Guiquan Jia

3.9k total citations · 2 hit papers
16 papers, 2.3k citations indexed

About

Guiquan Jia is a scholar working on Pulmonary and Respiratory Medicine, Physiology and Molecular Biology. According to data from OpenAlex, Guiquan Jia has authored 16 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Pulmonary and Respiratory Medicine, 8 papers in Physiology and 5 papers in Molecular Biology. Recurrent topics in Guiquan Jia's work include Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (7 papers), Asthma and respiratory diseases (5 papers) and Systemic Sclerosis and Related Diseases (3 papers). Guiquan Jia is often cited by papers focused on Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (7 papers), Asthma and respiratory diseases (5 papers) and Systemic Sclerosis and Related Diseases (3 papers). Guiquan Jia collaborates with scholars based in United States, United Kingdom and Australia. Guiquan Jia's co-authors include Joseph R. Arron, David F. Choy, Prescott G. Woodruff, John V. Fahy, Alexander R. Abbas, Barmak Modrek, Laura L. Koth, Peter Bradding, Aarti Shikotra and Lawren C. Wu and has published in prestigious journals such as The Journal of Immunology, American Journal of Respiratory and Critical Care Medicine and Journal of Allergy and Clinical Immunology.

In The Last Decade

Guiquan Jia

16 papers receiving 2.3k citations

Hit Papers

T-helper Type 2–driven In... 2009 2026 2014 2020 2009 2012 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guiquan Jia United States 11 1.7k 1.4k 754 321 311 16 2.3k
Sofia Mosesova United States 10 2.2k 1.3× 1.5k 1.1× 929 1.2× 416 1.3× 589 1.9× 16 2.6k
Merdad V. Parsey United States 11 1.2k 0.7× 841 0.6× 724 1.0× 237 0.7× 287 0.9× 16 1.8k
Simone A. Walker United Kingdom 23 883 0.5× 542 0.4× 1.1k 1.4× 480 1.5× 212 0.7× 36 1.9k
James Zangrilli United States 25 2.7k 1.6× 1.9k 1.4× 991 1.3× 601 1.9× 733 2.4× 62 3.5k
Erica Bazzan Italy 20 899 0.5× 1.1k 0.8× 493 0.7× 110 0.3× 121 0.4× 66 2.0k
Peter Rosenthal United States 23 715 0.4× 342 0.2× 994 1.3× 496 1.5× 128 0.4× 37 2.0k
Yves Pachéco France 19 565 0.3× 726 0.5× 325 0.4× 96 0.3× 86 0.3× 50 1.5k
Kate G. Ackerman United States 21 415 0.2× 744 0.5× 501 0.7× 847 2.6× 78 0.3× 34 1.9k
Susanne M. Schmidt Germany 18 363 0.2× 472 0.3× 755 1.0× 127 0.4× 48 0.2× 30 1.9k
Zsuzsanna Szalai Hungary 19 454 0.3× 595 0.4× 258 0.3× 194 0.6× 945 3.0× 51 2.5k

Countries citing papers authored by Guiquan Jia

Since Specialization
Citations

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

Fields of papers citing papers by Guiquan Jia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guiquan Jia

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

All Works

16 of 16 papers shown
1.
Diarra, Moussa S., et al.. (2024). Detection of plasmids in Salmonella from poultry and investigating the potential horizontal transfer of antimicrobial resistance and virulence genes. Poultry Science. 104(1). 104591–104591. 1 indexed citations
2.
Heiden, Jason A. Vander, Jianping Yin, Wei‐Ching Liang, et al.. (2024). Isoform-selective TGF-β3 inhibition for systemic sclerosis. Med. 5(2). 132–147.e7. 15 indexed citations
3.
Chang, Sarah E., Guiquan Jia, Courtney Schiffman, et al.. (2024). Pursuing Clinical Predictors and Biomarkers for Progression in ILD: Analysis of the Pulmonary Fibrosis Foundation (PFF) Registry. Lung. 202(3). 269–273. 1 indexed citations
4.
Neighbors, Margaret, Qingling Li, Sha Zhu, et al.. (2023). Bioactive lipid lysophosphatidic acid species are associated with disease progression in idiopathic pulmonary fibrosis. Journal of Lipid Research. 64(6). 100375–100375. 8 indexed citations
5.
Jia, Guiquan, Thirumalai R. Ramalingam, Jason A. Vander Heiden, et al.. (2023). An interleukin 6 responsive plasma cell signature is associated with disease progression in systemic sclerosis interstitial lung disease. iScience. 26(11). 108133–108133. 11 indexed citations
6.
Jia, Guiquan, Daryle J. DePianto, Katrina B. Morshead, et al.. (2020). Osteopontin Links Myeloid Activation and Disease Progression in Systemic Sclerosis. Cell Reports Medicine. 1(8). 100140–100140. 72 indexed citations
7.
Huang, Zhiyu, Hua Zhang, Clara Posner, et al.. (2019). TAZ is required for lung alveolar epithelial cell differentiation after injury. JCI Insight. 4(14). 67 indexed citations
8.
Neighbors, Margaret, Christopher R. Cabanski, Thirumalai R. Ramalingam, et al.. (2018). Prognostic and predictive biomarkers for patients with idiopathic pulmonary fibrosis treated with pirfenidone: post-hoc assessment of the CAPACITY and ASCEND trials. The Lancet Respiratory Medicine. 6(8). 615–626. 77 indexed citations
9.
Shikotra, Aarti, David F. Choy, Salman Siddiqui, et al.. (2017). A CEACAM6-High Airway Neutrophil Phenotype and CEACAM6-High Epithelial Cells Are Features of Severe Asthma. The Journal of Immunology. 198(8). 3307–3317. 24 indexed citations
10.
Jia, Guiquan, Sanjay Chandriani, Alexander R. Abbas, et al.. (2017). CXCL14 is a candidate biomarker for Hedgehog signalling in idiopathic pulmonary fibrosis. Thorax. 72(9). 780–787. 45 indexed citations
11.
Simpson, Jodie L., Ian A. Yang, John W. Upham, et al.. (2016). Periostin levels and eosinophilic inflammation in poorly-controlled asthma. BMC Pulmonary Medicine. 16(1). 67–67. 57 indexed citations
12.
Simpson, Jodie L., Ian A. Yang, John W. Upham, et al.. (2015). Sputum and serum periostin levels are associated with, but do not predict sputum eosinophil proportion in severe asthma. American Journal of Respiratory and Critical Care Medicine. 191. 2 indexed citations
13.
Shikotra, Aarti, David F. Choy, Salman Siddiqui, et al.. (2015). CEACAM6-high airway neutrophils and epithelial cells are a feature of severe asthma. PA910–PA910. 1 indexed citations
14.
DePianto, Daryle J., Sanjay Chandriani, Alexander R. Abbas, et al.. (2014). Heterogeneous gene expression signatures correspond to distinct lung pathologies and biomarkers of disease severity in idiopathic pulmonary fibrosis. Thorax. 70(1). 48–56. 200 indexed citations
15.
Jia, Guiquan, Richard W. Erickson, David F. Choy, et al.. (2012). Periostin is a systemic biomarker of eosinophilic airway inflammation in asthmatic patients. Journal of Allergy and Clinical Immunology. 130(3). 647–654.e10. 455 indexed citations breakdown →
16.
Woodruff, Prescott G., Barmak Modrek, David F. Choy, et al.. (2009). T-helper Type 2–driven Inflammation Defines Major Subphenotypes of Asthma. American Journal of Respiratory and Critical Care Medicine. 180(5). 388–395. 1310 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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