Deepa Thaloor

1.4k citations
8 papers · 1.2k · h-index 8

Impact in

Papers in

    • Muscle Physiology and Disorders 5
    • Tissue Engineering and Regenerative Medicine 3
    • Knee injuries and reconstruction techniques 2

Deepa Thaloor

8 papers receiving 1.1k citations

Peers

Deepa Thaloor
Comparison fields: 5 of 103
  • Molecular Medicine 278
  • Rehabilitation 193
  • Genetics 128
  • Molecular Biology 702
  • Pharmaceutical Science 39
Replace Elahe Mahdipour with:
Elahe Mahdipour Iran
Franziska Busch Germany
Jingjuan Huang China
Shuwen Deng China
Bai‐Cheng He China
Xuqiang Nie China
Pankaj Chaturvedi United States
Masoud Darabi Iran
Thangavelu Soundara Rajan Italy
Ki‐Sook Park South Korea
Deepa Thaloor relative to Elahe Mahdipour Iran Elahe Mahdipour's profile →
Citations per field
00.5×1.5×2.2×
Elahe Mahdipour · 1×
Citations per year

Countries citing papers authored by Deepa Thaloor

Since Specialization
Citations

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

Fields of papers citing papers by Deepa Thaloor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 19 scholars most cited alongside Deepa Thaloor, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Deepa Thaloor Line = papers co-authored together Deepa Thaloor links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 1998310
2 2000197
3 1998194
4 1999190
5
Inhibition of angiogenic differentiation of human umbilical vein endothelial cells by curcumin.
1998148
6 1998141
7 199612
8 19987

About Deepa Thaloor

Deepa Thaloor is a scholar working on Molecular Biology, Surgery, Rehabilitation, Molecular Medicine and Cellular and Molecular Neuroscience, having authored 8 papers that have together received 1.2k indexed citations. Recurring topics across this work include Muscle Physiology and Disorders (5 papers), Tissue Engineering and Regenerative Medicine (3 papers), Wound Healing and Treatments (2 papers), Knee injuries and reconstruction techniques (2 papers), Curcumin's Biomedical Applications (2 papers), Silk-based biomaterials and applications (1 paper), NF-κB Signaling Pathways (1 paper) and Natural Fiber Reinforced Composites (1 paper). The work is most often cited by research in Molecular Medicine (278 citations), Rehabilitation (193 citations), Genetics (128 citations), Molecular Biology (702 citations) and Pharmaceutical Science (39 citations). Deepa Thaloor has collaborated with scholars based in United States and India. Frequent co-authors include Grace K. Pavlath, Radha K. Maheshwari, Gurmel S. Sidhu, Anoop Singh, Krishna Banaudha, R.C. Srimal, Gyanendra K. Patnaik, Thomas J. Murphy, Karen L. Abbott and Bret B. Friday. Their work appears in journals such as American Journal of Physiology-Cell Physiology, Developmental Dynamics, Wound Repair and Regeneration, Molecular Biology of the Cell and Journal of Cellular Physiology.

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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