Louise S. Mackenzie

873 total citations
20 papers, 665 citations indexed

About

Louise S. Mackenzie is a scholar working on Molecular Biology, Biochemistry and Pharmacology. According to data from OpenAlex, Louise S. Mackenzie has authored 20 papers receiving a total of 665 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 7 papers in Biochemistry and 6 papers in Pharmacology. Recurrent topics in Louise S. Mackenzie's work include Eicosanoids and Hypertension Pharmacology (7 papers), Inflammatory mediators and NSAID effects (6 papers) and Nitric Oxide and Endothelin Effects (5 papers). Louise S. Mackenzie is often cited by papers focused on Eicosanoids and Hypertension Pharmacology (7 papers), Inflammatory mediators and NSAID effects (6 papers) and Nitric Oxide and Endothelin Effects (5 papers). Louise S. Mackenzie collaborates with scholars based in United Kingdom, Saudi Arabia and United States. Louise S. Mackenzie's co-authors include Lisa A. Lione, Stephen J. Lye, M Mascarenhas, Teresa Petrocelli, Nicholas S. Kirkby, Jane A. Mitchell, Christopher D. Benham, Surajit Ray, Abhirup Banerjee and Bart Vorselaars and has published in prestigious journals such as Circulation, PLoS ONE and Scientific Reports.

In The Last Decade

Louise S. Mackenzie

20 papers receiving 655 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Louise S. Mackenzie United Kingdom 13 180 113 108 90 85 20 665
Lorraine King United States 15 195 1.1× 91 0.8× 101 0.9× 228 2.5× 33 0.4× 36 777
Wei Gong China 17 349 1.9× 70 0.6× 71 0.7× 26 0.3× 91 1.1× 74 979
Yawen Zhou China 17 246 1.4× 28 0.2× 45 0.4× 20 0.2× 123 1.4× 38 734
Oana A. Zeleznik United States 20 685 3.8× 28 0.2× 53 0.5× 24 0.3× 206 2.4× 59 1.2k
Masataka Takamiya Japan 19 524 2.9× 143 1.3× 54 0.5× 42 0.5× 22 0.3× 45 1.2k
Surabhi Chandra United States 15 226 1.3× 41 0.4× 16 0.1× 83 0.9× 242 2.8× 37 996
Kazunori Morita Japan 15 70 0.4× 56 0.5× 22 0.2× 71 0.8× 55 0.6× 43 603
Chien-an A. Hu United States 13 699 3.9× 29 0.3× 26 0.2× 85 0.9× 81 1.0× 20 1.3k

Countries citing papers authored by Louise S. Mackenzie

Since Specialization
Citations

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

Fields of papers citing papers by Louise S. Mackenzie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Louise S. Mackenzie. 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 Louise S. Mackenzie. The network helps show where Louise S. Mackenzie may publish in the future.

Co-authorship network of co-authors of Louise S. Mackenzie

This figure shows the co-authorship network connecting the top 25 collaborators of Louise S. Mackenzie. A scholar is included among the top collaborators of Louise S. Mackenzie 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 Louise S. Mackenzie. Louise S. Mackenzie 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.
Banerjee, Abhirup, Michail Mamalakis, Surajit Ray, et al.. (2022). Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers. International Journal of Molecular Sciences. 23(13). 7260–7260. 4 indexed citations
2.
Ray, Surajit, Abhirup Banerjee, Andrew J. Swift, et al.. (2022). A robust COVID-19 mortality prediction calculator based on Lymphocyte count, Urea, C-Reactive Protein, Age and Sex (LUCAS) with chest X-rays. Scientific Reports. 12(1). 18220–18220. 4 indexed citations
3.
Lione, Lisa A., et al.. (2021). Co-Incubation with PPARβ/δ Agonists and Antagonists Modeled Using Computational Chemistry: Effect on LPS Induced Inflammatory Markers in Pulmonary Artery. International Journal of Molecular Sciences. 22(6). 3158–3158. 4 indexed citations
4.
Ostovar, Mehrnoosh, M.L. Scott, Tomasz P. Radon, et al.. (2020). Discovery of novel small molecule inhibitors of S100P with in vitro anti-metastatic effects on pancreatic cancer cells. European Journal of Medicinal Chemistry. 203. 112621–112621. 26 indexed citations
5.
Banerjee, Abhirup, Surajit Ray, Bart Vorselaars, et al.. (2020). Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population. International Immunopharmacology. 86. 106705–106705. 120 indexed citations
6.
Souza, Alain, et al.. (2018). An overview of anti-diabetic plants used in Gabon: Pharmacology and toxicology. Journal of Ethnopharmacology. 216. 203–228. 21 indexed citations
7.
Mackenzie, Louise S.. (2017). Thyroid Hormone Receptor Antagonists: From Environmental Pollution to Novel Small Molecules. Vitamins and hormones. 106. 147–162. 13 indexed citations
8.
Zloh, Mire, et al.. (2016). Evidence that diclofenac and celecoxib are thyroid hormone receptor beta antagonists. Life Sciences. 146. 66–72. 17 indexed citations
9.
Mackenzie, Louise S., et al.. (2016). Methylglyoxal, A Metabolite Increased in Diabetes is Associated with Insulin Resistance, Vascular Dysfunction and Neuropathies. Current Drug Metabolism. 17(4). 359–367. 54 indexed citations
10.
Zloh, Mire, et al.. (2015). In silico modelling of prostacyclin and other lipid mediators to nuclear receptors reveal novel thyroid hormone receptor antagonist properties. Prostaglandins & Other Lipid Mediators. 122. 18–27. 5 indexed citations
11.
Mackenzie, Louise S., et al.. (2015). Linking Induction and Transrepression of PPARβ/δ with Cellular Function. Annual Research & Review in Biology. 6(4). 253–263. 3 indexed citations
12.
Mitchell, Jane A., Blerina Ahmetaj‐Shala, Nicholas S. Kirkby, et al.. (2014). Role of prostacyclin in pulmonary hypertension. Global Cardiology Science and Practice. 2014(4). 53–53. 59 indexed citations
13.
Ahmetaj‐Shala, Blerina, Nicholas S. Kirkby, Zhen Wang, et al.. (2014). Evidence That Links Loss of Cyclooxygenase-2 With Increased Asymmetric Dimethylarginine. Circulation. 131(7). 633–642. 69 indexed citations
14.
Mackenzie, Louise S., Joanne Lymn, & Alun D. Hughes. (2013). Linking phospholipase C isoforms with differentiation function in human vascular smooth muscle cells. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 1833(12). 3006–3012. 7 indexed citations
15.
Mackenzie, Louise S. & Lisa A. Lione. (2013). Harnessing the benefits of PPARβ/δ agonists. Life Sciences. 93(25-26). 963–967. 33 indexed citations
16.
Kirkby, Nicholas S., Anne K. Zaiss, Paula Urquhart, et al.. (2013). LC-MS/MS Confirms That COX-1 Drives Vascular Prostacyclin Whilst Gene Expression Pattern Reveals Non-Vascular Sites of COX-2 Expression. PLoS ONE. 8(7). e69524–e69524. 50 indexed citations
17.
Benham, Christopher D., et al.. (2013). Selective inhibition of NADPH oxidase reverses the over contraction of diabetic rat aorta. Redox Biology. 2. 61–64. 17 indexed citations
18.
Mackenzie, Louise S., et al.. (2013). Nitric oxide‐dependent vasodilation is compromised in isolated pulmonary arteries from COX knockout mice. The FASEB Journal. 27(S1). 1 indexed citations
19.
Kirkby, Nicholas S., Melissa V. Chan, Martina H. Lundberg, et al.. (2013). Aspirin‐triggered 15‐epi‐lipoxin A 4 predicts cyclooxygenase‐2 in the lungs of LPS‐treated mice but not in the circulation: implications for a clinical test. The FASEB Journal. 27(10). 3938–3946. 17 indexed citations
20.

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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