Tamar Pirtskhalava
Impact in
- Aging top 0.05%
- Genetics, Aging, and Longevity in Model Organisms
- Physiology top 0.05%
- Telomeres, Telomerase, and Senescence
- Adipose Tissue and Metabolism
Papers in
- Physiology 40
- Telomeres, Telomerase, and Senescence 29
- Adipose Tissue and Metabolism 12
- Alzheimer's disease research and treatments 2
- Aging 4
- Genetics, Aging, and Longevity in Model Organisms 4
- Co-authors
- James L. Kirkland (29 shared papers)Tamar Tchkonia (28 shared papers)Nino Giorgadze (12 shared papers)Kurt O. Johnson (4 shared papers)Yi Zhu (4 shared papers)Mikolaj B. Ogrodnik (4 shared papers)Michael B. Stout (5 shared papers)Ming Xu (4 shared papers)
- Journals
- Aging Cell (7 papers)Aging (4 papers)Cell Metabolism (4 papers)EBioMedicine (4 papers)American Journal of Physiology-Endocrinology and Metabolism (4 papers)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Tamar Pirtskhalava
44 papers receiving 15.5k citations
Tamar Pirtskhalava's Hit Papers
Peers
Comparison fields: 5 of 125
- Aging 1.6k
- Physiology 9.4k
- Geriatrics and Gerontology 300
- Genetics 541
- Epidemiology 1.3k
Countries citing papers authored by Tamar Pirtskhalava
This map shows the geographic impact of Tamar Pirtskhalava'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 Pirtskhalava with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tamar Pirtskhalava more than expected).
Fields of papers citing papers by Tamar Pirtskhalava
This network shows the impact of papers produced by Tamar Pirtskhalava. 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 Pirtskhalava. The network helps show where Tamar Pirtskhalava may publish in the future.
Co-authors
The 25 scholars most cited alongside Tamar Pirtskhalava, 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 45 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The Achilles’ heel of senescent cells: from transcriptome to senolytic drugs Hit paper breakdown → | 2015 | 2018 |
| 2 | Senolytics improve physical function and increase lifespan in old age Hit paper breakdown → | 2018 | 1816 |
| 3 | Cellular senescence mediates fibrotic pulmonary disease Hit paper breakdown → | 2017 | 1285 |
| 4 | Senolytics decrease senescent cells in humans: Preliminary report from a clinical trial of Dasatinib plus Quercetin in individuals with diabetic kidney disease Hit paper breakdown → | 2019 | 1095 |
| 5 | Targeting cellular senescence prevents age-related bone loss in mice Hit paper breakdown → | 2017 | 959 |
| 6 | Identification of a novel senolytic agent, navitoclax, targeting the Bcl‐2 family of anti‐apoptotic factors Hit paper breakdown → | 2015 | 937 |
| 7 | Fisetin is a senotherapeutic that extends health and lifespan Hit paper breakdown → | 2018 | 835 |
| 8 | JAK inhibition alleviates the cellular senescence-associated secretory phenotype and frailty in old age Hit paper breakdown → | 2015 | 732 |
| 9 | Chronic senolytic treatment alleviates established vasomotor dysfunction in aged or atherosclerotic mice Hit paper breakdown → | 2016 | 652 |
| 10 | New agents that target senescent cells: the flavone, fisetin, and the BCL-XL inhibitors, A1331852 and A1155463 Hit paper breakdown → | 2017 | 626 |
| 11 | Targeting senescent cells alleviates obesity‐induced metabolic dysfunction Hit paper breakdown → | 2019 | 538 |
| 12 | Targeting senescent cells enhances adipogenesis and metabolic function in old age Hit paper breakdown → | 2015 | 518 |
| 13 | Obesity-Induced Cellular Senescence Drives Anxiety and Impairs Neurogenesis Hit paper breakdown → | 2019 | 456 |
| 14 | Whole‐body senescent cell clearance alleviates age‐related brain inflammation and cognitive impairment in mice Hit paper breakdown → | 2021 | 325 |
| 15 | 2016 | 310 | |
| 16 | 2006 | 306 | |
| 17 | 2002 | 293 | |
| 18 | Senolytics reduce coronavirus-related mortality in old mice Hit paper breakdown → | 2021 | 271 |
| 19 | 2002 | 225 | |
| 20 | 2004 | 205 |
About Tamar Pirtskhalava
Tamar Pirtskhalava is a scholar working on Physiology, Aging, Epidemiology, Developmental Neuroscience and Endocrinology, Diabetes and Metabolism, having authored 45 papers that have together received 15.7k indexed citations. Recurring topics across this work include Telomeres, Telomerase, and Senescence (29 papers), Adipose Tissue and Metabolism (12 papers), Adipokines, Inflammation, and Metabolic Diseases (8 papers), Peroxisome Proliferator-Activated Receptors (4 papers), Genetics, Aging, and Longevity in Model Organisms (4 papers), Alzheimer's disease research and treatments (2 papers), Neurogenesis and neuroplasticity mechanisms (2 papers) and Dementia and Cognitive Impairment Research (1 paper). The work is most often cited by research in Aging (1.6k citations), Physiology (9.4k citations), Geriatrics and Gerontology (300 citations), Genetics (541 citations) and Epidemiology (1.3k citations). Tamar Pirtskhalava has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include James L. Kirkland, Tamar Tchkonia, Nino Giorgadze, Kurt O. Johnson, Yi Zhu, Mikolaj B. Ogrodnik, Michael B. Stout, Ming Xu, Nathan K. LeBrasseur and Michael D. Jensen. Their work appears in journals such as Aging Cell, Aging, Cell Metabolism, EBioMedicine and American Journal of Physiology-Endocrinology and Metabolism.
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.