John Moat

1.2k total citations
18 papers, 513 citations indexed

About

John Moat is a scholar working on Molecular Biology, Molecular Medicine and Infectious Diseases. According to data from OpenAlex, John Moat has authored 18 papers receiving a total of 513 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 6 papers in Molecular Medicine and 4 papers in Infectious Diseases. Recurrent topics in John Moat's work include Antibiotic Resistance in Bacteria (6 papers), Bacteriophages and microbial interactions (3 papers) and Dermatology and Skin Diseases (3 papers). John Moat is often cited by papers focused on Antibiotic Resistance in Bacteria (6 papers), Bacteriophages and microbial interactions (3 papers) and Dermatology and Skin Diseases (3 papers). John Moat collaborates with scholars based in United Kingdom, Canada and Australia. John Moat's co-authors include Mathew Upton, Christopher G. Dowson, Graeme Fox, Stephen Eyre, Madhura Castelino, Pauline Ho, Anne Barton, Paul Martin, Freya Harrison and Eleanor Jameson and has published in prestigious journals such as The Lancet, Scientific Reports and ACS Applied Materials & Interfaces.

In The Last Decade

John Moat

16 papers receiving 508 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John Moat United Kingdom	10	193	156	114	70	61	18	513
Ching-Wen Huang Taiwan	6	200 1.0×	122 0.8×	88 0.8×	30 0.4×	35 0.6×	8	745
Zhen Luo China	14	289 1.5×	59 0.4×	103 0.9×	32 0.5×	122 2.0×	23	516
Damian Neubauer Poland	18	555 2.9×	161 1.0×	585 5.1×	34 0.5×	76 1.2×	46	914
Germana Lentini Italy	15	181 0.9×	33 0.2×	45 0.4×	49 0.7×	46 0.8×	31	640
Montserrat Pujol Spain	14	301 1.6×	53 0.3×	89 0.8×	19 0.3×	34 0.6×	52	727
Damien Keogh Singapore	9	214 1.1×	59 0.4×	77 0.7×	40 0.6×	69 1.1×	9	471
James A. Booth United States	16	333 1.7×	71 0.5×	35 0.3×	62 0.9×	53 0.9×	44	840
Paweł Stączek Poland	21	463 2.4×	402 2.6×	25 0.2×	70 1.0×	63 1.0×	67	1.1k
Louise Thomas United Kingdom	10	288 1.5×	136 0.9×	38 0.3×	23 0.3×	55 0.9×	14	550
Cristiane de Souza Carvalho‐Wodarz Germany	15	190 1.0×	50 0.3×	64 0.6×	22 0.3×	73 1.2×	22	632

Countries citing papers authored by John Moat

Since Specialization

Citations

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

Fields of papers citing papers by John Moat

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Moat

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

All Works

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18 of 18 papers shown

1.

Rogers, Nicola J., et al.. (2025). Membrane lipid composition directs the cellular selectivity of antimicrobial metallohelices. RSC Medicinal Chemistry. 16(5). 2249–2260. 1 indexed citations

2.

Stefanini, Irene, Houdini H.T. Wu, Li Xu-McCrae, et al.. (2022). Genomic Assembly of Clinical Candida glabrata (Nakaseomyces glabrata) Isolates Reveals within-Species Structural Plasticity and Association with In Vitro Antifungal Susceptibility. Microbiology Spectrum. 10(6). e0182722–e0182722. 4 indexed citations

3.

Alav, Ilyas, et al.. (2021). Antibiotic Efficacy Testing in an Ex vivo Model of Pseudomonas aeruginosa and Staphylococcus aureus Biofilms in the Cystic Fibrosis Lung. Journal of Visualized Experiments.

4.

Alav, Ilyas, et al.. (2021). Antibiotic Efficacy Testing in an Ex vivo Model of Pseudomonas aeruginosa and Staphylococcus aureus Biofilms in the Cystic Fibrosis Lung. Journal of Visualized Experiments. 14 indexed citations

5.

Thummeepak, Rapee, Udomluk Leungtongkam, Andrew Millard, et al.. (2020). Investigating Bacteriophages Targeting the Opportunistic Pathogen Acinetobacter baumannii. Antibiotics. 9(4). 200–200. 32 indexed citations

6.

Anonye, Blessing O., Ricky Cain, John Moat, et al.. (2020). Anti-biofilm efficacy of a medieval treatment for bacterial infection requires the combination of multiple ingredients. Scientific Reports. 10(1). 12687–12687. 23 indexed citations

7.

Kirk, Ralph, Matilda Bingham, Paul Doyle, et al.. (2020). Novel C-7 carbon substituted fourth generation fluoroquinolones targeting N. Gonorrhoeae infections. Bioorganic & Medicinal Chemistry Letters. 30(20). 127428–127428. 3 indexed citations

8.

Townsend, Eleanor R., John Moat, & Eleanor Jameson. (2020). CAUTI’s next top model – Model dependent Klebsiella biofilm inhibition by bacteriophages and antimicrobials. Biofilm. 2. 100038–100038. 36 indexed citations

9.

Hapeshi, Alexia, Nicola J. Rogers, Viktor Brabec, et al.. (2019). Metallohelices that kill Gram-negative pathogens using intracellular antimicrobial peptide pathways. Chemical Science. 10(42). 9708–9720. 30 indexed citations

10.

Chen, Feng, John Moat, Guy J. Clarkson, et al.. (2018). Biguanide Iridium(III) Complexes with Potent Antimicrobial Activity. Journal of Medicinal Chemistry. 61(16). 7330–7344. 86 indexed citations

11.

Castelino, Madhura, Stephen Eyre, John Moat, et al.. (2017). Optimisation of methods for bacterial skin microbiome investigation: primer selection and comparison of the 454 versus MiSeq platform. BMC Microbiology. 17(1). 23–23. 115 indexed citations

12.

Kuroki, Agnès, Parveen Sangwan, Yue Qu, et al.. (2017). Sequence Control as a Powerful Tool for Improving the Selectivity of Antimicrobial Polymers. ACS Applied Materials & Interfaces. 9(46). 40117–40126. 101 indexed citations

13.

Castelino, Madhura, John Moat, Richard Parslew, et al.. (2017). AB0115 Comparison of the bacterial stool microbiota in established psoriatic arthritis (PSA) and psoriasis (PSC) - exploratory analysis of pilot data. Annals of the Rheumatic Diseases. 76. 1087–1087. 3 indexed citations

14.

Moat, John, Athanasios Rizoulis, Graeme Fox, & Mathew Upton. (2016). Domestic shower hose biofilms contain fungal species capable of causing opportunistic infection. Journal of Water and Health. 14(5). 727–737. 22 indexed citations

15.

Castelino, Madhura, Stephen Eyre, John Moat, et al.. (2015). Bacterial Skin Microbiome in Psoriatic Arthritis: Pilot Data from Psoriatic Plaques on Dry Skin Sites from Patients with Psoriasis (PsC) and Psoriatic Arthritis (PsA). ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1 indexed citations

16.

Castelino, Madhura, Stephen Eyre, John Moat, et al.. (2015). The skin microbiome in psoriatic arthritis: methodology development and pilot data. The Lancet. 385. S27–S27. 7 indexed citations

17.

Moat, John, et al.. (2009). Application of a novel decontamination process using gaseous ozone. Canadian Journal of Microbiology. 55(8). 928–933. 35 indexed citations

18.

Moat, John. (1985). The experience of Arvon. Critical Quarterly. 27(1). 63–70.

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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