 29 607 1.5× 306 0.9× 192 0.7× 466 1.8× 205 0.9× 91 2.1k
Noriko Tomita Japan 30 944 2.4× 541 1.6× 197 0.7× 118 0.5× 174 0.8× 117 2.5k
Pavel Kulich Czechia 18 289 0.7× 168 0.5× 152 0.5× 92 0.4× 178 0.8× 71 1.3k
Minakshi Prasad India 18 293 0.7× 309 0.9× 173 0.6× 56 0.2× 175 0.8× 79 1.4k
G.J. Russell-Jones Australia 13 1.5k 3.7× 236 0.7× 208 0.7× 339 1.3× 111 0.5× 15 2.8k
Peter M. Moyle Australia 28 1.1k 2.7× 360 1.0× 264 0.9× 80 0.3× 161 0.7× 66 2.1k
Moon‐Hee Sung South Korea 33 1.7k 4.4× 287 0.8× 360 1.3× 111 0.4× 248 1.1× 107 3.0k
Huichen Guo China 28 700 1.8× 448 1.3× 217 0.8× 91 0.3× 140 0.6× 114 2.6k

Countries citing papers authored by J.A. Verschoor

Since Specialization
Citations

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

Fields of papers citing papers by J.A. Verschoor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J.A. Verschoor. 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 J.A. Verschoor. The network helps show where J.A. Verschoor may publish in the future.

Co-authorship network of co-authors of J.A. Verschoor

This figure shows the co-authorship network connecting the top 25 collaborators of J.A. Verschoor. A scholar is included among the top collaborators of J.A. Verschoor 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 J.A. Verschoor. J.A. Verschoor 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.
Verschoor, J.A., et al.. (2024). A quality decay model with multinomial logistic regression and image-based deep learning to predict the firmness of ‘Conference’ pears in the downstream supply chains. Journal of Stored Products Research. 109. 102450–102450. 1 indexed citations
2.
Scriba, Manfred, Les Baillie, Arwyn T. Jones, et al.. (2024). Application of Monoclonal Anti-Mycolate Antibodies in Serological Diagnosis of Tuberculosis. Tropical Medicine and Infectious Disease. 9(11). 269–269.
3.
Mishra, Puneet, J.A. Verschoor, Mariska Nijenhuis‐de Vries, G. Polder, & Martin P. Boer. (2023). Portable near-infrared spectral imaging combining deep learning and chemometrics for dry matter and soluble solids prediction in intact kiwifruit. Infrared Physics & Technology. 131. 104677–104677. 10 indexed citations
4.
O’Kennedy, Martha M., Célia Abolnik, Tanja Smith, et al.. (2023). Immunogenicity of adjuvanted plant-produced SARS-CoV-2 Beta spike VLP vaccine in New Zealand white rabbits. Vaccine. 41(13). 2261–2269. 9 indexed citations
5.
Verschoor, J.A., et al.. (2021). Low Oxygen Storage Improves Tomato Postharvest Cold Tolerance, Especially for Tomatoes Cultivated with Far-Red LED Light. Foods. 10(8). 1699–1699. 2 indexed citations
6.
Lemmer, Yolandy, et al.. (2018). The antigenicity and cholesteroid nature of mycolic acids determined by recombinant chicken antibodies. PLoS ONE. 13(8). e0200298–e0200298. 2 indexed citations
7.
Baird, Mark S., et al.. (2013). Differential spontaneous folding of mycolic acids from Mycobacterium tuberculosis. Chemistry and Physics of Lipids. 180. 15–22. 24 indexed citations
8.
Semete‐Makokotlela, Boitumelo, Yolandy Lemmer, Lonji Kalombo, et al.. (2010). In vivo evaluation of the biodistribution and safety of PLGA nanoparticles as drug delivery systems. Nanomedicine Nanotechnology Biology and Medicine. 6(5). 662–671. 328 indexed citations
9.
Kalombo, Lonji, et al.. (2010). In vivo uptake and acute immune response to orally administered chitosan and PEG coated PLGA nanoparticles. Toxicology and Applied Pharmacology. 249(2). 158–165. 72 indexed citations
10.
11.
Verschoor, J.A., et al.. (2008). A novel application of affinity biosensor technology to detect antibodies to mycolic acid in tuberculosis patients. Journal of Immunological Methods. 332(1-2). 61–72. 42 indexed citations
12.
Bokum, Annemieke ten, et al.. (2008). Cholesteroid nature of free mycolic acids from M. tuberculosis. Chemistry and Physics of Lipids. 152(2). 95–103. 25 indexed citations
13.
Dulayymi, Juma’a R. Al, et al.. (2007). The first syntheses of single enantiomers of the major methoxymycolic acid of Mycobacterium tuberculosis. Tetrahedron. 63(12). 2571–2592. 46 indexed citations
14.
Korf, Hannelie, Gwenda Pynaert, Kurt G. Tournoy, et al.. (2006). Macrophage Reprogramming by Mycolic Acid Promotes a Tolerogenic Response in Experimental Asthma. American Journal of Respiratory and Critical Care Medicine. 174(2). 152–160. 49 indexed citations
15.
Bragg, R R, et al.. (1997). Isolation and identification of NAD‐independent bacteria from chickens with symptoms of infectious coryza. Avian Pathology. 26(3). 595–606. 28 indexed citations
16.
Bragg, R R, et al.. (1997). Monoclonal antibody characterization of reference isolates of different serogroups of Haemophilus paragallinarum. Avian Pathology. 26(4). 749–764. 5 indexed citations
17.
18.
Verschoor, J.A., et al.. (1990). Spontaneous Fusion Between Splenocytes and Myeloma Cells Induced by Bacterial Immunization. Hybridoma. 9(5). 511–518. 6 indexed citations
19.
Verschoor, J.A., Nico Vermeulen, & Leon Visser. (1990). Haptenated nylon-coated polystyrene plates as a solid phase for ELISA. Journal of Immunological Methods. 127(1). 43–49. 4 indexed citations
20.
Verschoor, J.A., et al.. (1988). Isotype restriction of murine antibodies towards the loop region of hen's egg white lysozyme. Immunology Letters. 17(1). 21–28. 2 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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