Michael J. Fay

1.0k total citations
36 papers, 682 citations indexed

About

Michael J. Fay is a scholar working on Molecular Biology, Social Psychology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Michael J. Fay has authored 36 papers receiving a total of 682 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 12 papers in Social Psychology and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Michael J. Fay's work include Neuroendocrine regulation and behavior (10 papers), Receptor Mechanisms and Signaling (5 papers) and Ion Transport and Channel Regulation (4 papers). Michael J. Fay is often cited by papers focused on Neuroendocrine regulation and behavior (10 papers), Receptor Mechanisms and Signaling (5 papers) and Ion Transport and Channel Regulation (4 papers). Michael J. Fay collaborates with scholars based in United States, United Kingdom and France. Michael J. Fay's co-authors include William G. North, Jinlin Du, Kenneth Longo, Anthony J. Verlangieri, Peter C. Lamar, Walter C. Prozialeck, C.T. De Rosa, Andrew S. Friedmann, Xiaoming Yu and Ira M. Sigar and has published in prestigious journals such as Environmental Health Perspectives, Chemosphere and Infection and Immunity.

In The Last Decade

Michael J. Fay

36 papers receiving 661 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael J. Fay United States 16 256 150 118 101 86 36 682
Yow‐Jiun Jeng United States 13 182 0.7× 61 0.4× 37 0.3× 49 0.5× 125 1.5× 20 570
B. Bréant France 20 341 1.3× 63 0.4× 100 0.8× 42 0.4× 33 0.4× 27 1.8k
Gary J. Chellman United States 17 126 0.5× 44 0.3× 31 0.3× 139 1.4× 67 0.8× 31 746
Kaushik Maiti Australia 20 343 1.3× 35 0.2× 66 0.6× 63 0.6× 53 0.6× 33 1.2k
Takao Mori Japan 20 254 1.0× 33 0.2× 46 0.4× 306 3.0× 53 0.6× 79 1.4k
H. G. Bohnet Germany 19 307 1.2× 41 0.3× 35 0.3× 60 0.6× 61 0.7× 58 1.4k
Dana Shuey United States 15 299 1.2× 105 0.7× 20 0.2× 202 2.0× 178 2.1× 30 990
Sang‐Young Chun South Korea 22 759 3.0× 61 0.4× 41 0.3× 50 0.5× 209 2.4× 47 2.0k
Dinesh Stanislaus United States 20 592 2.3× 38 0.3× 27 0.2× 67 0.7× 100 1.2× 41 1.2k
Manabu Matsuda Japan 18 284 1.1× 23 0.2× 31 0.3× 84 0.8× 49 0.6× 43 906

Countries citing papers authored by Michael J. Fay

Since Specialization
Citations

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

Fields of papers citing papers by Michael J. Fay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael J. Fay. 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 Michael J. Fay. The network helps show where Michael J. Fay may publish in the future.

Co-authorship network of co-authors of Michael J. Fay

This figure shows the co-authorship network connecting the top 25 collaborators of Michael J. Fay. A scholar is included among the top collaborators of Michael J. Fay 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 Michael J. Fay. Michael J. Fay 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.
Chandar, Nalini, et al.. (2017). Preclinical evaluation of the Aurora kinase inhibitors AMG 900, AZD1152-HQPA, and MK-5108 on SW-872 and 93T449 human liposarcoma cells. In Vitro Cellular & Developmental Biology - Animal. 54(1). 71–84. 6 indexed citations
2.
3.
Mayer, Alejandro M. S., et al.. (2009). Granulocytic differentiation of HL-60 promyelocytic leukemia cells is associated with increased expression of Cul5. In Vitro Cellular & Developmental Biology - Animal. 45(5-6). 264–274. 18 indexed citations
4.
Fay, Michael J.. (2005). Exposure To Contaminant Mixtures At USHazardous Waste Sites. WIT Transactions on Ecology and the Environment. 85. 2 indexed citations
5.
North, William G., et al.. (2003). Immunohistochemical evaluation of vasopressin expresion in breast fibrocystic disease and ductal carcinoma In situ (DCIS). Endocrine Pathology. 14(3). 257–262. 5 indexed citations
6.
Prozialeck, Walter C., et al.. (2002). Chlamydia trachomatis Disrupts N-Cadherin-Dependent Cell-Cell Junctions and Sequesters β-Catenin in Human Cervical Epithelial Cells. Infection and Immunity. 70(5). 2605–2613. 48 indexed citations
7.
Fay, Michael J., Nils G. Walter, & John M. Burke. (2001). Imaging of single hairpin ribozymes in solution by atomic force microscopy. RNA. 7(6). 887–895. 6 indexed citations
8.
9.
Pohl, Hana R., Peter R McClure, Michael J. Fay, James S. Holler, & C.T. De Rosa. (2001). Public health assessment of hexachlorobenzene. Chemosphere. 43(4-7). 903–908. 10 indexed citations
10.
North, William G., Michael J. Fay, & Jinlin Du. (1999). MCF-7 breast cancer cells express normal forms of all vasopressin receptors plus an abnormal V2R☆. Peptides. 20(7). 837–842. 43 indexed citations
11.
Pohl, Hana R., Nickolette Roney, Michael J. Fay, et al.. (1999). Site-specific consultation for a chemical mixture. Toxicology and Industrial Health. 15(5). 470–479. 5 indexed citations
12.
Rosa, C.T. De, et al.. (1998). Public health challenges posed by chemical mixtures.. Environmental Health Perspectives. 106(suppl 6). 1271–1280. 25 indexed citations
13.
North, William G., Michael J. Fay, Kenneth Longo, & Jinlin Du. (1997). Functional Vasopressin V1 Type Receptors are Present in Variant as Well as Classical Forms of Small-Cell Carcinoma. Peptides. 18(7). 985–993. 13 indexed citations
14.
Rosa, C.T. De, et al.. (1996). Public health implications of hazardous waste sites: Findings, assessment and research. Food and Chemical Toxicology. 34(11-12). 1131–1138. 37 indexed citations
15.
North, William G., Sara I. Pai, Andrew S. Friedmann, et al.. (1995). Vasopressin gene related products are markers of human breast cancer. Breast Cancer Research and Treatment. 34(3). 229–235. 31 indexed citations
16.
Fay, Michael J., Marilyn J. Bush, & Anthony J. Verlangieri. (1994). Effect of aldonic acids on the uptake of ascorbic acid by 3T3 mouse fibroblasts and human T lymphoma cells. General Pharmacology The Vascular System. 25(7). 1465–1469. 8 indexed citations
17.
Fay, Michael J., Andrew S. Friedmann, Xiaoming Yu, & William G. North. (1994). Vasopressin and vasopressin-receptor immunoreactivity in small-cell lung carcinoma (SCCL) cell lines: disruption in the activation cascade of V1a-receptors in variant SCCL. Cancer Letters. 82(2). 167–174. 13 indexed citations
18.
Verlangieri, Anthony J., Michael J. Fay, & Anthony W. Bannon. (1991). Comparison of the anti-scorbutic activity of L-ascorbic acid and ester C® in the non-ascorbate synthesizing Osteogenic Disorder Shionogi (ODS) rat. Life Sciences. 48(23). 2275–2281. 14 indexed citations
19.
Fay, Michael J. & Anthony J. Verlangieri. (1991). Stimulatory action of calcium L-threonate on ascorbic acid uptake by a human T-lymphoma cell line. Life Sciences. 49(19). 1377–1381. 17 indexed citations
20.
Fay, Michael J., Marilyn J. Bush, & Anthony J. Verlangieri. (1990). Effects of cytochalasin B on the uptake of ascorbic acid and glucose by 3T3 fibroblasts: Mechanism of impaired ascorbate transport in diabetes. Life Sciences. 46(9). 619–624. 16 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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