Countries citing papers authored by Jayashree Sahni
Since
Specialization
Citations
This map shows the geographic impact of Jayashree Sahni'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 Jayashree Sahni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jayashree Sahni more than expected).
This network shows the impact of papers produced by Jayashree Sahni. 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 Jayashree Sahni. The network helps show where Jayashree Sahni may publish in the future.
Co-authorship network of co-authors of Jayashree Sahni
This figure shows the co-authorship network connecting the top 25 collaborators of Jayashree Sahni.
A scholar is included among the top collaborators of Jayashree Sahni 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 Jayashree Sahni. Jayashree Sahni is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Boyer, David S., Jayashree Sahni, Sascha Fauser, et al.. (2021). Systemic Antisense Oligonucleotide Inhibition of Complement Factor B for Treatment of Geographic Atrophy: Results of a Placebo-controlled Phase 1 Dose-Escalation Study. Investigative Ophthalmology & Visual Science. 62(8). 196–196.1 indexed citations
4.
Maunz, Andreas, Fethallah Benmansour, Yun Li, et al.. (2020). Diagnostic accuracy of a machine-learning algorithm to detect and classify choroidal neovascularization based on SD-OCT in neovascular age-related macular degeneration (nAMD). Investigative Ophthalmology & Visual Science. 61(7). 2649–2649.
5.
Jones, Ian L., Andreas Maunz, Thomas Albrecht, et al.. (2020). Development and External Validation of a Machine Learning Model for Predicting Response to anti-VEGF Treatment in Patients with neovascular AMD. Investigative Ophthalmology & Visual Science. 61(9).1 indexed citations
6.
Jaffe, Glenn J., Jayashree Sahni, Sascha Fauser, et al.. (2020). Development of IONIS-FB-LRx to Treat Geographic Atrophy Associated with AMD. Investigative Ophthalmology & Visual Science. 61(7). 4305–4305.23 indexed citations
7.
Chakravarthy, Usha, Richard Foxton, Sabine Uhles, et al.. (2020). Sustained Vessel Stabilization Through Targeting Angiopoietin-2 (Ang-2) With Faricimab, a Bispecific Antibody Neutralizing Both Ang-2 and Vascular Endothelial Growth Factor A (VEGF-A). Investigative Ophthalmology & Visual Science. 61(7). 5126–5126.1 indexed citations
8.
Sahni, Jayashree, et al.. (2019). A machine learning approach to predict response to anti-VEGF treatment in patients with neovascular age-related macular degeneration using SD-OCT. Investigative Ophthalmology & Visual Science. 60(11).1 indexed citations
Chakravarthy, Usha, Dietmar Schwab, Patrick G. Cech, et al.. (2016). The novel bispecific monoclonal anti-VEGF/anti-Ang2 antibody RG7716 shows promise in wet age-related macular degeneration patients with suboptimal response to prior anti-VEGF monotherapy. Investigative Ophthalmology & Visual Science. 57(12).1 indexed citations
13.
Czanner, Gabriela, Simon Harding, M Holland, et al.. (2015). Safety and acceptability of an organic light emitting diode (OLED) sleep mask for the treatment of retinal diseases: INSIGHT Study. Investigative Ophthalmology & Visual Science. 56(7). 3161–3161.
14.
Zheng, Yalin, et al.. (2015). Impact of hypertension on choroidal thickness in central serous chorioretinopathy. Investigative Ophthalmology & Visual Science. 56(7). 3717–3717.1 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.