Damodaran Narayanan

1.5k total citations · 1 hit paper
29 papers, 1.1k citations indexed

About

Damodaran Narayanan is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Sensory Systems. According to data from OpenAlex, Damodaran Narayanan has authored 29 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 9 papers in Cellular and Molecular Neuroscience and 6 papers in Sensory Systems. Recurrent topics in Damodaran Narayanan's work include Ion channel regulation and function (11 papers), Neuroscience and Neuropharmacology Research (6 papers) and Ion Channels and Receptors (6 papers). Damodaran Narayanan is often cited by papers focused on Ion channel regulation and function (11 papers), Neuroscience and Neuropharmacology Research (6 papers) and Ion Channels and Receptors (6 papers). Damodaran Narayanan collaborates with scholars based in United States. Damodaran Narayanan's co-authors include Jonathan H. Jaggar, Adebowale Adebiyi, John P. Bannister, Olga K. Weinberg, M. Dennis Leo, Geetha Samak, Radhakrishna Rao, Guiling Zhao, Frederick A. Boop and Kyle S. Gabrick and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and SHILAP Revista de lepidopterología.

In The Last Decade

Damodaran Narayanan

27 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

TP53 mutation defines a unique subgroup within complex karyotype de novo and therapy-related MDS/AML

2022 100 citations Olga K. Weinberg, Alexa J. Siddon et al. Blood Advances profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Damodaran Narayanan United States	20	663	224	223	211	171	29	1.1k
Stefan Amisten Sweden	26	780 1.2×	197 0.9×	130 0.6×	58 0.3×	139 0.8×	44	1.7k
Kinya Seo United States	16	728 1.1×	220 1.0×	397 1.8×	120 0.6×	123 0.7×	22	1.4k
Songwei Wu United States	24	979 1.5×	399 1.8×	246 1.1×	520 2.5×	233 1.4×	44	1.8k
Anita K. Salyers United States	16	202 0.3×	247 1.1×	221 1.0×	52 0.2×	206 1.2×	21	942
Christina Leibrock Germany	17	350 0.5×	95 0.4×	88 0.4×	101 0.5×	60 0.4×	25	932
Gongxiong Wu United States	18	354 0.5×	223 1.0×	54 0.2×	37 0.2×	239 1.4×	23	1.1k
William M. Isenberg United States	17	377 0.6×	437 2.0×	113 0.5×	93 0.4×	279 1.6×	26	1.4k
Angela K. Best United States	17	1.0k 1.6×	498 2.2×	201 0.9×	68 0.3×	70 0.4×	23	1.5k
Ahmed Chraïbi Canada	17	915 1.4×	165 0.7×	66 0.3×	168 0.8×	152 0.9×	21	1.3k
Marion Gericke Belgium	13	438 0.7×	264 1.2×	179 0.8×	189 0.9×	187 1.1×	14	761

Countries citing papers authored by Damodaran Narayanan

Since Specialization

Citations

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

Fields of papers citing papers by Damodaran Narayanan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Damodaran Narayanan

This figure shows the co-authorship network connecting the top 25 collaborators of Damodaran Narayanan. A scholar is included among the top collaborators of Damodaran Narayanan 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 Damodaran Narayanan. Damodaran Narayanan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Goyal, Amrita, Damodaran Narayanan, Alvaro C. Laga, et al.. (2022). Tocilizumab for treatment of cutaneous and systemic manifestations of vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic (VEXAS) syndrome without myelodysplastic syndrome. JAAD Case Reports. 23. 15–19. 47 indexed citations

Weinberg, Olga K., Alexa J. Siddon, Yazan F. Madanat, et al.. (2022). TP53 mutation defines a unique subgroup within complex karyotype de novo and therapy-related MDS/AML. Blood Advances. 6(9). 2847–2853. 100 indexed citations breakdown →

Narayanan, Damodaran, Olga Pozdnyakova, Robert P. Hasserjian, Sanjay S. Patel, & Olga K. Weinberg. (2021). Effect of DNMT3A variant allele frequency and double mutation on clinicopathologic features of patients with de novo AML. Blood Advances. 5(11). 2539–2549. 10 indexed citations

Kessler, M, et al.. (2020). On the tip of my tongue: A 76‐year‐old female kidney transplant patient with tongue ulcer. Transplant Infectious Disease. 22(6). e13389–e13389.

Narayanan, Damodaran & Olga K. Weinberg. (2019). How I investigate acute myeloid leukemia. International Journal of Laboratory Hematology. 42(1). 3–15. 49 indexed citations

Samak, Geetha, Ruchika Gangwar, Avtar S. Meena, et al.. (2016). Calcium Channels and Oxidative Stress Mediate a Synergistic Disruption of Tight Junctions by Ethanol and Acetaldehyde in Caco-2 Cell Monolayers. Scientific Reports. 6(1). 38899–38899. 34 indexed citations

Wang, Qian, M. Dennis Leo, Damodaran Narayanan, Korah P. Kuruvilla, & Jonathan H. Jaggar. (2016). Local coupling of TRPC6 to ANO1/TMEM16A channels in smooth muscle cells amplifies vasoconstriction in cerebral arteries. American Journal of Physiology-Cell Physiology. 310(11). C1001–C1009. 45 indexed citations

Leo, M. Dennis, John P. Bannister, Damodaran Narayanan, et al.. (2014). Dynamic regulation of β1 subunit trafficking controls vascular contractility. Proceedings of the National Academy of Sciences. 111(6). 2361–2366. 70 indexed citations

Samak, Geetha, Kamaljit K. Chaudhry, Ruchika Gangwar, et al.. (2014). Calcium/Ask1/MKK7/JNK2/c-Src signalling cascade mediates disruption of intestinal epithelial tight junctions by dextran sulfate sodium. Biochemical Journal. 465(3). 503–515. 84 indexed citations

10.

Bannister, John P., M. Dennis Leo, Damodaran Narayanan, et al.. (2013). The voltage‐dependent L‐type Ca²⁺ (Ca_V1.2) channel C‐terminus fragment is a bi‐modal vasodilator. The Journal of Physiology. 591(12). 2987–2998. 27 indexed citations

11.

Narayanan, Damodaran, Simon Bulley, M. Dennis Leo, et al.. (2013). Smooth muscle cell transient receptor potential polycystin‐2 (TRPP2) channels contribute to the myogenic response in cerebral arteries. The Journal of Physiology. 591(20). 5031–5046. 67 indexed citations

12.

Kidd, Michael W., et al.. (2013). Intravascular pressure stimulates functional Kv1.5 surface expression in mesenteric artery smooth muscle cells. The FASEB Journal. 27(S1). 1 indexed citations

13.

Narayanan, Damodaran, Simon Bulley, M. Dennis Leo, et al.. (2013). Smooth muscle cell transient receptor potential polycystin (TRPP)2 channels contribute to the myogenic response in cerebral arteries. The FASEB Journal. 27(S1). 1 indexed citations

14.

Schwingshackl, Andreas, Bin Teng, Manik C. Ghosh, et al.. (2012). Regulation of interleukin-6 secretion by the two-pore-domain potassium channel Trek-1 in alveolar epithelial cells. American Journal of Physiology-Lung Cellular and Molecular Physiology. 304(4). L276–L286. 32 indexed citations

15.

Narayanan, Damodaran, Adebowale Adebiyi, & Jonathan H. Jaggar. (2012). Inositol trisphosphate receptors in smooth muscle cells. American Journal of Physiology-Heart and Circulatory Physiology. 302(11). H2190–H2210. 70 indexed citations

16.

Samak, Geetha, Damodaran Narayanan, Jonathan H. Jaggar, & Radhakrishna Rao. (2011). CaV1.3 Channels and Intracellular Calcium Mediate Osmotic Stress-induced N-terminal c-Jun Kinase Activation and Disruption of Tight Junctions in Caco-2 Cell Monolayers. Journal of Biological Chemistry. 286(34). 30232–30243. 45 indexed citations

17.

Narayanan, Damodaran, Qi Xi, Lawrence M. Pfeffer, & Jonathan H. Jaggar. (2010). Mitochondria Control Functional Ca _V 1.2 Expression in Smooth Muscle Cells of Cerebral Arteries. Circulation Research. 107(5). 631–641. 63 indexed citations

18.

Adebiyi, Adebowale, Guiling Zhao, Damodaran Narayanan, et al.. (2010). Isoform-Selective Physical Coupling of TRPC3 Channels to IP3 Receptors in Smooth Muscle Cells Regulates Arterial Contractility. Biophysical Journal. 98(3). 343a–343a. 3 indexed citations

19.

Adebiyi, Adebowale, Damodaran Narayanan, & Jonathan H. Jaggar. (2010). Caveolin-1 Assembles Type 1 Inositol 1,4,5-Trisphosphate Receptors and Canonical Transient Receptor Potential 3 Channels into a Functional Signaling Complex in Arterial Smooth Muscle Cells. Journal of Biological Chemistry. 286(6). 4341–4348. 67 indexed citations

20.

Xi, Qi, Edward S. Umstot, Guiling Zhao, et al.. (2009). Glutamate regulates Ca²⁺ signals in smooth muscle cells of newborn piglet brain slice arterioles through astrocyte- and heme oxygenase-dependent mechanisms. American Journal of Physiology-Heart and Circulatory Physiology. 298(2). H562–H569. 27 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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