Usha Gutti

842 total citations
21 papers, 638 citations indexed

About

Usha Gutti is a scholar working on Molecular Biology, Hematology and Cancer Research. According to data from OpenAlex, Usha Gutti has authored 21 papers receiving a total of 638 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 7 papers in Hematology and 6 papers in Cancer Research. Recurrent topics in Usha Gutti's work include Platelet Disorders and Treatments (6 papers), MicroRNA in disease regulation (6 papers) and Cancer-related molecular mechanisms research (4 papers). Usha Gutti is often cited by papers focused on Platelet Disorders and Treatments (6 papers), MicroRNA in disease regulation (6 papers) and Cancer-related molecular mechanisms research (4 papers). Usha Gutti collaborates with scholars based in India and United States. Usha Gutti's co-authors include Ravi Kumar Gutti, Ram Babu Undi, Sharmistha Bhattacharyya, Harvey B. Pollard, Roopa Biswas, Meera Srivastava, Nagaraja S. Balakathiresan, David L. Armistead, Clifton L. Dalgard and Ravinder Kandi and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Neuroscience and Experimental Cell Research.

In The Last Decade

Usha Gutti

21 papers receiving 631 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Usha Gutti India 13 313 191 158 94 83 21 638
Carson A. Wills United States 8 325 1.0× 127 0.7× 46 0.3× 44 0.5× 156 1.9× 14 647
Álvaro Gutiérrez-Uzquiza Spain 14 440 1.4× 101 0.5× 48 0.3× 80 0.9× 108 1.3× 35 736
Luca Simula France 17 466 1.5× 231 1.2× 57 0.4× 52 0.6× 50 0.6× 28 782
Patrycja Przygodzka Poland 15 265 0.8× 133 0.7× 32 0.2× 48 0.5× 58 0.7× 30 576
Peta Wood Australia 12 464 1.5× 138 0.7× 80 0.5× 35 0.4× 117 1.4× 13 600
Mingqiang Ren United States 18 586 1.9× 107 0.6× 40 0.3× 93 1.0× 36 0.4× 41 837
Shuai Yang China 17 499 1.6× 231 1.2× 38 0.2× 55 0.6× 52 0.6× 35 884
Pushpankur Ghoshal United States 15 289 0.9× 88 0.5× 59 0.4× 22 0.2× 79 1.0× 23 520
Longgui Chen United States 8 290 0.9× 97 0.5× 46 0.3× 17 0.2× 140 1.7× 13 566
Carolyn Kemp United States 9 381 1.2× 112 0.6× 47 0.3× 53 0.6× 186 2.2× 11 747

Countries citing papers authored by Usha Gutti

Since Specialization
Citations

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

Fields of papers citing papers by Usha Gutti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Usha Gutti. 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 Usha Gutti. The network helps show where Usha Gutti may publish in the future.

Co-authorship network of co-authors of Usha Gutti

This figure shows the co-authorship network connecting the top 25 collaborators of Usha Gutti. A scholar is included among the top collaborators of Usha Gutti 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 Usha Gutti. Usha Gutti 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.
Raghuwanshi, Sanjeev, Swati Dahariya, Usha Gutti, et al.. (2018). MicroRNA function in megakaryocytes. Platelets. 30(7). 809–816. 15 indexed citations
2.
Gutti, Usha, et al.. (2018). Justicia adhatoda induces megakaryocyte differentiation through mitochondrial ROS generation. Phytomedicine. 43. 135–139. 15 indexed citations
3.
Sahu, Itishri, et al.. (2018). Systems biology approach to study the role of miRNA in promoter targeting during megakaryopoiesis. Experimental Cell Research. 366(2). 192–198. 5 indexed citations
4.
Raghuwanshi, Sanjeev, et al.. (2018). Current Updates on Role of Lipids in Hematopoiesis. Infectious Disorders - Drug Targets. 18(3). 192–198. 8 indexed citations
5.
Raghuwanshi, Sanjeev, Swati Dahariya, Ravinder Kandi, et al.. (2017). Epigenetic Mechanisms: Role in Hematopoietic Stem Cell Lineage Commitment and Differentiation. Current Drug Targets. 19(14). 1683–1695. 15 indexed citations
6.
Undi, Ram Babu, Usha Gutti, & Ravi Kumar Gutti. (2016). LiCl regulates mitochondrial biogenesis during megakaryocyte development. Journal of Trace Elements in Medicine and Biology. 39. 193–201. 35 indexed citations
7.
Undi, Ram Babu, Usha Gutti, & Ravi Kumar Gutti. (2016). Role of let-7b/Fzd4 axis in mitochondrial biogenesis through wnt signaling: In neonatal and adult megakaryocytes. The International Journal of Biochemistry & Cell Biology. 79. 61–68. 8 indexed citations
8.
9.
Gutti, Usha, et al.. (2016). Erythropoietin and thrombopoietin mimetics: Natural alternatives to erythrocyte and platelet disorders. Critical Reviews in Oncology/Hematology. 108. 175–186. 7 indexed citations
10.
Undi, Ram Babu, et al.. (2015). Apoptosis: role in myeloid cell development. Blood Research. 50(2). 73–73. 24 indexed citations
11.
Karnati, Hanuma Kumar, et al.. (2015). microRNAs: Key Players in Hematopoiesis. Advances in experimental medicine and biology. 887. 171–211. 11 indexed citations
12.
Undi, Ram Babu, et al.. (2015). Wnt Signaling: Role in Regulation of Haematopoiesis. Indian Journal of Hematology and Blood Transfusion. 32(2). 123–134. 28 indexed citations
13.
Kandi, Ravinder, Usha Gutti, Ram Babu Undi, Itishri Sahu, & Ravi Kumar Gutti. (2015). Understanding thrombocytopenia: physiological role of microRNA in survival of neonatal megakaryocytes. Journal of Thrombosis and Thrombolysis. 40(3). 310–316. 7 indexed citations
14.
Sahu, Itishri, Seema Mishra, Ram Babu Undi, et al.. (2014). Sequence and structural difference favors a distinct preference of Wnt3a binding with co-receptor LRP6. Journal of Biomolecular Structure and Dynamics. 33(10). 2133–2144. 1 indexed citations
15.
Kandi, Ravinder, et al.. (2014). MiR-125b and miR-99a encoded on chromosome 21 co-regulate vincristine resistance in childhood acute megakaryoblastic leukemia. Hematology/Oncology and Stem Cell Therapy. 8(2). 95–97. 13 indexed citations
16.
Heilig, Elizabeth, Usha Gutti, T.C. Tai, Jun Shen, & Raymond J. Kelleher. (2013). Trans-Dominant Negative Effects of Pathogenic PSEN1 Mutations on  -Secretase Activity and A  Production. Journal of Neuroscience. 33(28). 11606–11617. 57 indexed citations
17.
Bhattacharyya, Sharmistha, Nagaraja S. Balakathiresan, Clifton L. Dalgard, et al.. (2011). Elevated miR-155 Promotes Inflammation in Cystic Fibrosis by Driving Hyperexpression of Interleukin-8. Journal of Biological Chemistry. 286(13). 11604–11615. 181 indexed citations
18.
Balakathiresan, Nagaraja S., Sharmistha Bhattacharyya, Usha Gutti, et al.. (2009). Tristetraprolin regulates IL-8 mRNA stability in cystic fibrosis lung epithelial cells. American Journal of Physiology-Lung Cellular and Molecular Physiology. 296(6). L1012–L1018. 40 indexed citations
19.
Ziegler, Shira G., Michael J. Eblan, Usha Gutti, et al.. (2007). Glucocerebrosidase mutations in Chinese subjects from Taiwan with sporadic Parkinson disease. Molecular Genetics and Metabolism. 91(2). 195–200. 87 indexed citations
20.
Eblan, Michael J., Sonja W. Scholz, Barbara Stubblefield, et al.. (2006). Glucocerebrosidase mutations are not found in association with LRRK2 G2019S in subjects with parkinsonism. Neuroscience Letters. 404(1-2). 163–165. 10 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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