Automated Author Profile

Xue, Dong

Current S-Index

51.3

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.5

Average Dataset Index per dataset

Total Datasets

96

Total datasets for this author

Average FAIR Score

18.4%

Average FAIR Score per dataset

Total Citations

55

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

CCDC 2241341: Experimental Crystal Structure Determination

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
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.5517/ccdc.csd.cc2f79bbJanuary 2025

CCDC 2379860: Experimental Crystal Structure Determination

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
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.5517/ccdc.csd.cc2kwfplJanuary 2025

CCDC 2241342: Experimental Crystal Structure Determination

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
1 Citation0 Mentions15% FAIR0.7 Dataset Index
10.5517/ccdc.csd.cc2f79ccJanuary 2025

CCDC 2427337: Experimental Crystal Structure Determination

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
1 Citation0 Mentions15% FAIR0.7 Dataset Index
10.5517/ccdc.csd.cc2mgv65January 2025

CCDC 2427959: Experimental Crystal Structure Determination

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
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.5517/ccdc.csd.cc2mhh8xJanuary 2025

CCDC 2410054: Experimental Crystal Structure Determination

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
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.5517/ccdc.csd.cc2lwvp1January 2025

CCDC 2394900: Experimental Crystal Structure Determination

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
1 Citation0 Mentions15% FAIR0.7 Dataset Index
10.5517/ccdc.csd.cc2ld2vyJanuary 2025

CCDC 2294059: Experimental Crystal Structure Determination

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
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.5517/ccdc.csd.cc2h04xlJanuary 2025

Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation

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
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.30029735.v1January 2025

Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation

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
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.30029735January 2025