Bhanu P. Jena

4.1k total citations
120 papers, 3.0k citations indexed

About

Bhanu P. Jena is a scholar working on Molecular Biology, Cell Biology and Physiology. According to data from OpenAlex, Bhanu P. Jena has authored 120 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 90 papers in Molecular Biology, 66 papers in Cell Biology and 30 papers in Physiology. Recurrent topics in Bhanu P. Jena's work include Lipid Membrane Structure and Behavior (64 papers), Cellular transport and secretion (63 papers) and Erythrocyte Function and Pathophysiology (22 papers). Bhanu P. Jena is often cited by papers focused on Lipid Membrane Structure and Behavior (64 papers), Cellular transport and secretion (63 papers) and Erythrocyte Function and Pathophysiology (22 papers). Bhanu P. Jena collaborates with scholars based in United States, Sweden and Germany. Bhanu P. Jena's co-authors include Aleksandar Jeremić, Won Jin Cho, Sang‐Joon Cho, John P. Geibel, Stefan W. Schneider, Hans Oberleithner, J. K. H. Hörber, Marvin H. Stromer, Kumudesh C. Sritharan and Rania Abu‐Hamdah and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nano Letters.

In The Last Decade

Bhanu P. Jena

117 papers receiving 2.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bhanu P. Jena United States 32 2.0k 1.5k 656 442 412 120 3.0k
Nobuhiro Morone Japan 31 2.6k 1.3× 1.3k 0.9× 419 0.6× 288 0.7× 185 0.4× 60 4.1k
Kate Poole Australia 27 1.0k 0.5× 637 0.4× 803 1.2× 78 0.2× 260 0.6× 50 2.3k
Kazuhiro Kohama Japan 32 1.7k 0.8× 1.1k 0.8× 345 0.5× 95 0.2× 142 0.3× 153 3.3k
С. В. Зайцев Russia 32 1.2k 0.6× 265 0.2× 421 0.6× 921 2.1× 232 0.6× 135 3.1k
Aleksander F. Sikorski Poland 30 1.8k 0.9× 718 0.5× 952 1.5× 262 0.6× 50 0.1× 144 3.0k
Gustavo Egea Spain 37 2.1k 1.1× 1.4k 0.9× 437 0.7× 224 0.5× 42 0.1× 105 3.7k
Elena V. Sviderskaya United Kingdom 36 1.8k 0.9× 2.1k 1.4× 304 0.5× 79 0.2× 151 0.4× 68 3.5k
James Gulick United States 41 4.0k 2.0× 673 0.4× 246 0.4× 360 0.8× 87 0.2× 80 5.6k
H. Randolph Byers United States 35 2.4k 1.2× 1.7k 1.1× 395 0.6× 178 0.4× 61 0.1× 63 4.7k
Evelyn Ralston United States 41 3.1k 1.5× 1.3k 0.8× 1.5k 2.3× 340 0.8× 55 0.1× 74 4.7k

Countries citing papers authored by Bhanu P. Jena

Since Specialization
Citations

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

Fields of papers citing papers by Bhanu P. Jena

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bhanu P. Jena. 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 Bhanu P. Jena. The network helps show where Bhanu P. Jena may publish in the future.

Co-authorship network of co-authors of Bhanu P. Jena

This figure shows the co-authorship network connecting the top 25 collaborators of Bhanu P. Jena. A scholar is included among the top collaborators of Bhanu P. Jena 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 Bhanu P. Jena. Bhanu P. Jena 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.
Li, Meishan, et al.. (2024). Quantum dot-based thermometry uncovers decreased myosin efficiency in an experimental intensive care unit model. Frontiers in Physiology. 15. 1485249–1485249.
2.
Kim, Hyunbae, Juncheng Wei, Zhenfeng Song, et al.. (2021). Regulation of hepatic circadian metabolism by the E3 ubiquitin ligase HRD1-controlled CREBH/PPARα transcriptional program. Molecular Metabolism. 49. 101192–101192. 22 indexed citations
3.
Jena, Bhanu P.. (2020). ATP Synthase: Energy Generating Machinery in Cells. 57–62.
4.
Cacciani, Nicola, Heba Salah, Meishan Li, et al.. (2019). Chaperone co‐inducer BGP‐15 mitigates early contractile dysfunction of the soleus muscle in a rat ICU model. Acta Physiologica. 229(1). e13425–e13425. 19 indexed citations
5.
Lewis, Kenneth T., Krishna Rao Maddipati, Xuequn Chen, et al.. (2019). Self-Assembly and Biogenesis of the Cellular Membrane are Dictated by Membrane Stretch and Composition. The Journal of Physical Chemistry B. 123(32). 6997–7005. 3 indexed citations
6.
Liu, Ming, Xuebao Zhang, Takeshi Sakamoto, et al.. (2015). COPII-Dependent ER Export: A Critical Component of Insulin Biogenesis and β-Cell ER Homeostasis. Molecular Endocrinology. 29(8). 1156–1169. 31 indexed citations
7.
8.
Lewis, Kenneth T., et al.. (2014). Proteome of the insulin-secreting Min6 cell porosome complex: Involvement of Hsp90 in its assembly and function. Journal of Proteomics. 114. 83–92. 8 indexed citations
9.
Mao, Guangzhao, et al.. (2011). Lysophosphatidylcholine inhibits membrane‐associated SNARE complex disassembly. Journal of Cellular and Molecular Medicine. 16(8). 1701–1708. 5 indexed citations
10.
Cho, Won Jin, et al.. (2010). Involvement of β-adrenergic receptor in synaptic vesicle swelling and implication in neurotransmitter release. Journal of Cellular and Molecular Medicine. 15(3). 572–576. 10 indexed citations
11.
Jena, Bhanu P.. (2009). Membrane Fusion: Role of SNAREs and Calcium. Protein and Peptide Letters. 16(7). 712–717. 22 indexed citations
12.
Cho, Won‐Jin, Bhanu P. Jena, & Aleksandar Jeremić. (2008). Chapter 13 Nano‐Scale Imaging and Dynamics of Amylin‐Membrane Interactions and Its Implication in Type II Diabetes Mellitus. Methods in cell biology. 90. 267–286. 34 indexed citations
13.
Potoff, Jeffrey J., et al.. (2008). Ca2+–dimethylphosphate complex formation: Providing insight into Ca2+‐mediated local dehydration and membrane fusion in cells. Cell Biology International. 32(4). 361–366. 35 indexed citations
14.
Ren, Gang, et al.. (2008). EM 3D contour maps provide protein assembly at the nanoscale within the neuronal porosome complex. Journal of Microscopy. 232(1). 106–111. 29 indexed citations
15.
Cho, Won Jin, Aleksandar Jeremić, Huan Jin, Gang Ren, & Bhanu P. Jena. (2007). Neuronal fusion pore assembly requires membrane cholesterol. Cell Biology International. 31(11). 1301–1308. 49 indexed citations
16.
Jena, Bhanu P.. (2006). Tribute to Professor Bhanu P. Jena. Journal of Cellular and Molecular Medicine. 10(2). 270–270. 10 indexed citations
17.
Abu‐Hamdah, Rania, Won‐Jin Cho, Sang‐Joon Cho, et al.. (2003). Regulation of the water channel aquaporin‐1: isolation and reconstitution of the regulatory complex. Cell Biology International. 28(1). 7–17. 57 indexed citations
18.
Jena, Bhanu P. & J. K. H. Hörber. (2002). Atomic force microscopy in cell biology. Academic Press eBooks. 71 indexed citations
19.
Cho, Sang‐Joon, et al.. (2002). Structure and Dynamics of the Fusion Pores in Live GH-Secreting Cells Revealed Using Atomic Force Microscopy. Endocrinology. 143(3). 1144–1144. 93 indexed citations
20.
Basson, Marc D., et al.. (1994). Effect of tyrosine kinase inhibition on basal and epidermal growth factor‐stimulated human Caco‐2 enterocyte sheet migration and proliferation. Journal of Cellular Physiology. 160(3). 491–501. 31 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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