Tim M. Curtis

5.7k total citations · 1 hit paper
105 papers, 4.5k citations indexed

About

Tim M. Curtis is a scholar working on Molecular Biology, Ophthalmology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Tim M. Curtis has authored 105 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Molecular Biology, 35 papers in Ophthalmology and 22 papers in Cellular and Molecular Neuroscience. Recurrent topics in Tim M. Curtis's work include Retinal Diseases and Treatments (31 papers), Retinal Development and Disorders (26 papers) and Glaucoma and retinal disorders (13 papers). Tim M. Curtis is often cited by papers focused on Retinal Diseases and Treatments (31 papers), Retinal Development and Disorders (26 papers) and Glaucoma and retinal disorders (13 papers). Tim M. Curtis collaborates with scholars based in United Kingdom, Slovakia and United States. Tim M. Curtis's co-authors include Alan W. Stitt, Tom A. Gardiner, C. Norman Scholfield, J. Graham McGeown, Reinhold J. Medina, Mary K. McGahon, Noemi Lois, Mei Chen, Timothy J. Lyons and Peter Bankhead and has published in prestigious journals such as Nature Communications, PLoS ONE and Circulation Research.

In The Last Decade

Tim M. Curtis

102 papers receiving 4.4k citations

Hit Papers

The progress in understanding and treatment of diabetic r... 2015 2026 2018 2022 2015 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tim M. Curtis United Kingdom 33 1.9k 1.7k 1.2k 569 517 105 4.5k
Robert N. Frank United States 44 2.9k 1.5× 2.1k 1.2× 1.7k 1.4× 595 1.0× 577 1.1× 134 6.1k
Masaki Tanito Japan 43 3.2k 1.7× 2.0k 1.1× 2.0k 1.7× 300 0.5× 197 0.4× 264 5.6k
Gareth J. McKay United Kingdom 27 1.2k 0.6× 1.0k 0.6× 909 0.8× 231 0.4× 111 0.2× 145 3.2k
Yoshihiro Noda Japan 33 691 0.4× 829 0.5× 598 0.5× 453 0.8× 175 0.3× 117 2.9k
Chang‐Hao Yang Taiwan 34 2.5k 1.3× 1.0k 0.6× 1.8k 1.5× 110 0.2× 115 0.2× 239 4.1k
Ye Yang China 35 282 0.1× 899 0.5× 350 0.3× 217 0.4× 32 0.1× 193 3.6k
Emmanuel Van Obberghen France 54 165 0.1× 6.1k 3.5× 244 0.2× 2.0k 3.4× 428 0.8× 150 9.7k
Qiaobing Huang China 37 106 0.1× 1.8k 1.1× 84 0.1× 663 1.2× 369 0.7× 142 4.1k

Countries citing papers authored by Tim M. Curtis

Since Specialization
Citations

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

Fields of papers citing papers by Tim M. Curtis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tim M. Curtis. 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 Tim M. Curtis. The network helps show where Tim M. Curtis may publish in the future.

Co-authorship network of co-authors of Tim M. Curtis

This figure shows the co-authorship network connecting the top 25 collaborators of Tim M. Curtis. A scholar is included among the top collaborators of Tim M. Curtis 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 Tim M. Curtis. Tim M. Curtis 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.
Constantinou, Anthony C., et al.. (2025). Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes. Knowledge-Based Systems. 329. 114382–114382. 1 indexed citations
2.
Wood, Heather, Heping Xu, Timothy J. Lyons, et al.. (2024). Relaxation of mitochondrial hyperfusion in the diabetic retina via N6-furfuryladenosine confers neuroprotection regardless of glycaemic status. Nature Communications. 15(1). 1124–1124. 9 indexed citations
3.
Lundy, Fionnuala T., et al.. (2024). Immunological isolation and characterization of neuronal progenitors from human dental pulp: A laboratory‐based investigation. International Endodontic Journal. 57(8). 1136–1146.
4.
Little, Karis, et al.. (2023). Aldehyde Dehydrogenase and Aldo-Keto Reductase Enzymes: Basic Concepts and Emerging Roles in Diabetic Retinopathy. Antioxidants. 12(7). 1466–1466. 6 indexed citations
5.
Balowski, Joseph, Jianhong Ou, Lingyun Song, et al.. (2022). Identification of enhancer regulatory elements that direct epicardial gene expression during zebrafish heart regeneration. Development. 149(4). 20 indexed citations
6.
O’Hare, Michael, Gema Esquiva, Mary K. McGahon, et al.. (2022). Loss of TRPV2-mediated blood flow autoregulation recapitulates diabetic retinopathy in rats. JCI Insight. 7(18). 16 indexed citations
7.
Augustine, Josy, et al.. (2021). The Role of Lipoxidation in the Pathogenesis of Diabetic Retinopathy. Frontiers in Endocrinology. 11. 621938–621938. 57 indexed citations
8.
Hombrebueno, José R., Timothy J. Lyons, Derek P. Brazil, et al.. (2019). Uncoupled turnover disrupts mitochondrial quality control in diabetic retinopathy. JCI Insight. 4(23). 44 indexed citations
9.
Ashraf, Sadaf, Paul Canning, Ileana Micu, et al.. (2019). CAMKII as a therapeutic target for growth factor-induced retinal and choroidal neovascularisation. JCI Insight. 4(6). 12 indexed citations
10.
Curtis, Tim M., et al.. (2017). Zinc Protects Oxidative Stress‐Induced RPE Death by Reducing Mitochondrial Damage and Preventing Lysosome Rupture. Oxidative Medicine and Cellular Longevity. 2017(1). 6926485–6926485. 35 indexed citations
11.
O’Hare, Michael, et al.. (2017). Diabetes impairs TRPV2 channel activity in rat retinal arterioles.. Investigative Ophthalmology & Visual Science. 58(8). 4052–4052. 1 indexed citations
12.
Karim, Ikhlas El, Maelíosa McCrudden, Gerard J. Linden, et al.. (2015). TNFα-induced p38 MAPK activation regulates TRPA1 and TRPV4 activity in human odontoblast-like cells. Journal of Dental Research. 1 indexed citations
13.
Karim, Ikhlas El, Maelíosa McCrudden, Gerard J. Linden, et al.. (2015). TNF-α–Induced p38MAPK Activation Regulates TRPA1 and TRPV4 Activity in Odontoblast-Like Cells. American Journal Of Pathology. 185(11). 2994–3002. 57 indexed citations
14.
Curtis, Tim M., et al.. (2013). Advanced Glycation End Products and Diabetic Retinopathy. Current Medicinal Chemistry. 20(26). 3234–3240. 96 indexed citations
15.
Karim, Ikhlas El, Gerard J. Linden, Tim M. Curtis, et al.. (2011). Human Dental Pulp Fibroblasts Express the “Cold-sensing” Transient Receptor Potential Channels TRPA1 and TRPM8. Journal of Endodontics. 37(4). 473–478. 54 indexed citations
16.
McGahon, Mary K., et al.. (2009). Ca2+-Activated ClCurrent in Retinal Arteriolar Smooth Muscle. Investigative Ophthalmology & Visual Science. 50(1). 364–364. 14 indexed citations
17.
Scholfield, C. Norman, J. Graham McGeown, & Tim M. Curtis. (2007). Cellular Physiology of Retinal and Choroidal Arteriolar Smooth Muscle Cells. Microcirculation. 14(1). 11–24. 29 indexed citations
18.
Curtis, Tim M., et al.. (2007). Calcium Signaling in Ocular Arterioles. Critical Reviews in Eukaryotic Gene Expression. 17(1). 1–12. 11 indexed citations
19.
McGahon, Mary K., Jennine M. Dawicki-McKenna, C. Norman Scholfield, J. Graham McGeown, & Tim M. Curtis. (2005). A-Type Potassium Current in Retinal Arteriolar Smooth Muscle Cells. Investigative Ophthalmology & Visual Science. 46(9). 3281–3281. 28 indexed citations
20.
Scholfield, C. Norman & Tim M. Curtis. (2000). Heterogeneity in Cytosolic Calcium Regulation among Different Microvascular Smooth Muscle Cells of the Rat Retina. Microvascular Research. 59(2). 233–242. 37 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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