Tamar Tchkonia
Impact in
- Aging top 0.02%
- Genetics, Aging, and Longevity in Model Organisms
- Physiology top 0.01%
- Telomeres, Telomerase, and Senescence
- Adipose Tissue and Metabolism
Papers in
- Physiology 124
- Telomeres, Telomerase, and Senescence 91
- Adipose Tissue and Metabolism 30
- Co-authors
- James L. Kirkland (186 shared papers)Nathan K. LeBrasseur (28 shared papers)Yi Zhu (18 shared papers)Tamar Pirtskhalava (27 shared papers)Michael D. Jensen (17 shared papers)Nino Giorgadze (22 shared papers)Sundeep Khosla (15 shared papers)Jan van Deursen (2 shared papers)
- Journals
- Aging Cell (14 papers)Aging (9 papers)Obesity (7 papers)American Journal of Physiology-Endocrinology and Metabolism (6 papers)GeroScience (5 papers)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Tamar Tchkonia
185 papers receiving 27.9k citations
Tamar Tchkonia's Hit Papers
Peers
Comparison fields: 5 of 162
- Aging 3.4k
- Physiology 15.0k
- Geriatrics and Gerontology 1.1k
- Endocrine and Autonomic Systems 1.6k
- Immunology 4.5k
Countries citing papers authored by Tamar Tchkonia
This map shows the geographic impact of Tamar Tchkonia'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 Tamar Tchkonia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tamar Tchkonia more than expected).
Fields of papers citing papers by Tamar Tchkonia
This network shows the impact of papers produced by Tamar Tchkonia. 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 Tamar Tchkonia. The network helps show where Tamar Tchkonia may publish in the future.
Co-authors
The 25 scholars most cited alongside Tamar Tchkonia, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 193 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders Hit paper breakdown → | 2011 | 2848 |
| 2 | Cellular senescence and the senescent secretory phenotype: therapeutic opportunities Hit paper breakdown → | 2013 | 1379 |
| 3 | Senolytics in idiopathic pulmonary fibrosis: Results from a first-in-human, open-label, pilot study Hit paper breakdown → | 2019 | 909 |
| 4 | Targeting cellular senescence prevents age-related bone loss in mice Hit paper breakdown → | 2017 | 881 |
| 5 | Identification of a novel senolytic agent, navitoclax, targeting the Bcl‐2 family of anti‐apoptotic factors Hit paper breakdown → | 2015 | 862 |
| 6 | Fat tissue, aging, and cellular senescence Hit paper breakdown → | 2010 | 854 |
| 7 | Senolytic drugs: from discovery to translation Hit paper breakdown → | 2020 | 790 |
| 8 | Cellular senescence drives age-dependent hepatic steatosis Hit paper breakdown → | 2017 | 776 |
| 9 | Cellular Senescence: A Translational Perspective Hit paper breakdown → | 2017 | 771 |
| 10 | Cellular senescence and senolytics: the path to the clinic Hit paper breakdown → | 2022 | 703 |
| 11 | JAK inhibition alleviates the cellular senescence-associated secretory phenotype and frailty in old age Hit paper breakdown → | 2015 | 674 |
| 12 | Chronic senolytic treatment alleviates established vasomotor dysfunction in aged or atherosclerotic mice Hit paper breakdown → | 2016 | 605 |
| 13 | New agents that target senescent cells: the flavone, fisetin, and the BCL-XL inhibitors, A1331852 and A1155463 Hit paper breakdown → | 2017 | 577 |
| 14 | Identification of HSP90 inhibitors as a novel class of senolytics Hit paper breakdown → | 2017 | 545 |
| 15 | A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues Hit paper breakdown → | 2022 | 523 |
| 16 | Mechanisms and Metabolic Implications of Regional Differences among Fat Depots Hit paper breakdown → | 2013 | 521 |
| 17 | Targeting senescent cells enhances adipogenesis and metabolic function in old age Hit paper breakdown → | 2015 | 480 |
| 18 | Identification of Senescent Cells in the Bone Microenvironment Hit paper breakdown → | 2016 | 435 |
| 19 | The Clinical Potential of Senolytic Drugs Hit paper breakdown → | 2017 | 414 |
| 20 | 2010 | 401 |
About Tamar Tchkonia
Tamar Tchkonia is a scholar working on Physiology, Molecular Biology, Epidemiology, Immunology and Aging, having authored 193 papers that have together received 28.3k indexed citations. Recurring topics across this work include Telomeres, Telomerase, and Senescence (91 papers), Adipokines, Inflammation, and Metabolic Diseases (41 papers), Adipose Tissue and Metabolism (30 papers), Genetics, Aging, and Longevity in Model Organisms (27 papers), Neutrophil, Myeloperoxidase and Oxidative Mechanisms (22 papers), Circadian rhythm and melatonin (13 papers), MicroRNA in disease regulation (10 papers) and Mesenchymal stem cell research (9 papers). The work is most often cited by research in Aging (3.4k citations), Physiology (15.0k citations), Geriatrics and Gerontology (1.1k citations), Endocrine and Autonomic Systems (1.6k citations) and Immunology (4.5k citations). Tamar Tchkonia has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include James L. Kirkland, Nathan K. LeBrasseur, Yi Zhu, Tamar Pirtskhalava, Michael D. Jensen, Nino Giorgadze, Sundeep Khosla, Jan van Deursen, Darren J. Baker and Jan M. van Deursen. Their work appears in journals such as Aging Cell, Aging, Obesity, American Journal of Physiology-Endocrinology and Metabolism and GeroScience.
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.