Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Hyperproinsulinaemia in obese fat/fat mice associated with a carboxypeptidase E mutation which reduces enzyme activity
1995523 citationsJürgen Κ. Naggert, Patsy M. Nishina et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Patsy M. Nishina
Since
Specialization
Citations
This map shows the geographic impact of Patsy M. Nishina'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 Patsy M. Nishina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patsy M. Nishina more than expected).
Fields of papers citing papers by Patsy M. Nishina
This network shows the impact of papers produced by Patsy M. Nishina. 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 Patsy M. Nishina. The network helps show where Patsy M. Nishina may publish in the future.
Co-authorship network of co-authors of Patsy M. Nishina
This figure shows the co-authorship network connecting the top 25 collaborators of Patsy M. Nishina.
A scholar is included among the top collaborators of Patsy M. Nishina 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 Patsy M. Nishina. Patsy M. Nishina is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chang, Bo, et al.. (2017). A new mouse model of early-onset glaucoma.. Investigative Ophthalmology & Visual Science. 58(8). 2125–2125.1 indexed citations
4.
Krebs, Mark P., Wanda L. Hicks, & Patsy M. Nishina. (2016). Mfrp regulates ocular growth in mice and interacts with Prss56. Investigative Ophthalmology & Visual Science. 57(12). 3609–3609.1 indexed citations
5.
Chang, Bo, R.E. Hurd, Jieping Wang, & Patsy M. Nishina. (2013). Survey of Common Genetic Eye Diseases in Mouse Strains. Investigative Ophthalmology & Visual Science. 54(15). 265–265.1 indexed citations
6.
Ray, Thomas A., Nazarul Hasan, Maureen A. McCall, et al.. (2012). GPR179, An Orphan G Protein-Coupled Receptor, Is Critical To Depolarizing Cell Function And Interacts With GRM6. Investigative Ophthalmology & Visual Science. 53(14). 3156–3156.1 indexed citations
Peachey, Neal S., Maureen A. McCall, Jillian N. Pearring, et al.. (2011). Trpm1 Point Mutation Underlies Retinal Dysfunction In The Mtvr27 Mouse Model Of Complete Congenital Stationary Night Blindness. Investigative Ophthalmology & Visual Science. 52(14). 4124–4124.1 indexed citations
9.
Edwards, Malia M., Takayuki Baba, D. Scott McLeod, et al.. (2010). Mutations in Lama1 Disrupt Ganglion Cell Development and Axonal Migration in the Mouse. Investigative Ophthalmology & Visual Science. 51(13). 5945–5945.1 indexed citations
Mehalow, Adrienne K., et al.. (2006). Prefoldin 5 (Pfdn5) Is Essential for the Development of the Mouse Photoreceptor Outer Segment (Os). Investigative Ophthalmology & Visual Science. 47(13). 2296–2296.1 indexed citations
12.
Naggert, Jürgen Κ., et al.. (2005). The Transcription Factor NR2E3 Suppresses Cone Cell Proliferation. Investigative Ophthalmology & Visual Science. 46(13). 3982–3982.2 indexed citations
Naggert, Jürgen Κ., et al.. (2004). Elucidating the Function of NR2E3 through Identification of Interacting Factors. Investigative Ophthalmology & Visual Science. 45(13). 5317–5317.1 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.