Diana Jurk
- Physiology top 0.2%
- Molecular Biology top 2%
- Immunology top 2%
- Aging top 0.1%
- Epidemiology top 2%
- Co-authors
- João F. PassosThomas von ZglinickiChunfang WangGlyn NelsonCarmen Martín-RuizConor LawlessTamar TchkoniaJames L. Kirkland
- Topics
- Telomeres, Telomerase, and Senescence (29 papers)Genetics, Aging, and Longevity in Model Organisms (10 papers)Circadian rhythm and melatonin (8 papers)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Diana Jurk
41 papers receiving 7.3k citations
Hit Papers
Peers
Comparison fields: 5 of 141
- Physiology 4.0k
- Molecular Biology 3.1k
- Immunology 1.5k
- Aging 1.2k
- Epidemiology 1.0k
Countries citing papers authored by Diana Jurk
This map shows the geographic impact of Diana Jurk'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 Diana Jurk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diana Jurk more than expected).
Fields of papers citing papers by Diana Jurk
This network shows the impact of papers produced by Diana Jurk. 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 Diana Jurk. The network helps show where Diana Jurk may publish in the future.
Co-authorship network of co-authors of Diana Jurk
This figure shows the co-authorship network connecting the top 25 collaborators of Diana Jurk. A scholar is included among the top collaborators of Diana Jurk 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 Diana Jurk. Diana Jurk is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 6 | |
| 4 | 9 | |
| 5 | 15 | |
| 6 | Telomere dysfunction in ageing and age-related diseasesbreakdown → | 434 |
| 7 | A new gene set identifies senescent cells and predicts senescence-associated pathways across tissuesbreakdown → | 453 |
| 8 | 33 | |
| 9 | 77 | |
| 10 | 74 | |
| 11 | 271 | |
| 12 | 147 | |
| 13 | 287 | |
| 14 | Chronic inflammation induces telomere dysfunction and accelerates ageing in micebreakdown → | 593 |
| 15 | A senescent cell bystander effect: senescence‐induced senescencebreakdown → | 541 |
| 16 | 10 | |
| 17 | Telomeres are favoured targets of a persistent DNA damage response in ageing and stress-induced senescencebreakdown → | 651 |
| 18 | 53 | |
| 19 | 185 | |
| 20 | DNA damage response and cellular senescence in tissues of aging micebreakdown → | 530 |
About Diana Jurk
Diana Jurk is a scholar working on Aging, Physiology and Endocrine and Autonomic Systems, having authored 42 papers that have together received 7.4k indexed citations. Recurring topics across this work include Telomeres, Telomerase, and Senescence (29 papers), Genetics, Aging, and Longevity in Model Organisms (10 papers) and Circadian rhythm and melatonin (8 papers). The work is most often cited by research in Aging (1.2k citations), Physiology (4.0k citations) and Geriatrics and Gerontology (386 citations). Diana Jurk has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include João F. Passos, Thomas von Zglinicki, Chunfang Wang, Glyn Nelson, Carmen Martín-Ruiz, Conor Lawless, Tamar Tchkonia, James L. Kirkland, Mikołaj Ogrodnik and Clara Correia‐Melo. Their work appears in journals such as Nature Communications, Genes & Development and The EMBO Journal.
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.