Nicholas Macedo

425 total citations · 1 hit paper
6 papers, 282 citations indexed

About

Nicholas Macedo is a scholar working on Cellular and Molecular Neuroscience, Oncology and Artificial Intelligence. According to data from OpenAlex, Nicholas Macedo has authored 6 papers receiving a total of 282 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Cellular and Molecular Neuroscience, 2 papers in Oncology and 2 papers in Artificial Intelligence. Recurrent topics in Nicholas Macedo's work include Cerebrospinal fluid and hydrocephalus (2 papers), Machine Learning in Healthcare (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Nicholas Macedo is often cited by papers focused on Cerebrospinal fluid and hydrocephalus (2 papers), Machine Learning in Healthcare (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Nicholas Macedo collaborates with scholars based in United States. Nicholas Macedo's co-authors include Rizwan Haq, David M. Miller, Howard L. Kaufman, Raag D. Airan, Quan Zhou, Eben L. Rosenthal, Muna Aryal, Susan Lee, Jeffrey B. Wang and Jonathan H. Chen and has published in prestigious journals such as Nature Biotechnology, Nature Nanotechnology and Journal of Controlled Release.

In The Last Decade

Nicholas Macedo

5 papers receiving 279 citations

Hit Papers

align trajectories

What are hit papers?

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.

Clinical landscape of oncolytic virus research in 2020

2020 242 citations Nicholas Macedo, David M. Miller et al. Journal for ImmunoTherapy of Cancer profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nicholas Macedo United States	3	195	167	106	54	44	6	282
Victoria A. Jennings United Kingdom	8	241 1.2×	193 1.2×	136 1.3×	126 2.3×	40 0.9×	16	379
Shohei Miyamoto Japan	10	257 1.3×	191 1.1×	163 1.5×	100 1.9×	48 1.1×	16	392
Shinnosuke Kurachi Japan	11	341 1.7×	132 0.8×	345 3.3×	51 0.9×	45 1.0×	16	459
Gray Kueberuwa United Kingdom	10	138 0.7×	305 1.8×	133 1.3×	153 2.8×	17 0.4×	22	397
Ruth Morgan United Kingdom	6	166 0.9×	193 1.2×	88 0.8×	167 3.1×	39 0.9×	6	349
Aizhan Tastanova Switzerland	8	75 0.4×	91 0.5×	106 1.0×	48 0.9×	18 0.4×	15	248
Marcela P. Cataldi United States	9	221 1.1×	71 0.4×	206 1.9×	42 0.8×	26 0.6×	12	406
Anja Geisler Germany	10	195 1.0×	49 0.3×	255 2.4×	32 0.6×	13 0.3×	15	364
Matthias Hebben France	9	296 1.5×	89 0.5×	308 2.9×	21 0.4×	12 0.3×	11	406
Immacolata Zampaglione Italy	8	136 0.7×	37 0.2×	181 1.7×	67 1.2×	111 2.5×	11	315

Countries citing papers authored by Nicholas Macedo

Since Specialization

Citations

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

Fields of papers citing papers by Nicholas Macedo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nicholas Macedo

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

All Works

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6 of 6 papers shown

1.

Shabestari, Sepideh Kiani, et al.. (2025). Clearance of intracranial debris by ultrasound reduces inflammation and improves outcomes in hemorrhagic stroke models. Nature Biotechnology.

2.

Purohit, Mahaveer P., et al.. (2025). Acoustically activatable liposomes as a translational nanotechnology for site-targeted drug delivery and noninvasive neuromodulation. Nature Nanotechnology. 20(11). 1688–1699. 2 indexed citations

3.

Swaminathan, Akshay, Susan Lee, Nicholas Macedo, et al.. (2024). Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns. JCO Clinical Cancer Informatics. 8(8). e2300091–e2300091. 1 indexed citations

4.

Swaminathan, Akshay, William Yang Wang, Susan Lee, et al.. (2023). Selective prediction for extracting unstructured clinical data. Journal of the American Medical Informatics Association. 31(1). 188–197. 7 indexed citations

5.

Aryal, Muna, et al.. (2022). Noninvasive ultrasonic induction of cerebrospinal fluid flow enhances intrathecal drug delivery. Journal of Controlled Release. 349. 434–442. 30 indexed citations

6.

Macedo, Nicholas, David M. Miller, Rizwan Haq, & Howard L. Kaufman. (2020). Clinical landscape of oncolytic virus research in 2020. Journal for ImmunoTherapy of Cancer. 8(2). e001486–e001486. 242 indexed citations breakdown →

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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