Sunil K. Panchal

4.8k total citations
64 papers, 3.7k citations indexed

About

Sunil K. Panchal is a scholar working on Endocrinology, Diabetes and Metabolism, Physiology and Nutrition and Dietetics. According to data from OpenAlex, Sunil K. Panchal has authored 64 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Endocrinology, Diabetes and Metabolism, 21 papers in Physiology and 19 papers in Nutrition and Dietetics. Recurrent topics in Sunil K. Panchal's work include Diet and metabolism studies (18 papers), Diet, Metabolism, and Disease (17 papers) and Phytochemicals and Antioxidant Activities (9 papers). Sunil K. Panchal is often cited by papers focused on Diet and metabolism studies (18 papers), Diet, Metabolism, and Disease (17 papers) and Phytochemicals and Antioxidant Activities (9 papers). Sunil K. Panchal collaborates with scholars based in Australia, Malaysia and Japan. Sunil K. Panchal's co-authors include Lindsay Brown, Hemant Poudyal, Leigh C. Ward, Vishal Diwan, Jennifer Waanders, Nikhil S. Bhandarkar, Stephen Wanyonyi, Peter Mouatt, Edward Bliss and Kate Kauter and has published in prestigious journals such as PLoS ONE, Scientific Reports and The FASEB Journal.

In The Last Decade

Sunil K. Panchal

63 papers receiving 3.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sunil K. Panchal Australia 30 1.0k 900 867 862 667 64 3.7k
Natalie C. Ward Australia 42 1.1k 1.1× 881 1.0× 1.4k 1.6× 845 1.0× 326 0.5× 129 5.5k
Sung‐Joon Lee South Korea 37 574 0.6× 579 0.6× 1.6k 1.8× 674 0.8× 457 0.7× 104 3.9k
Zahra Bahadoran Iran 31 904 0.9× 738 0.8× 741 0.9× 491 0.6× 349 0.5× 129 3.4k
Hemant Poudyal Australia 23 805 0.8× 742 0.8× 561 0.6× 705 0.8× 553 0.8× 41 2.7k
Carani Venkatraman Anuradha India 43 973 0.9× 1.8k 2.0× 1.1k 1.3× 507 0.6× 758 1.1× 134 5.0k
Alejandro Gugliucci United States 35 854 0.8× 1.5k 1.7× 777 0.9× 320 0.4× 518 0.8× 111 4.7k
Amir Hadi Iran 35 795 0.8× 499 0.6× 711 0.8× 497 0.6× 418 0.6× 123 3.1k
Teruyoshi Yanagita Japan 40 1.2k 1.1× 697 0.8× 1.9k 2.1× 2.0k 2.3× 706 1.1× 165 5.0k
Tomasz Szkudelski Poland 27 1.3k 1.3× 2.1k 2.3× 1.3k 1.5× 488 0.6× 595 0.9× 84 5.2k
Dorothy Klimis‐Zacas United States 31 617 0.6× 415 0.5× 705 0.8× 589 0.7× 528 0.8× 67 3.1k

Countries citing papers authored by Sunil K. Panchal

Since Specialization
Citations

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

Fields of papers citing papers by Sunil K. Panchal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sunil K. Panchal

This figure shows the co-authorship network connecting the top 25 collaborators of Sunil K. Panchal. A scholar is included among the top collaborators of Sunil K. Panchal 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 Sunil K. Panchal. Sunil K. Panchal 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.
Panchal, Sunil K., et al.. (2024). COVID-19 lockdown impacts on eating patterns and lifestyle behaviours of residents of Western Sydney: Fact not Fiction. Proceedings of The Nutrition Society. 83(OCE1). 1 indexed citations
2.
Panchal, Sunil K. & Lindsay Brown. (2024). Review: Ageing, Health and Macroalgae. Medical Research Archives. 1 indexed citations
3.
Cruzat, Vínicius Fernandes, et al.. (2023). The Potential of Spent Coffee Grounds in Functional Food Development. Nutrients. 15(4). 994–994. 53 indexed citations
4.
Li, Li, et al.. (2023). Capsicum Waste as a Sustainable Source of Capsaicinoids for Metabolic Diseases. Foods. 12(4). 907–907. 35 indexed citations
5.
Panchal, Sunil K., Marie Magnusson, Andrew J. Cole, et al.. (2022). Freshwater Macroalgae, Oedogonium, Grown in Wastewater Reduce Diet-Induced Metabolic Syndrome in Rats. International Journal of Molecular Sciences. 23(22). 13811–13811. 1 indexed citations
6.
Panchal, Sunil K. & Lindsay Brown. (2022). Tropical fruits from Australia as potential treatments for metabolic syndrome. Current Opinion in Pharmacology. 63. 102182–102182. 11 indexed citations
7.
Panchal, Sunil K. & Lindsay Brown. (2022). Potential Benefits of Anthocyanins in Chronic Disorders of the Central Nervous System. Molecules. 28(1). 80–80. 11 indexed citations
8.
Bhandarkar, Nikhil S., Peter Mouatt, Marwan E. Majzoub, et al.. (2021). Coffee Pulp, a By-Product of Coffee Production, Modulates Gut Microbiota and Improves Metabolic Syndrome in High-Carbohydrate, High-Fat Diet-Fed Rats. Pathogens. 10(11). 1369–1369. 28 indexed citations
9.
Panchal, Sunil K., et al.. (2020). Dietary Saturated Fatty Acids Modulate Pain Behaviour in Trauma-Induced Osteoarthritis in Rats. Nutrients. 12(2). 509–509. 17 indexed citations
10.
11.
John, Oliver Dean, et al.. (2020). Tropical foods as functional foods for metabolic syndrome. Food & Function. 11(8). 6946–6960. 16 indexed citations
12.
Paul, Nicholas A., Peter Mouatt, Marwan E. Majzoub, et al.. (2020). Carrageenans from the Red Seaweed Sarconema filiforme Attenuate Symptoms of Diet-Induced Metabolic Syndrome in Rats. Marine Drugs. 18(2). 97–97. 54 indexed citations
13.
John, Oliver Dean, Peter Mouatt, Marwan E. Majzoub, et al.. (2019). Physiological and Metabolic Effects of Yellow Mangosteen (Garcinia dulcis) Rind in Rats with Diet-Induced Metabolic Syndrome. International Journal of Molecular Sciences. 21(1). 272–272. 30 indexed citations
14.
Shafie, Siti Raihanah, Stephen Wanyonyi, Sunil K. Panchal, & Lindsay Brown. (2019). Linseed Components Are More Effective Than Whole Linseed in Reversing Diet-Induced Metabolic Syndrome in Rats. Nutrients. 11(7). 1677–1677. 10 indexed citations
15.
Arora, Meenakshi, et al.. (2019). Low-Dose Curcumin Nanoparticles Normalise Blood Pressure in Male Wistar Rats with Diet-Induced Metabolic Syndrome. Nutrients. 11(7). 1542–1542. 29 indexed citations
16.
Panchal, Sunil K., et al.. (2018). An improved rat model for chronic inflammatory bowel disease. Pharmacological Reports. 71(1). 149–155. 17 indexed citations
17.
Panchal, Sunil K., Edward Bliss, & Lindsay Brown. (2018). Capsaicin in Metabolic Syndrome. Nutrients. 10(5). 630–630. 126 indexed citations
18.
Bhandarkar, Nikhil S., Senthil Arun Kumar, Jarad Martin, Lindsay Brown, & Sunil K. Panchal. (2018). Attenuation of Metabolic Syndrome by EPA/DHA Ethyl Esters in Testosterone-Deficient Obese Rats. Marine Drugs. 16(6). 182–182. 8 indexed citations
19.
John, Oliver Dean, Stephen Wanyonyi, Peter Mouatt, Sunil K. Panchal, & Lindsay Brown. (2018). Achacha (Garcinia humilis) Rind Improves Cardiovascular Function in Rats with Diet-Induced Metabolic Syndrome. Nutrients. 10(10). 1425–1425. 18 indexed citations
20.
Bishnoi, Mahendra, Pragyanshu Khare, Lindsay Brown, & Sunil K. Panchal. (2018). Transient receptor potential (TRP) channels: a metabolic TR(i)P to obesity prevention and therapy. Obesity Reviews. 19(9). 1269–1292. 26 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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