Automated Author ProfileXue, Dong
Xue, Dong
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 51.3 (sum of 96 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Huapeng, Ruan ;
- Zhongtao, Feng ;
- Xue, Dong ;
- Haiyan, Cui ;
- Xinping, Wang ;
- Min, Liu
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Zhongtao, Feng ;
- Xue, Dong ;
- Haiyan, Cui ;
- Xinping, Wang ;
- Min, Liu ;
- Huapeng, Ruan
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Min, Liu ;
- Huapeng, Ruan ;
- Zhongtao, Feng ;
- Xue, Dong ;
- Haiyan, Cui ;
- Xinping, Wang
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Jiang, Qin ;
- Dong, Jianyang ;
- Lei, Fang ;
- Yu, Dejiang ;
- Li, Ting ;
- Sun, Huaming ;
- Xue, Dong
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Jiang, Qin ;
- Dong, Jianyang ;
- Yu, Dejiang ;
- Lei, Fang ;
- Li, Ting ;
- Kang, Tengfei ;
- Fan, Juan ;
- Sun, Huaming ;
- Xue, Dong
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Zhou, Xuechen ;
- Dong, Jianyang ;
- Liao, Huijuan ;
- Jiang, Qin ;
- Zhang, Bowen ;
- Li, Ting ;
- Lei, Fang ;
- Sun, Huaming ;
- Xue, Dong
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Liao, Huijuan ;
- Dong, Jianyang ;
- Zhou, Xuechen ;
- Jiang, Qin ;
- Lv, Zishan ;
- Lei, Fang ;
- Xue, Dong
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Li, Jingsheng ;
- Wang, Pengpeng ;
- Bai, Baoyu ;
- Xiao, Yulin ;
- Wan, Ya-Fei ;
- Yan, Yonggang ;
- Li, Fei ;
- Song, Geyang ;
- Li, Gang ;
- Wang, Chao ;
- Zhang, Xue-Peng ;
- Dong, Jianyang ;
- Kang, Tengfei ;
- Xue, Dong
Acute kidney injury (AKI) is a prevalent and life-threatening condition characterized by abrupt renal function decline and subsequent inflammatory cascades. PANoptosis has emerged as a significant contributor to the pathophysiology of AKI. This research aimed to explore the diagnostic and therapeutic implications of PANoptosis-related genes in AKI. Kidney biopsy transcriptomic expression data were obtained from the GEO database. Differentially expressed genes (DEGs) associated with PANoptosis were identified between AKI and controls. WGCNA identified hub PANoptosis-related genes. PANoptosis scores and immune cell infiltration were calculated by ssGSEA. Machine learning algorithms was used to select feature genes. ROC analysis evaluated their diagnostic performance. Drug–gene interactions were explored. We identified 3460 DEGs between AKI and controls (61 upregulated and 11 downregulated) related to PANoptosis, mainly enriched in cytokine signaling and apoptosis. Eight hub PANoptosis genes were identified. PANoptosis scores were significantly higher in AKI patients (p < 0.001). CASP8, CASP4, SFN, FAS, and CASP1 were selected as feature genes, with CASP8 having the highest AUC at 0.850 in the training set. A nomogram combining these genes demonstrated strong predictive power. Furthermore, these genes were related to immune cell infiltration positively and had potential drug associations. Validation in a renal ischemia–reperfusion injury rat model confirmed the upregulation of CASP8 (p < 0.01), CASP4 (p < 0.001), SFN (p < 0.0001), FAS (p < 0.01), and CASP1 (p < 0.01). Our study identifies PANoptosis-related genes as potential diagnostic markers and therapeutic targets in AKI, highlighting their role in immune dysregulation in AKI.
Authors
- Chen, Zhen ;
- Wang, Xiaogang ;
- Shao, Yifan ;
- Wang, Kai ;
- Xue, Dong
Acute kidney injury (AKI) is a prevalent and life-threatening condition characterized by abrupt renal function decline and subsequent inflammatory cascades. PANoptosis has emerged as a significant contributor to the pathophysiology of AKI. This research aimed to explore the diagnostic and therapeutic implications of PANoptosis-related genes in AKI. Kidney biopsy transcriptomic expression data were obtained from the GEO database. Differentially expressed genes (DEGs) associated with PANoptosis were identified between AKI and controls. WGCNA identified hub PANoptosis-related genes. PANoptosis scores and immune cell infiltration were calculated by ssGSEA. Machine learning algorithms was used to select feature genes. ROC analysis evaluated their diagnostic performance. Drug–gene interactions were explored. We identified 3460 DEGs between AKI and controls (61 upregulated and 11 downregulated) related to PANoptosis, mainly enriched in cytokine signaling and apoptosis. Eight hub PANoptosis genes were identified. PANoptosis scores were significantly higher in AKI patients (p < 0.001). CASP8, CASP4, SFN, FAS, and CASP1 were selected as feature genes, with CASP8 having the highest AUC at 0.850 in the training set. A nomogram combining these genes demonstrated strong predictive power. Furthermore, these genes were related to immune cell infiltration positively and had potential drug associations. Validation in a renal ischemia–reperfusion injury rat model confirmed the upregulation of CASP8 (p < 0.01), CASP4 (p < 0.001), SFN (p < 0.0001), FAS (p < 0.01), and CASP1 (p < 0.01). Our study identifies PANoptosis-related genes as potential diagnostic markers and therapeutic targets in AKI, highlighting their role in immune dysregulation in AKI.
Authors
- Chen, Zhen ;
- Wang, Xiaogang ;
- Shao, Yifan ;
- Wang, Kai ;
- Xue, Dong