cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28537596
title: Curcumin alleviates renal fibrosis in chronic kidney disease by targeting the circ_0008925-related pathway

description: Curcumin has been shown to inhibit renal fibrosis, but whether curcumin mediates renal fibrosis progression by regulating the circular RNA (circRNA)-related pathway remain unclear. TGF-β1 was used to construct renal injury and fibrosis cell model. Cell growth was evaluated by cell counting kit 8 assay, EdU assay and flow cytometry. Fibrosis marker and interleukin 6 signal transducer (IL6ST) protein levels were measured using western bolt analysis. Inflammation factor concentrations were determined by ELISA. Circ_0008925, miR-204-5p and IL6ST expression was assessed by qRT-PCR. Unilateral ureteral obstruction (UUO) mice models were constructed to assess the role of curcumin <i>in vivo</i>. Curcumin treatment alleviated TGF-β1-induced HK-2 cell apoptosis, inflammation and fibrosis <i>in vitro</i>, as well as relieved renal injury in UUO mice models <i>in vivo</i>. Circ_0008925 was highly expressed in TGF-β1-induced HK-2 cells and its expression was inhibited by curcumin. Circ_0008925 could sponge miR-204-5p to positively regulate IL6ST. The inhibition effect of curcumin on TGF-β1-induced HK-2 cell injury and fibrosis was reversed by circ_0008925 overexpression, miR-204-5p inhibitor or IL6ST upregulation. Besides, circ_0008925 knockdown inhibited TGF-β1-induced HK-2 cell injury and fibrosis by suppressing IL6ST expression. Curcumin relieved renal fibrosis through regulating circ_0008925/miR-204-5p/IL6ST axis. Curcumin inhibits renal fibrosis by reducing circ_0008925 expression.Circ_0008925 sponges miR-204-5p.MiR-204-5p targets IL6ST.Curcumin regulates the circ_0008925/miR-204-5p/IL6ST axis. Curcumin inhibits renal fibrosis by reducing circ_0008925 expression. Circ_0008925 sponges miR-204-5p. MiR-204-5p targets IL6ST. Curcumin regulates the circ_0008925/miR-204-5p/IL6ST axis.
DOI: 10.6084/m9.figshare.28537596.v1
title: Curcumin alleviates renal fibrosis in chronic kidney disease by targeting the circ_0008925-related pathway

description: Curcumin has been shown to inhibit renal fibrosis, but whether curcumin mediates renal fibrosis progression by regulating the circular RNA (circRNA)-related pathway remain unclear. TGF-β1 was used to construct renal injury and fibrosis cell model. Cell growth was evaluated by cell counting kit 8 assay, EdU assay and flow cytometry. Fibrosis marker and interleukin 6 signal transducer (IL6ST) protein levels were measured using western bolt analysis. Inflammation factor concentrations were determined by ELISA. Circ_0008925, miR-204-5p and IL6ST expression was assessed by qRT-PCR. Unilateral ureteral obstruction (UUO) mice models were constructed to assess the role of curcumin <i>in vivo</i>. Curcumin treatment alleviated TGF-β1-induced HK-2 cell apoptosis, inflammation and fibrosis <i>in vitro</i>, as well as relieved renal injury in UUO mice models <i>in vivo</i>. Circ_0008925 was highly expressed in TGF-β1-induced HK-2 cells and its expression was inhibited by curcumin. Circ_0008925 could sponge miR-204-5p to positively regulate IL6ST. The inhibition effect of curcumin on TGF-β1-induced HK-2 cell injury and fibrosis was reversed by circ_0008925 overexpression, miR-204-5p inhibitor or IL6ST upregulation. Besides, circ_0008925 knockdown inhibited TGF-β1-induced HK-2 cell injury and fibrosis by suppressing IL6ST expression. Curcumin relieved renal fibrosis through regulating circ_0008925/miR-204-5p/IL6ST axis. Curcumin inhibits renal fibrosis by reducing circ_0008925 expression.Circ_0008925 sponges miR-204-5p.MiR-204-5p targets IL6ST.Curcumin regulates the circ_0008925/miR-204-5p/IL6ST axis. Curcumin inhibits renal fibrosis by reducing circ_0008925 expression. Circ_0008925 sponges miR-204-5p. MiR-204-5p targets IL6ST. Curcumin regulates the circ_0008925/miR-204-5p/IL6ST axis.
DOI: 10.6084/m9.figshare.28766266
title: Causal associations between environmental factors and risk of IgA nephropathy and membranous nephropathy: a bidirectional Mendelian randomization and mediation analysis

description: IgA nephropathy (IgAN) and membranous nephropathy (MN) have intricate etiologies that are poorly understood. This study aimed to investigate the impact of genetically predicted environmental factors on IgAN and MN. We used bidirectional two-step Mendelian randomization (MR) analysis utilizing large-scale genome-wide association study (GWAS) data to investigate the relationships between 68 environmental exposures and IgAN and MN. The main method is inverse variance weighted (IVW). Sensitivity analyses were conducted to validate the causal estimates. Furthermore, the two-step MR was used to explore possible mediating effects. A total of 20 significant causal associations were identified. Genetically predicted educational attainment (EA), average household income, gluten-free diet, cheese intake, fresh fruit intake, cognitive performance, and intelligence were associated with a reduced risk of IgAN (<i>p</i> &lt; 0.05); whereas frequency of alcohol consumption, insomnia, triglycerides, transferrin saturation, percentage body fat, body mass index (BMI), waist circumference, and blood pressure were associated with the risk of IgAN (<i>p</i> &lt; 0.05). Genetically predicted EA and moderate to vigorous physical activity were associated with a reduced risk of MN (<i>p</i> &lt; 0.05); on the other hand, beef intake, waist-to-hip ratio, and nitrogen oxides were associated with the risk of MN (<i>p</i> &lt; 0.05). In addition, we observed that insomnia, BMI, and waist circumference partially mediated the causal link between EA and IgAN, with mediation proportions of 12.52%, 11.06%, and 9.93%, respectively. This study provides novel evidence of causal relationships between 20 genetically predicted environmental factors and the risk of IgAN and MN. These findings may inform targeted prevention strategies and contribute to improved disease risk assessment.
DOI: 10.6084/m9.figshare.28766266.v1
title: Causal associations between environmental factors and risk of IgA nephropathy and membranous nephropathy: a bidirectional Mendelian randomization and mediation analysis

description: IgA nephropathy (IgAN) and membranous nephropathy (MN) have intricate etiologies that are poorly understood. This study aimed to investigate the impact of genetically predicted environmental factors on IgAN and MN. We used bidirectional two-step Mendelian randomization (MR) analysis utilizing large-scale genome-wide association study (GWAS) data to investigate the relationships between 68 environmental exposures and IgAN and MN. The main method is inverse variance weighted (IVW). Sensitivity analyses were conducted to validate the causal estimates. Furthermore, the two-step MR was used to explore possible mediating effects. A total of 20 significant causal associations were identified. Genetically predicted educational attainment (EA), average household income, gluten-free diet, cheese intake, fresh fruit intake, cognitive performance, and intelligence were associated with a reduced risk of IgAN (<i>p</i> &lt; 0.05); whereas frequency of alcohol consumption, insomnia, triglycerides, transferrin saturation, percentage body fat, body mass index (BMI), waist circumference, and blood pressure were associated with the risk of IgAN (<i>p</i> &lt; 0.05). Genetically predicted EA and moderate to vigorous physical activity were associated with a reduced risk of MN (<i>p</i> &lt; 0.05); on the other hand, beef intake, waist-to-hip ratio, and nitrogen oxides were associated with the risk of MN (<i>p</i> &lt; 0.05). In addition, we observed that insomnia, BMI, and waist circumference partially mediated the causal link between EA and IgAN, with mediation proportions of 12.52%, 11.06%, and 9.93%, respectively. This study provides novel evidence of causal relationships between 20 genetically predicted environmental factors and the risk of IgAN and MN. These findings may inform targeted prevention strategies and contribute to improved disease risk assessment.
DOI: 10.6084/m9.figshare.28227152
title: Effects of high-sensitivity C-reactive protein and left ventricular hypertrophy on cognitive function in hemodialysis patients

description: To examine the effects of high-sensitivity C-reactive protein (hs-CRP) and left ventricular hypertrophy (LVH) on the cognitive function of hemodialysis (HD) patients, and to explore the relationship between hs-CRP, LVH, and cognitive impairment (CI). A cross-sectional study was conducted on 232 HD patients. Besides, general clinical data were gathered, and patients’ cognitive functions were assessed using the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ). CI risk factors were screened using logistic regression modeling based on hs-CRP values (low risk &lt;1 mg/L, intermediate risk 1–3 mg/L, and high risk &gt;3 mg/L) and LVH status (normal and hypertrophic) groupings. The synergistic effect of hs-CRP and LVH on CI was also analyzed using the EpiR package. Among HD patients, 122 (52.59%) patients had CI. Multifactorial logistic regression analysis showed that the following factors were associated with an increased risk of CI in HD patients: age (OR = 1.048; 95% CI 1.014–1.083; <i>p</i> = 0.005), LVH (OR = 3.741; 95% CI 1.828–7.657; <i>p</i> &lt; 0.001), and high-risk hs-CRP levels (&gt;3 mg/L; OR = 3.238; 95% CI 1.349–7.768; <i>p</i> = 0.009). In addition, there was a significant synergy between hs-CRP high risk (&gt;3 mg/L) and LVH. Age, LVH, and high risk of hs-CRP (&gt;3 mg/L) were independent risk factors for CI in HD patients. Moreover, HD patients with both hs-CRP high risk (&gt;3.0 mg/L) and LVH were at higher risk of developing CI, and lowering hs-CRP levels and preventing LVH may prevent CI.
DOI: 10.6084/m9.figshare.28227152.v1
title: Effects of high-sensitivity C-reactive protein and left ventricular hypertrophy on cognitive function in hemodialysis patients

description: To examine the effects of high-sensitivity C-reactive protein (hs-CRP) and left ventricular hypertrophy (LVH) on the cognitive function of hemodialysis (HD) patients, and to explore the relationship between hs-CRP, LVH, and cognitive impairment (CI). A cross-sectional study was conducted on 232 HD patients. Besides, general clinical data were gathered, and patients’ cognitive functions were assessed using the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ). CI risk factors were screened using logistic regression modeling based on hs-CRP values (low risk &lt;1 mg/L, intermediate risk 1–3 mg/L, and high risk &gt;3 mg/L) and LVH status (normal and hypertrophic) groupings. The synergistic effect of hs-CRP and LVH on CI was also analyzed using the EpiR package. Among HD patients, 122 (52.59%) patients had CI. Multifactorial logistic regression analysis showed that the following factors were associated with an increased risk of CI in HD patients: age (OR = 1.048; 95% CI 1.014–1.083; <i>p</i> = 0.005), LVH (OR = 3.741; 95% CI 1.828–7.657; <i>p</i> &lt; 0.001), and high-risk hs-CRP levels (&gt;3 mg/L; OR = 3.238; 95% CI 1.349–7.768; <i>p</i> = 0.009). In addition, there was a significant synergy between hs-CRP high risk (&gt;3 mg/L) and LVH. Age, LVH, and high risk of hs-CRP (&gt;3 mg/L) were independent risk factors for CI in HD patients. Moreover, HD patients with both hs-CRP high risk (&gt;3.0 mg/L) and LVH were at higher risk of developing CI, and lowering hs-CRP levels and preventing LVH may prevent CI.
DOI: 10.6084/m9.figshare.28537529.v1
title: Association between renal function and memory-related disease: evidence from the China Health and Retirement Longitudinal Study

description: Previous studies have reported that renal function is associated brain structure and cognitive dysfunction. However, the association between renal function and memory-related disease was not well characterized. Altogether, 5,282 individuals were included in this study based on China Longitudinal Study of Health and Retirement. Four estimated glomerular filtration rate indicators (eGFR), including CG, CKD-EPIscr, CKD-EPIscr-cys, and CKD-EPIcys were used to evaluate the association between renal function and memory-related disease. The multivariable-adjusted HRs (95% CIs) of the memory-related disease in the low eGFR group (eGFR &lt; 90 mL/min/1.73m<sup>2</sup>) were 1.56 (1.13–2.16) for CG, 1.56 (1.19–2.06) for CKD-EPIscr, 1.45 (1.06–1.99) for CKD-EPIscr-cys and 1.27 (0.91–1.77) for CKD-EPIcys, respectively. Similarly, each SD increase of eGFR was associated with reduced risk of memory-related disease on continuous analyses. Subgroup analyses further confirmed these associations. Moreover, the addition of eGFR to conventional risk factors improved the predictive power for memory-related disease (net reclassification improvement: 13.90% for CG, 19.83% for CKD-EPIscr and 30.65% for CKD-EPIscr-cys). In conclusion, impaired renal function was associated with the increasing risk of memory-related disease, indicating that renal function may be a potential indicator for memory-related disease. Further studies from other races and populations are needed to replicate our findings and to clarify the potential mechanisms.
DOI: 10.6084/m9.figshare.28149166
title: Assessment of renal insufficiency in patients with renal artery stenosis by multiparametric magnetic resonance imaging

description: To evaluate the function of kidneys with renal artery stenosis using multiparametric magnetic resonance imaging, assess the diagnostic efficacy of multiparametric magnetic resonance imaging for single kidney dysfunction. Renal multiparametric magnetic resonance imaging was performed on 62 patients with RAS using the Philips Ingenia CX 3.0 T MRI machine. The scanning sequences included arterial spin labeling, phase contrast MRI, diffusion weighted imaging, T1 mapping, and blood oxygen level-dependent MRI. All patients underwent radionuclide renal dynamic imaging, and the glomerular filtration rate (GFR) was calculated to determine renal function. Individual kidneys from renal artery stenosis patients were classified into normal (GFR ≥ 30) and reduced (GFR &lt; 30) groups and the ability of the uni- and multi-variate logistic regression model to predict the group was determined. MR parameters demonstrated considerable diagnostic efficacy for single kidney dysfunction, with AUC range of 0.597– 0.864. The strongest predictor was mean renal artery blood flow. The sensitivity and specificity were 0.93 and 0.69AUC was 0.864. The strongest predictors of the renal microstructure were cortical apparent diffusion coeffecient and T1 value, with ROC AUCs of 0.756 and 0.741, sensitivities of 0.875 and 0.689, and specificities of 0.537 and 0.731. Multiparametric MRI combined with the values of cortical renal blood flow and cortical T1 exhibited the highest diagnostic efficacy, with an AUC of 0.92, and sensitivity of 0.919, and specificity of 0.743. Multiparametric magnetic resonance imaging can effectively detect the single renal dysfunction of kidneys with renal artery stenosis, which holds promise for the diagnosis and prognosis of patients with renal artery stenosis. Multiple MR parameters demonstrated good predictive ability for single kidney function impairment.The combined application of multiparametric MRI indicators enhance the diagnostic efficacy of renal function impairment in renal artery stenosis.The renal cortical RBF combining the cortical T1 value exhibited the highest diagnostic efficacy for renal function impairment. Multiple MR parameters demonstrated good predictive ability for single kidney function impairment. The combined application of multiparametric MRI indicators enhance the diagnostic efficacy of renal function impairment in renal artery stenosis. The renal cortical RBF combining the cortical T1 value exhibited the highest diagnostic efficacy for renal function impairment.
DOI: 10.6084/m9.figshare.28149166.v1
title: Assessment of renal insufficiency in patients with renal artery stenosis by multiparametric magnetic resonance imaging

description: To evaluate the function of kidneys with renal artery stenosis using multiparametric magnetic resonance imaging, assess the diagnostic efficacy of multiparametric magnetic resonance imaging for single kidney dysfunction. Renal multiparametric magnetic resonance imaging was performed on 62 patients with RAS using the Philips Ingenia CX 3.0 T MRI machine. The scanning sequences included arterial spin labeling, phase contrast MRI, diffusion weighted imaging, T1 mapping, and blood oxygen level-dependent MRI. All patients underwent radionuclide renal dynamic imaging, and the glomerular filtration rate (GFR) was calculated to determine renal function. Individual kidneys from renal artery stenosis patients were classified into normal (GFR ≥ 30) and reduced (GFR &lt; 30) groups and the ability of the uni- and multi-variate logistic regression model to predict the group was determined. MR parameters demonstrated considerable diagnostic efficacy for single kidney dysfunction, with AUC range of 0.597– 0.864. The strongest predictor was mean renal artery blood flow. The sensitivity and specificity were 0.93 and 0.69AUC was 0.864. The strongest predictors of the renal microstructure were cortical apparent diffusion coeffecient and T1 value, with ROC AUCs of 0.756 and 0.741, sensitivities of 0.875 and 0.689, and specificities of 0.537 and 0.731. Multiparametric MRI combined with the values of cortical renal blood flow and cortical T1 exhibited the highest diagnostic efficacy, with an AUC of 0.92, and sensitivity of 0.919, and specificity of 0.743. Multiparametric magnetic resonance imaging can effectively detect the single renal dysfunction of kidneys with renal artery stenosis, which holds promise for the diagnosis and prognosis of patients with renal artery stenosis. Multiple MR parameters demonstrated good predictive ability for single kidney function impairment.The combined application of multiparametric MRI indicators enhance the diagnostic efficacy of renal function impairment in renal artery stenosis.The renal cortical RBF combining the cortical T1 value exhibited the highest diagnostic efficacy for renal function impairment. Multiple MR parameters demonstrated good predictive ability for single kidney function impairment. The combined application of multiparametric MRI indicators enhance the diagnostic efficacy of renal function impairment in renal artery stenosis. The renal cortical RBF combining the cortical T1 value exhibited the highest diagnostic efficacy for renal function impairment.
DOI: 10.6084/m9.figshare.28537529
title: Association between renal function and memory-related disease: evidence from the China Health and Retirement Longitudinal Study

description: Previous studies have reported that renal function is associated brain structure and cognitive dysfunction. However, the association between renal function and memory-related disease was not well characterized. Altogether, 5,282 individuals were included in this study based on China Longitudinal Study of Health and Retirement. Four estimated glomerular filtration rate indicators (eGFR), including CG, CKD-EPIscr, CKD-EPIscr-cys, and CKD-EPIcys were used to evaluate the association between renal function and memory-related disease. The multivariable-adjusted HRs (95% CIs) of the memory-related disease in the low eGFR group (eGFR &lt; 90 mL/min/1.73m<sup>2</sup>) were 1.56 (1.13–2.16) for CG, 1.56 (1.19–2.06) for CKD-EPIscr, 1.45 (1.06–1.99) for CKD-EPIscr-cys and 1.27 (0.91–1.77) for CKD-EPIcys, respectively. Similarly, each SD increase of eGFR was associated with reduced risk of memory-related disease on continuous analyses. Subgroup analyses further confirmed these associations. Moreover, the addition of eGFR to conventional risk factors improved the predictive power for memory-related disease (net reclassification improvement: 13.90% for CG, 19.83% for CKD-EPIscr and 30.65% for CKD-EPIscr-cys). In conclusion, impaired renal function was associated with the increasing risk of memory-related disease, indicating that renal function may be a potential indicator for memory-related disease. Further studies from other races and populations are needed to replicate our findings and to clarify the potential mechanisms.
DOI: 10.5281/zenodo.15400563
title: PowerBench Dataset – Part 1: Line Outage and State Estimation

description: PowerBench: Line Outage and State Estimation Datasets

This dataset is part of the PowerBench benchmark suite for evaluating machine learning methods in power distribution networks. It include two of the three core learning tasks:

Line Outage Detection



Simulates realistic single and multi phase line outages across the IEEE 34-bus, 123-bus, and 8500-node distribution feeders.

Includes outage event labels, network topology and sensor metadata.

Supports graph classification, branch/node level localization.


State Estimation



Focuses on voltage magnitude estimation using sparse bus and branch sensor data under varying operating conditions.

Suitable for node-level regression and graph-based imputation.


All scenarios are generated using OpenDSS with Python automation via OpenDSSDirect.py. Networks are exported as .gml graphs compatible with NetworkX. Metadata on sensor placements is included. See the included README.md for individual directory structure and data loading instructions.
DOI: 10.5281/zenodo.15400564
title: PowerBench Dataset – Part 1: Line Outage and State Estimation

description: PowerBench: Line Outage and State Estimation Datasets

This dataset is part of the PowerBench benchmark suite for evaluating machine learning methods in power distribution networks. It include two of the three core learning tasks:

Line Outage Detection



Simulates realistic single and multi phase line outages across the IEEE 34-bus, 123-bus, and 8500-node distribution feeders.

Includes outage event labels, network topology and sensor metadata.

Supports graph classification, branch/node level localization.


State Estimation



Focuses on voltage magnitude estimation using sparse bus and branch sensor data under varying operating conditions.

Suitable for node-level regression and graph-based imputation.


All scenarios are generated using OpenDSS with Python automation via OpenDSSDirect.py. Networks are exported as .gml graphs compatible with NetworkX. Metadata on sensor placements is included. See the included README.md for individual directory structure and data loading instructions.