Chunla He

1.1k total citations · 1 hit paper
9 papers, 784 citations indexed

About

Chunla He is a scholar working on Biomaterials, Computational Mechanics and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Chunla He has authored 9 papers receiving a total of 784 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Biomaterials, 3 papers in Computational Mechanics and 2 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Chunla He's work include Nanoparticle-Based Drug Delivery (4 papers), Field-Flow Fractionation Techniques (3 papers) and Graphene and Nanomaterials Applications (2 papers). Chunla He is often cited by papers focused on Nanoparticle-Based Drug Delivery (4 papers), Field-Flow Fractionation Techniques (3 papers) and Graphene and Nanomaterials Applications (2 papers). Chunla He collaborates with scholars based in United States. Chunla He's co-authors include Zhoumeng Lin, Jim E. Riviere, Nancy A. Monteiro‐Riviere, Sara Wagner Robb, Yi‐Hsien Cheng, Amara E. Ezeamama, John E. Vena, Mark H. Ebell, Qiran Chen and Wei‐Chun Chou and has published in prestigious journals such as ACS Nano, Journal of Controlled Release and Nutrients.

In The Last Decade

Chunla He

9 papers receiving 768 citations

Hit Papers

Meta-Analysis of Nanoparticle Delivery to Tumors Using a ... 2020 2026 2022 2024 2020 50 100 150 200

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chunla He United States 8 214 203 137 133 111 9 784
Yinghua Lv China 25 177 0.8× 257 1.3× 459 3.4× 439 3.3× 163 1.5× 69 1.5k
Daniel Winnica United States 15 50 0.2× 62 0.3× 409 3.0× 43 0.3× 75 0.7× 21 1.0k
Hee‐Gyoo Kang South Korea 18 33 0.2× 200 1.0× 366 2.7× 25 0.2× 148 1.3× 70 936
Ling Long China 14 83 0.4× 96 0.5× 152 1.1× 161 1.2× 39 0.4× 48 709
Marı́a P. Carrasco Spain 20 94 0.4× 103 0.5× 532 3.9× 17 0.1× 46 0.4× 65 1.1k
Zheng Fang China 13 50 0.2× 95 0.5× 217 1.6× 49 0.4× 135 1.2× 57 825
Ahmed Reda Egypt 13 98 0.5× 97 0.5× 402 2.9× 244 1.8× 30 0.3× 37 908
Alison Montpetit United States 12 28 0.1× 134 0.7× 246 1.8× 7 0.1× 25 0.2× 18 879
Lianzhi Cui China 10 73 0.3× 119 0.6× 205 1.5× 8 0.1× 32 0.3× 13 561
Junko Matsumoto Japan 17 86 0.4× 26 0.1× 232 1.7× 15 0.1× 26 0.2× 66 807

Countries citing papers authored by Chunla He

Since Specialization
Citations

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

Fields of papers citing papers by Chunla He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chunla He

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

All Works

9 of 9 papers shown
1.
Mi, Kun, Wei‐Chun Chou, Qiran Chen, et al.. (2024). Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models. Journal of Controlled Release. 374. 219–229. 35 indexed citations
2.
Chou, Wei‐Chun, Qiran Chen, Long Yuan, et al.. (2023). An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice. Journal of Controlled Release. 361. 53–63. 98 indexed citations
3.
Chen, Qiran, Long Yuan, Wei‐Chun Chou, et al.. (2023). Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice. ACS Nano. 17(20). 19810–19831. 46 indexed citations
4.
Cheng, Yi‐Hsien, Chunla He, Jim E. Riviere, Nancy A. Monteiro‐Riviere, & Zhoumeng Lin. (2020). Meta-Analysis of Nanoparticle Delivery to Tumors Using a Physiologically Based Pharmacokinetic Modeling and Simulation Approach. ACS Nano. 14(3). 3075–3095. 230 indexed citations breakdown →
5.
Lin, Zhoumeng, Chunla He, Drew R. Magstadt, et al.. (2019). Tissue residue depletion and estimation of extralabel meat withdrawal intervals for tulathromycin in calves after pneumatic dart administration. Journal of Animal Science. 97(9). 3714–3726. 3 indexed citations
6.
7.
He, Chunla, Zhoumeng Lin, Sara Wagner Robb, & Amara E. Ezeamama. (2015). Serum Vitamin D Levels and Polycystic Ovary syndrome: A Systematic Review and Meta-Analysis. Nutrients. 7(6). 4555–4577. 165 indexed citations
8.
He, Chunla, et al.. (2014). Circadian disrupting exposures and breast cancer risk: a meta-analysis. International Archives of Occupational and Environmental Health. 88(5). 533–547. 124 indexed citations
9.
Lin, Zhoumeng, James R. Roede, Chunla He, Dean P. Jones, & Nikolay M. Filipov. (2014). Short-term oral atrazine exposure alters the plasma metabolome of male C57BL/6 mice and disrupts α-linolenate, tryptophan, tyrosine and other major metabolic pathways. Toxicology. 326. 130–141. 30 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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