Ritesh K. Baboota

1.4k total citations
24 papers, 1.1k citations indexed

About

Ritesh K. Baboota is a scholar working on Physiology, Molecular Biology and Epidemiology. According to data from OpenAlex, Ritesh K. Baboota has authored 24 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Physiology, 8 papers in Molecular Biology and 8 papers in Epidemiology. Recurrent topics in Ritesh K. Baboota's work include Adipose Tissue and Metabolism (11 papers), Diet and metabolism studies (6 papers) and Biochemical Analysis and Sensing Techniques (5 papers). Ritesh K. Baboota is often cited by papers focused on Adipose Tissue and Metabolism (11 papers), Diet and metabolism studies (6 papers) and Biochemical Analysis and Sensing Techniques (5 papers). Ritesh K. Baboota collaborates with scholars based in India, Sweden and United States. Ritesh K. Baboota's co-authors include Mahendra Bishnoi, Kanthi Kiran Kondepudi, Ravneet K. Boparai, Dhirendra Singh, Pragyanshu Khare, Jaspreet Kaur, Ulf Smith, Kanwaljit Chopra, Sneha Jagtap and Kamlesh K. Bhutani and has published in prestigious journals such as Journal of Clinical Investigation, PLoS ONE and Diabetes.

In The Last Decade

Ritesh K. Baboota

24 papers receiving 1.1k citations

Peers

Ritesh K. Baboota
Chu-Sook Kim South Korea
Tao Tong China
Anca Ungurianu Romania
Hatem M. Abuohashish Saudi Arabia
Larissa Staurengo‐Ferrari Brazil
Ki-Choon Choi South Korea
Hui Fan China
Ilenia Cirilli Italy
Chu-Sook Kim South Korea
Ritesh K. Baboota
Citations per year, relative to Ritesh K. Baboota Ritesh K. Baboota (= 1×) peers Chu-Sook Kim

Countries citing papers authored by Ritesh K. Baboota

Since Specialization
Citations

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

Fields of papers citing papers by Ritesh K. Baboota

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ritesh K. Baboota. 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 Ritesh K. Baboota. The network helps show where Ritesh K. Baboota may publish in the future.

Co-authorship network of co-authors of Ritesh K. Baboota

This figure shows the co-authorship network connecting the top 25 collaborators of Ritesh K. Baboota. A scholar is included among the top collaborators of Ritesh K. Baboota 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 Ritesh K. Baboota. Ritesh K. Baboota 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.
Spinelli, Rosa, Ritesh K. Baboota, Silvia Gogg, et al.. (2023). Increased cell senescence in human metabolic disorders. Journal of Clinical Investigation. 133(12). 52 indexed citations
2.
Baboota, Ritesh K., Aidin Rawshani, L Bonnet, et al.. (2022). BMP4 and Gremlin 1 regulate hepatic cell senescence during clinical progression of NAFLD/NASH. Nature Metabolism. 4(8). 1007–1021. 55 indexed citations
3.
Bonnet, L, Ida Alexandersson, Ritesh K. Baboota, et al.. (2022). Cellular senescence in hepatocytes contributes to metabolic disturbances in NASH. Frontiers in Endocrinology. 13. 957616–957616. 21 indexed citations
4.
Baboota, Ritesh K., Rosa Spinelli, Malin C. Erlandsson, et al.. (2022). Chronic hyperinsulinemia promotes human hepatocyte senescence. Molecular Metabolism. 64. 101558–101558. 30 indexed citations
5.
Hoffmann, Jenny, Shahram Hedjazifar, L Bonnet, et al.. (2021). Adult mice are unresponsive to AAV8-Gremlin1 gene therapy targeting the liver. PLoS ONE. 16(2). e0247300–e0247300. 1 indexed citations
6.
Ferrannini, Ele, Ritesh K. Baboota, Shahram Hedjazifar, et al.. (2019). Mannose is an insulin-regulated metabolite reflecting whole-body insulin sensitivity in man. Metabolism. 102. 153974–153974. 24 indexed citations
7.
Baboota, Ritesh K., Katleen Lemaire, Marc Fransen, et al.. (2019). Functional peroxisomes are required for β-cell integrity in mice. Molecular Metabolism. 22. 71–83. 27 indexed citations
8.
Khare, Pragyanshu, Priyanka Mangal, Ritesh K. Baboota, et al.. (2018). Involvement of Glucagon in Preventive Effect of Menthol Against High Fat Diet Induced Obesity in Mice. Frontiers in Pharmacology. 9. 1244–1244. 30 indexed citations
9.
Malheiro, Ana Rita, et al.. (2018). Autonomous Purkinje cell axonal dystrophy causes ataxia in peroxisomal multifunctional protein‐2 deficiency. Brain Pathology. 28(5). 631–643. 7 indexed citations
10.
Baboota, Ritesh K., Simone Denis, Ursula Loizides‐Mangold, et al.. (2017). Mitochondrial disruption in peroxisome deficient cells is hepatocyte selective but is not mediated by common hepatic peroxisomal metabolites. Mitochondrion. 39. 51–59. 18 indexed citations
11.
Baboota, Ritesh K., Pragyanshu Khare, Priyanka Mangal, et al.. (2017). Dihydrocapsiate supplementation prevented high-fat diet–induced adiposity, hepatic steatosis, glucose intolerance, and gut morphological alterations in mice. Nutrition Research. 51. 40–56. 18 indexed citations
12.
Singh, Dhirendra, Shashank Singh, Vijay Kumar, et al.. (2017). Co-supplementation of isomalto-oligosaccharides potentiates metabolic health benefits of polyphenol-rich cranberry extract in high fat diet-fed mice via enhanced gut butyrate production. European Journal of Nutrition. 57(8). 2897–2911. 49 indexed citations
13.
Singh, Dhirendra, Pragyanshu Khare, Ritesh K. Baboota, et al.. (2017). Coadministration of isomalto‐oligosaccharides augments metabolic health benefits of cinnamaldehyde in high fat diet fed mice. BioFactors. 43(6). 821–835. 30 indexed citations
14.
Singh, Dhirendra, Pragyanshu Khare, Kanthi Kiran Kondepudi, et al.. (2015). A novel cobiotic-based preventive approach against high-fat diet-induced adiposity, nonalcoholic fatty liver and gut derangement in mice. International Journal of Obesity. 40(3). 487–496. 82 indexed citations
15.
Baboota, Ritesh K., et al.. (2015). Microarray Based Gene Expression Analysis of Murine Brown and Subcutaneous Adipose Tissue: Significance with Human. PLoS ONE. 10(5). e0127701–e0127701. 12 indexed citations
16.
Baboota, Ritesh K., Dhirendra Singh, Jaspreet Kaur, et al.. (2014). Capsaicin Induces “Brite” Phenotype in Differentiating 3T3-L1 Preadipocytes. PLoS ONE. 9(7). e103093–e103093. 108 indexed citations
17.
Baboota, Ritesh K., Nida Murtaza, Sneha Jagtap, et al.. (2014). Capsaicin-induced transcriptional changes in hypothalamus and alterations in gut microbial count in high fat diet fed mice. The Journal of Nutritional Biochemistry. 25(9). 893–902. 95 indexed citations
18.
Murtaza, Nida, Ritesh K. Baboota, Sneha Jagtap, et al.. (2014). Finger millet bran supplementation alleviates obesity-induced oxidative stress, inflammation and gut microbial derangements in high-fat diet-fed mice. British Journal Of Nutrition. 112(9). 1447–1458. 53 indexed citations
19.
Bishnoi, Mahendra, et al.. (2013). Role of transient receptor potential channels in adipocyte biology. Expert Review of Endocrinology & Metabolism. 8(2). 173–182. 9 indexed citations
20.
Baboota, Ritesh K., Mahendra Bishnoi, Padma Ambalam, et al.. (2013). Functional food ingredients for the management of obesity and associated co-morbidities – A review. Journal of Functional Foods. 5(3). 997–1012. 130 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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