Aiying Xing

531 total citations
34 papers, 391 citations indexed

About

Aiying Xing is a scholar working on Infectious Diseases, Epidemiology and Surgery. According to data from OpenAlex, Aiying Xing has authored 34 papers receiving a total of 391 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Infectious Diseases, 24 papers in Epidemiology and 14 papers in Surgery. Recurrent topics in Aiying Xing's work include Tuberculosis Research and Epidemiology (25 papers), Mycobacterium research and diagnosis (16 papers) and Infectious Diseases and Tuberculosis (10 papers). Aiying Xing is often cited by papers focused on Tuberculosis Research and Epidemiology (25 papers), Mycobacterium research and diagnosis (16 papers) and Infectious Diseases and Tuberculosis (10 papers). Aiying Xing collaborates with scholars based in China and Australia. Aiying Xing's co-authors include Hongyan Jia, Zongde Zhang, Liping Pan, Boping Du, Qi Sun, Rongrong Wei, Fei Liu, Mengqiu Gao, Xia Zhang and Zihui Li and has published in prestigious journals such as PLoS ONE, Scientific Reports and Frontiers in Immunology.

In The Last Decade

Aiying Xing

32 papers receiving 387 citations

Peers

Aiying Xing

EPIDE

SURGE

IMMUN

Boping Du China

Xirong Wu China

Gael Fenhalls South Africa

Kirsten Wall United States

Erin E. Smith United States

Toshinori Tsukahara Japan

Vijayalakshmi Valluri India

Saima Ahmed United States

Sayma Rahman Sweden

Marc Rodrigue France

Boping Du China

Aiying Xing

244 ×0.9

184 ×0.9

EPIDE

156 ×1.0

SURGE

77 ×0.7

33 ×0.8

IMMUN

Citations per year, relative to Aiying Xing Aiying Xing (= 1×) peers Boping Du

Countries citing papers authored by Aiying Xing

Since Specialization

Citations

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

Fields of papers citing papers by Aiying Xing

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aiying Xing

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

All Works

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20 of 20 papers shown

Dong, Jing, Ruixue Song, Yingchao Wang, et al.. (2024). Identification of important modules and biomarkers in tuberculosis based on WGCNA. Frontiers in Microbiology. 15. 1354190–1354190. 2 indexed citations

Li, Zihui, Bing Wang, Boping Du, et al.. (2024). The incremental value of Mycobacterium tuberculosis trace nucleic acid detection in CT-guided percutaneous biopsy needle rinse solutions for the diagnosis of tuberculosis. Frontiers in Microbiology. 15. 1335526–1335526. 1 indexed citations

Deng, Ling, Boping Du, Aiying Xing, et al.. (2024). T-cell receptor and B-cell receptor repertoires profiling in pleural tuberculosis. Frontiers in Immunology. 15. 1473486–1473486.

Li, Wensheng, Weibi Chen, Qi Sun, et al.. (2022). Identification of protein biomarkers in host cerebrospinal fluid for differential diagnosis of tuberculous meningitis and other meningitis. Frontiers in Neurology. 13. 886040–886040. 6 indexed citations

Xing, Aiying, Zihui Li, Liping Pan, et al.. (2022). Rapid Detection of Mycobacterium tuberculosis in Pleural Fluid Using Resuscitation-Promoting Factor-Based Thin Layer Agar Culture Method. Frontiers in Microbiology. 13. 803521–803521. 2 indexed citations

Pan, Liping, Hongyan Jia, Guofang Deng, et al.. (2022). Diagnostic Performance of a Novel CXCL10 mRNA Release Assay for Mycobacterium tuberculosis Infection. Frontiers in Microbiology. 13. 825413–825413. 3 indexed citations

Cai, Yi, Jialou Zhu, Lanyue Zhang, et al.. (2019). Histone deacetylase inhibitors impair the host immune response against Mycobacterium tuberculosis infection. Tuberculosis. 118. 101861–101861. 6 indexed citations

Pan, Liping, Fei Liu, Jin-Li Zhang, et al.. (2019). Genome-Wide miRNA Analysis Identifies Potential Biomarkers in Distinguishing Tuberculous and Viral Meningitis. Frontiers in Cellular and Infection Microbiology. 9. 323–323. 21 indexed citations

Jia, Hongyan, Jinghui Wang, Liping Pan, et al.. (2019). A Two-Way Proteome Microarray Strategy to Identify Novel Mycobacterium tuberculosis-Human Interactors. Frontiers in Cellular and Infection Microbiology. 9. 65–65. 6 indexed citations

10.

Xie, Li, Yonghong Zhang, Fei Gao, et al.. (2018). Prospective Comparison of QFT-GIT and T-SPOT.TB Assays for Diagnosis of Active Tuberculosis. Scientific Reports. 8(1). 5882–5882. 31 indexed citations

11.

Xiao, Jing, Hongyan Jia, Liping Pan, et al.. (2018). Application of the CRISPRi system to repress sepF expression in Mycobacterium smegmatis. Infection Genetics and Evolution. 72. 183–190. 13 indexed citations

12.

Pan, Liping, Hongyan Jia, Zhiguo Zhang, et al.. (2018). Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection. Frontiers in Microbiology. 9. 1267–1267. 31 indexed citations

13.

Li, Zihui, Boping Du, Jing Li, et al.. (2017). Cerebrospinal fluid metabolomic profiling in tuberculous and viral meningitis: Screening potential markers for differential diagnosis. Clinica Chimica Acta. 466. 38–45. 26 indexed citations

14.

Pan, Liping, Na Wei, Hongyan Jia, et al.. (2017). Genome-wide transcriptional profiling identifies potential signatures in discriminating active tuberculosis from latent infection. Oncotarget. 8(68). 112907–112916. 22 indexed citations

15.

Jia, Hongyan, Liping Pan, Boping Du, et al.. (2016). Diagnostic performance of interferon-γ release assay for lymph node tuberculosis. Diagnostic Microbiology and Infectious Disease. 85(1). 56–60. 12 indexed citations

16.

Pan, Liping, Hongyan Jia, Fei Liu, et al.. (2015). [Evaluation of T-SPOT.TB assay in the diagnosis of pulmonary tuberculosis within different age groups].. PubMed. 38(12). 892–6. 1 indexed citations

17.

Pan, Liping, Hongyan Jia, Fei Liu, et al.. (2015). Risk factors for false-negative T-SPOT.TB assay results in patients with pulmonary and extra-pulmonary TB. Journal of Infection. 70(4). 367–380. 52 indexed citations

18.

Zhang, Xia, Fei Liu, Qi Li, et al.. (2014). A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection. Diagnostic Microbiology and Infectious Disease. 79(4). 432–437. 13 indexed citations

19.

Liu, Yang, Guanglu Jiang, Shijie Zhou, et al.. (2013). Application of Hyperbranched Rolling Circle Amplification for Direct Detection of Mycobacterium Tuberculosis in Clinical Sputum Specimens. PLoS ONE. 8(6). e64583–e64583. 12 indexed citations

20.

Zhang, Xia, Hongyan Jia, Fei Liu, et al.. (2013). Prevalence and Risk Factors for Latent Tuberculosis Infection among Health Care Workers in China: A Cross-Sectional Study. PLoS ONE. 8(6). e66412–e66412. 50 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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