Xiaobing Long

905 citations
15 papers · 664 indexed · h-index 10

Impact in

Papers in

Xiaobing Long

14 papers receiving 655 citations

Peers

Xiaobing Long
Comparison fields: 5 of 73
  • Otorhinolaryngology 195
  • Immunology and Allergy 141
  • Neurology 160
  • Cancer Research 118
  • Physiology 171
Replace Nader Akbari Dilmaghani with:
Nader Akbari Dilmaghani Iran
Mireya Fuentes Spain
Oğuz Güçlü Türkiye
Stefania Agostino Italy
Abby L. Dotson United States
Takao Hamamoto Japan
Hironobu Nishijima Japan
Zhaohui Shi China
Enver Altaş Türkiye
Xiaobing Long relative to Nader Akbari Dilmaghani Iran Nader Akbari Dilmaghani's profile →
Citations per field
00.5×7.8×
Nader Akbari Dilmaghani · 1×
Citations per year

Countries citing papers authored by Xiaobing Long

Since Specialization
Citations

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

Fields of papers citing papers by Xiaobing Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Xiaobing Long, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Xiaobing Long Line = papers co-authored together Xiaobing Long links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 2020238
2 2018176
3 202051
4 201643
5 202331
6 202128
7 202027
8 202127
9 201815
10 202011
11 20208
12 20215
13
Neutrophil-to-lymphocyte ratio is a powerful predictor of adult patients with acute respiratory distress syndrome who might benefit from corticosteroid therapy.
20212
14 20222
15 20240

About Xiaobing Long

Xiaobing Long is a scholar working on Neurology, Otorhinolaryngology, Biological Psychiatry, Cancer Research and Immunology and Allergy, having authored 15 papers that have together received 664 indexed citations. Recurring topics across this work include Neuroinflammation and Neurodegeneration Mechanisms (6 papers), MicroRNA in disease regulation (2 papers), Allergic Rhinitis and Sensitization (2 papers), Alzheimer's disease research and treatments (2 papers), Neonatal and fetal brain pathology (2 papers), Asthma and respiratory diseases (2 papers), Sinusitis and nasal conditions (2 papers) and Extracellular vesicles in disease (2 papers). The work is most often cited by research in Otorhinolaryngology (195 citations), Immunology and Allergy (141 citations), Neurology (160 citations), Cancer Research (118 citations) and Physiology (171 citations). Xiaobing Long has collaborated with scholars based in China, Philippines and United States. Frequent co-authors include Huaqiu Zhang, Qian Jiang, Yiping Yang, Xiaolong Yao, Xuejun He, Kai Zhao, Weidong Tian, Haozhou Wang, Bo Liao and Yang Wang. Their work appears in journals such as Molecular Therapy, Allergy, Aging, Journal of Neuroinflammation and International Immunopharmacology.

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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