Matthew C. Havrda

1.6k total citations
30 papers, 1.2k citations indexed

About

Matthew C. Havrda is a scholar working on Molecular Biology, Cancer Research and Neurology. According to data from OpenAlex, Matthew C. Havrda has authored 30 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 10 papers in Cancer Research and 6 papers in Neurology. Recurrent topics in Matthew C. Havrda's work include Glioma Diagnosis and Treatment (5 papers), Inflammasome and immune disorders (5 papers) and Parkinson's Disease Mechanisms and Treatments (4 papers). Matthew C. Havrda is often cited by papers focused on Glioma Diagnosis and Treatment (5 papers), Inflammasome and immune disorders (5 papers) and Parkinson's Disease Mechanisms and Treatments (4 papers). Matthew C. Havrda collaborates with scholars based in United States, Switzerland and Lebanon. Matthew C. Havrda's co-authors include Lucy Liaw, Howard C. Crawford, Lynn M. Matrisian, Hirotaka Haro, Mark A. Israel, Katharine M. von Herrmann, Alison L. Young, Stephen Lee, Brenton R. Paolella and William F. Hickey and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Neuroscience and Cancer Research.

In The Last Decade

Matthew C. Havrda

28 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew C. Havrda United States 16 712 225 181 178 161 30 1.2k
Mineyoshi Aoyama Japan 22 676 0.9× 194 0.9× 87 0.5× 274 1.5× 172 1.1× 74 1.4k
Qiji Liu China 22 899 1.3× 228 1.0× 119 0.7× 131 0.7× 90 0.6× 87 1.6k
Weiping Su China 19 398 0.6× 127 0.6× 150 0.8× 134 0.8× 67 0.4× 32 975
Gui‐Xian Zhao China 18 595 0.8× 417 1.9× 173 1.0× 151 0.8× 111 0.7× 35 1.5k
Alessandro Campanella Italy 24 807 1.1× 157 0.7× 125 0.7× 95 0.5× 281 1.7× 31 1.9k
Giovanni Marfia Italy 27 655 0.9× 229 1.0× 74 0.4× 232 1.3× 136 0.8× 74 1.9k
Ke Yan China 25 540 0.8× 206 0.9× 143 0.8× 136 0.8× 153 1.0× 52 1.6k
Pengyu Tang China 17 889 1.2× 304 1.4× 85 0.5× 66 0.4× 151 0.9× 33 1.4k
Jeff Stevens United States 21 508 0.7× 111 0.5× 227 1.3× 71 0.4× 86 0.5× 45 1.2k
Urs H. Langen United States 6 586 0.8× 135 0.6× 45 0.2× 169 0.9× 235 1.5× 6 1.3k

Countries citing papers authored by Matthew C. Havrda

Since Specialization
Citations

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

Fields of papers citing papers by Matthew C. Havrda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew C. Havrda. 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 Matthew C. Havrda. The network helps show where Matthew C. Havrda may publish in the future.

Co-authorship network of co-authors of Matthew C. Havrda

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew C. Havrda. A scholar is included among the top collaborators of Matthew C. Havrda 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 Matthew C. Havrda. Matthew C. Havrda 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.
Kettenbach, Arminja N., et al.. (2025). Coronin1A Regulates the Trafficking of Alpha Synuclein in Microglia. Journal of Neuroscience. 45(11). e1337242025–e1337242025.
2.
Havrda, Matthew C., et al.. (2024). Immunological shifts during early-stage Parkinson’s disease identified with DNA methylation data on longitudinally collected blood samples. npj Parkinson s Disease. 10(1). 21–21. 10 indexed citations
3.
Havrda, Matthew C., et al.. (2024). Inhibiting the Cholesterol Storage Enzyme ACAT1/SOAT1 in Aging Apolipoprotein E4 Mice Alters Their Brains’ Inflammatory Profiles. International Journal of Molecular Sciences. 25(24). 13690–13690. 3 indexed citations
4.
5.
Havrda, Matthew C., et al.. (2022). NLRP3 inflammasome in neurodegenerative disease. Translational research. 252. 21–33. 77 indexed citations
6.
Gulledge, Allan T., et al.. (2022). Abstract 905: Targeting muscarinic acetylcholine receptors in glioma stem like cells. Cancer Research. 82(12_Supplement). 905–905.
7.
Herrmann, Katharine M. von, Angeline S. Andrew, Yuliya I. Kuras, et al.. (2021). Plasma-borne indicators of inflammasome activity in Parkinson’s disease patients. npj Parkinson s Disease. 7(1). 2–2. 56 indexed citations
8.
Andrew, Angeline S., et al.. (2021). Lifestyle Factors and Parkinson’s Disease Risk in a Rural New England Case-Control Study. Parkinson s Disease. 2021. 1–7. 3 indexed citations
9.
10.
Young, Alison L., et al.. (2020). The effect of botulinum toxin on ureteral inflammation. World Journal of Urology. 39(6). 2197–2204. 3 indexed citations
11.
Herrmann, Katharine M. von, et al.. (2020). Slc6a3-dependent expression of a CAPS-associated Nlrp3 allele results in progressive behavioral abnormalities and neuroinflammation in aging mice. Journal of Neuroinflammation. 17(1). 213–213. 18 indexed citations
12.
Ran, Cong, Matthew C. Havrda, Huan Liu, et al.. (2017). Insulin-Mediated Signaling Facilitates Resistance to PDGFR Inhibition in Proneural hPDGFB-Driven Gliomas. Molecular Cancer Therapeutics. 16(4). 705–716. 19 indexed citations
13.
Zhang, Zhonghua, et al.. (2017). ID2 promotes survival of glioblastoma cells during metabolic stress by regulating mitochondrial function. Cell Death and Disease. 8(2). e2615–e2615. 27 indexed citations
14.
Havrda, Matthew C., Myung Chang Lee, Lananh Nguyen, et al.. (2017). Secretion-mediated STAT3 activation promotes self-renewal of glioma stem-like cells during hypoxia. Oncogene. 37(8). 1107–1118. 72 indexed citations
15.
Young, Alison L., Yash R. Patankar, Brent Berwin, et al.. (2017). Editor’s Highlight: Nlrp3 Is Required for Inflammatory Changes and Nigral Cell Loss Resulting From Chronic Intragastric Rotenone Exposure in Mice. Toxicological Sciences. 159(1). 64–75. 54 indexed citations
16.
Havrda, Matthew C., Brenton R. Paolella, Cong Ran, et al.. (2014). Id2 Mediates Oligodendrocyte Precursor Cell Maturation Arrest and Is Tumorigenic in a PDGF-Rich Microenvironment. Cancer Research. 74(6). 1822–1832. 27 indexed citations
17.
Havrda, Matthew C., et al.. (2012). Behavioral abnormalities and Parkinson's-like changes resulting from Id2 inactivation in mice. Disease Models & Mechanisms. 6(3). 819–27. 9 indexed citations
18.
Havrda, Matthew C., Brent T. Harris, Akio Mantani, et al.. (2008). Id2 Is Required for Specification of Dopaminergic Neurons during Adult Olfactory Neurogenesis. Journal of Neuroscience. 28(52). 14074–14087. 46 indexed citations
19.
Havrda, Matthew C., Michael Johnson, Christine F. O’Neill, & Lucy Liaw. (2006). A novel mechanism of transcriptional repression of p27kip1 through Notch/HRT2 signaling in vascular smooth muscle cells. Thrombosis and Haemostasis. 96(9). 361–370. 41 indexed citations
20.
Havrda, Matthew C., et al.. (2006). Variable Recombination Efficiency in Responder Transgenes Activated by Cre Recombinase in the Vasculature. Transgenic Research. 15(1). 101–106. 7 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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