cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27604813
title: Intraoperative hyperthermia is associated with increased acute kidney injury following cytoreductive surgery and hyperthermic intraperitoneal chemotherapy: a retrospective cohort study

description: Acute kidney injury (AKI) is common after cytoreduction surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) for peritoneal surface malignancies. Herein we analyzed the association between intraoperative hyperthermia and AKI following CRS-HIPEC. In this retrospective cohort study, we collected baseline and perioperative data from patients who underwent CRS-HIPEC mainly for pseudomyxoma peritonei between 2014 and 2020. Nasopharyngeal temperature was recorded at 5-min intervals. The area above the threshold was calculated for intraoperative hyperthermia (&gt;37.0 °C). AKI was diagnosed and classified according to the KDIGO creatinine criteria. A multivariable logistic regression model was established to assess the association between hyperthermia and AKI. A total of 480 patients were included in the analysis. Of these, 10.6% (51/480) developed AKI within 7 postoperative days. After correction for confounding factors, a larger area above the threshold of hyperthermia was significantly associated with an increased risk of AKI (odds ratio [OR] 1.36, 95% CI 1.14–1.63, <i>p</i> = 0.001). Among other factors, older age (OR 1.05, 95% CI 1.02–1.09, <i>p</i> = 0.002), postoperative hypotension requiring vasopressors (OR 2.09, 95% CI 1.02–4.27, <i>p</i> = 0.042), and intraperitoneal chemotherapy containing cisplatin (OR 2.75, 95% CI 1.20–6.33, <i>p</i> = 0.017) were also associated with an increased risk of AKI. Patients with AKI required longer mechanical ventilation, stayed longer in the intensive care unit and hospital, developed more complications, and required more intensive care unit readmission. Among patients undergoing CRS-HIPEC, intraoperative hyperthermia was independently associated with a higher risk of AKI; this effect was additive to other risk factors including cisplatin-containing chemotherapy.
DOI: 10.6084/m9.figshare.27604813.v1
title: Intraoperative hyperthermia is associated with increased acute kidney injury following cytoreductive surgery and hyperthermic intraperitoneal chemotherapy: a retrospective cohort study

description: Acute kidney injury (AKI) is common after cytoreduction surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) for peritoneal surface malignancies. Herein we analyzed the association between intraoperative hyperthermia and AKI following CRS-HIPEC. In this retrospective cohort study, we collected baseline and perioperative data from patients who underwent CRS-HIPEC mainly for pseudomyxoma peritonei between 2014 and 2020. Nasopharyngeal temperature was recorded at 5-min intervals. The area above the threshold was calculated for intraoperative hyperthermia (&gt;37.0 °C). AKI was diagnosed and classified according to the KDIGO creatinine criteria. A multivariable logistic regression model was established to assess the association between hyperthermia and AKI. A total of 480 patients were included in the analysis. Of these, 10.6% (51/480) developed AKI within 7 postoperative days. After correction for confounding factors, a larger area above the threshold of hyperthermia was significantly associated with an increased risk of AKI (odds ratio [OR] 1.36, 95% CI 1.14–1.63, <i>p</i> = 0.001). Among other factors, older age (OR 1.05, 95% CI 1.02–1.09, <i>p</i> = 0.002), postoperative hypotension requiring vasopressors (OR 2.09, 95% CI 1.02–4.27, <i>p</i> = 0.042), and intraperitoneal chemotherapy containing cisplatin (OR 2.75, 95% CI 1.20–6.33, <i>p</i> = 0.017) were also associated with an increased risk of AKI. Patients with AKI required longer mechanical ventilation, stayed longer in the intensive care unit and hospital, developed more complications, and required more intensive care unit readmission. Among patients undergoing CRS-HIPEC, intraoperative hyperthermia was independently associated with a higher risk of AKI; this effect was additive to other risk factors including cisplatin-containing chemotherapy.
DOI: 10.6084/m9.figshare.26311726
title: Mesangial cell-derived CircRNAs in chronic glomerulonephritis: RNA sequencing and bioinformatics analysis

description: Circular RNAs (circRNAs) have been shown to play critical roles in the initiation and progression of chronic glomerulonephritis (CGN), while their role from mesangial cells in contributing to the pathogenesis of CGN is rarely understood. Our study aims to explore the potential functions of mesangial cell-derived circRNAs using RNA sequencing (RNA-seq) and bioinformatics analysis. Mouse mesangial cells (MMCs) were stimulated by lipopolysaccharide (LPS) to establish an <i>in vitro</i> model of CGN. Pro-inflammatory cytokines and cell cycle stages were detected by Enzyme-linked immunosorbent assay (ELISA) and Flow Cytometry experiment, respectively. Subsequently, differentially expressed circRNAs (DE-circRNAs) were identified by RNA-seq. GEO microarrays were used to identify differentially expressed mRNAs (DE-mRNAs) between CGN and healthy populations. Weighted co-expression network analysis (WGCNA) was utilized to explore clinically significant modules of CGN. CircRNA-associated CeRNA networks were constructed by bioinformatics analysis. The hub mRNAs from CeRNA network were identified using LASSO algorithms. Furthermore, utilizing protein–protein interaction (PPI), gene ontology (GO), pathway enrichment (KEGG), and GSEA analyses to explore the potential biological function of target genes from CeRNA network. In addition, we investigated the relationships between immune cells and hub mRNAs from CeRNA network using CIBERSORT. The expression of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α was drastically increased in LPS-induced MMCs. The number of cells decreased significantly in the G1 phase but increased significantly in the S/G2 phase. A total of 6 DE-mRNAs were determined by RNA-seq, including 4 up-regulated circRNAs and 2 down-regulated circRNAs. WGCNA analysis identified 1747 DE-mRNAs of the turquoise module from CGN people in the GEO database. Then, the CeRNA networks, including 6 circRNAs, 38 miRNAs, and 80 mRNAs, were successfully constructed. The results of GO and KEGG analyses revealed that the target mRNAs were mainly enriched in immune, infection, and inflammation-related pathways. Furthermore, three hub mRNAs (BOC, MLST8, and HMGCS2) from the CeRNA network were screened using LASSO algorithms. GSEA analysis revealed that hub mRNAs were implicated in a great deal of immune system responses and inflammatory pathways, including IL-5 production, MAPK signaling pathway, and JAK-STAT signaling pathway. Moreover, according to an evaluation of immune infiltration, hub mRNAs have statistical correlations with neutrophils, plasma cells, monocytes, and follicular helper T cells. Our findings provide fundamental and novel insights for further investigations into the role of mesangial cell-derived circRNAs in CGN pathogenesis.
DOI: 10.6084/m9.figshare.26539441
title: Insomnia and sleep duration for kidney function: Mendelian randomization study

description: Extensive researches highlight the detrimental impact of sleep disorders such as insomnia and insufficient sleep duration on kidney function. However, establishing a clear causal relationship between insomnia, sleep duration, and kidney function remains challenging. This study aims to estimate this relationship using Mendelian randomization (MR). Independent genetic variants strongly associated with insomnia (<i>N</i> = 462,341) and sleep duration (<i>N</i> = 460,099) were selected as instrumental variables from corresponding genome-wide association studies (GWAS). Kidney function parameters, including serum creatinine, estimated glomerular filtration rate by cystatin C (eGFRcys), acute renal failure (ARF), chronic renal failure (CRF), kidney injury molecule-1, neutrophil gelatinase associated lipocalin, microalbuminuria, cystatin C, and β2 microglobulin, were derived from GWAS databases. A two-sample MR study was conducted to assess the causal relationship between sleep disorders and kidney function, and multivariable MR was used to identify potential mediators. The inverse-variance weighted was used as the primary estimate. MR analysis found robust evidence indicating that insomnia and short sleep duration were associated with an increased risk of elevated serum creatinine, regardless of adjusting for obesity. Causal links between sleep duration and eGFRcys or cystatin C were also identified. While genetically predicted insomnia and sleep duration were found to potentially impact ARF, CRF, microalbuminuria, and β2 microglobulin, the <i>p</i>-values in multivariable MR analysis became nonsignificant. No pleiotropy was detected. This study demonstrates a causal impact of insomnia on the risk of elevated serum creatinine and a positive effect of sleep duration on serum creatinine, eGFRcys, and cystatin C. Our findings also suggest their potential indirect effects on ARF, CRF, microalbuminuria, and β2 microglobulin mediated by obesity.
DOI: 10.6084/m9.figshare.27283236
title: Causal associations between kidney function and aortic valve stenosis: a bidirectional Mendelian randomization analysis

description: Aortic valve stenosis (AVS) is currently the most common heart valve disease. The results of observational studies on the incidence of AVS in the renal dysfunction population are contradictory due to the short follow-up period and different diagnostic criteria, etc. This study aimed to explore the causal relationship between kidney function and AVS using Mendelian randomization (MR) analysis. We acquired summary statistics of estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD) from the CKDGen Consortium and a study on AVS from the FinnGen biobank. Univariate and multivariable MR analyses were conducted to evaluate the causal associations. The MR-Egger intercept and MR-PRESSO Global test were applied to assess the pleiotropic effects. The heterogeneity of MR results was tested by Cochran’s Q statistic. Moreover, the Bonferroni and FDR corrections were performed for multiple tests. Genetically predicted decreased eGFR may be associated with a raised risk of AVS (OR = 0.045, <i>p</i> = 1.317e-04 by IVW; OR = 0.002, <i>p</i> = 0.004 by MR-Egger, OR = 0.091, <i>p</i> = 0.057 by WM). The causal association still established after multiple comparisons. Quality control analyses indicated the absence of heterogeneity and pleiotropy in our MR research. In addition, the causality of eGFR and AVS remained significant in multivariable MR analysis after adjusting BMI, hypertension, T2DM, LDL-C, and smoking. Our MR study discovered that reduced eGFR may be a causative risk factor for AVS. In addition, the evidence did not support a significant causal association of AVS on kidney function. The present study utilized bidirectional Mendelian randomization analysis to investigate the causal association between kidney function and aortic valve stenosis. The reduced estimated glomerular filtration rate may be associated with an increased risk of aortic valve stenosis. No significant causal relationship was found in reverse MR analysis.
DOI: 10.6084/m9.figshare.27283236.v1
title: Causal associations between kidney function and aortic valve stenosis: a bidirectional Mendelian randomization analysis

description: Aortic valve stenosis (AVS) is currently the most common heart valve disease. The results of observational studies on the incidence of AVS in the renal dysfunction population are contradictory due to the short follow-up period and different diagnostic criteria, etc. This study aimed to explore the causal relationship between kidney function and AVS using Mendelian randomization (MR) analysis. We acquired summary statistics of estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD) from the CKDGen Consortium and a study on AVS from the FinnGen biobank. Univariate and multivariable MR analyses were conducted to evaluate the causal associations. The MR-Egger intercept and MR-PRESSO Global test were applied to assess the pleiotropic effects. The heterogeneity of MR results was tested by Cochran’s Q statistic. Moreover, the Bonferroni and FDR corrections were performed for multiple tests. Genetically predicted decreased eGFR may be associated with a raised risk of AVS (OR = 0.045, <i>p</i> = 1.317e-04 by IVW; OR = 0.002, <i>p</i> = 0.004 by MR-Egger, OR = 0.091, <i>p</i> = 0.057 by WM). The causal association still established after multiple comparisons. Quality control analyses indicated the absence of heterogeneity and pleiotropy in our MR research. In addition, the causality of eGFR and AVS remained significant in multivariable MR analysis after adjusting BMI, hypertension, T2DM, LDL-C, and smoking. Our MR study discovered that reduced eGFR may be a causative risk factor for AVS. In addition, the evidence did not support a significant causal association of AVS on kidney function. The present study utilized bidirectional Mendelian randomization analysis to investigate the causal association between kidney function and aortic valve stenosis. The reduced estimated glomerular filtration rate may be associated with an increased risk of aortic valve stenosis. No significant causal relationship was found in reverse MR analysis.
DOI: 10.6084/m9.figshare.26539441.v1
title: Insomnia and sleep duration for kidney function: Mendelian randomization study

description: Extensive researches highlight the detrimental impact of sleep disorders such as insomnia and insufficient sleep duration on kidney function. However, establishing a clear causal relationship between insomnia, sleep duration, and kidney function remains challenging. This study aims to estimate this relationship using Mendelian randomization (MR). Independent genetic variants strongly associated with insomnia (<i>N</i> = 462,341) and sleep duration (<i>N</i> = 460,099) were selected as instrumental variables from corresponding genome-wide association studies (GWAS). Kidney function parameters, including serum creatinine, estimated glomerular filtration rate by cystatin C (eGFRcys), acute renal failure (ARF), chronic renal failure (CRF), kidney injury molecule-1, neutrophil gelatinase associated lipocalin, microalbuminuria, cystatin C, and β2 microglobulin, were derived from GWAS databases. A two-sample MR study was conducted to assess the causal relationship between sleep disorders and kidney function, and multivariable MR was used to identify potential mediators. The inverse-variance weighted was used as the primary estimate. MR analysis found robust evidence indicating that insomnia and short sleep duration were associated with an increased risk of elevated serum creatinine, regardless of adjusting for obesity. Causal links between sleep duration and eGFRcys or cystatin C were also identified. While genetically predicted insomnia and sleep duration were found to potentially impact ARF, CRF, microalbuminuria, and β2 microglobulin, the <i>p</i>-values in multivariable MR analysis became nonsignificant. No pleiotropy was detected. This study demonstrates a causal impact of insomnia on the risk of elevated serum creatinine and a positive effect of sleep duration on serum creatinine, eGFRcys, and cystatin C. Our findings also suggest their potential indirect effects on ARF, CRF, microalbuminuria, and β2 microglobulin mediated by obesity.
DOI: 10.6084/m9.figshare.27281036
title: Improved survival prediction for kidney transplant outcomes using artificial intelligence-based models: development of the UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool

description: The UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool, developed using advanced artificial intelligence (AI), significantly enhances the prediction of outcomes for deceased-donor kidney transplants in the UK. This study analyzed data from the UK Transplant Registry (UKTR), including 29,713 transplant cases between 2008 and 2022, to assess the predictive performance of three machine learning models: XGBoost, Random Survival Forest, and Optimal Decision Tree. Among these, XGBoost demonstrated exceptional performance with the highest concordance index of 0.74 and an area under the curve (AUC) consistently above 0.73, indicating robust discriminative ability and calibration. In comparison to the traditional Kidney Donor Risk Index (KDRI), which achieved a lower concordance index of 0.57, the UK-DTOP model marked a significant improvement, underscoring its superior predictive accuracy. The advanced capabilities of the XGBoost model were further highlighted through calibration assessments using the Integrated Brier Score (IBS), showing a score of 0.14, indicative of precise survival probability predictions. Additionally, unsupervised learning <i>via</i> k-means clustering was employed to identify five distinct clusters based on donor and transplant characteristics, uncovering nuanced insights into graft survival outcomes. These clusters were further analyzed using Bayesian Cox regression, which confirmed significant survival outcome variations across the clusters, thereby validating the model’s effectiveness in enhancing risk stratification. The UK-DTOP tool offers a comprehensive decision-support system that significantly refines pre-transplant decision-making. The study’s findings advocate for the adoption of AI-enhanced tools in healthcare systems to optimize organ matching and transplant success, potentially guiding future developments in global transplant practices.
DOI: 10.6084/m9.figshare.27281036.v1
title: Improved survival prediction for kidney transplant outcomes using artificial intelligence-based models: development of the UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool

description: The UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool, developed using advanced artificial intelligence (AI), significantly enhances the prediction of outcomes for deceased-donor kidney transplants in the UK. This study analyzed data from the UK Transplant Registry (UKTR), including 29,713 transplant cases between 2008 and 2022, to assess the predictive performance of three machine learning models: XGBoost, Random Survival Forest, and Optimal Decision Tree. Among these, XGBoost demonstrated exceptional performance with the highest concordance index of 0.74 and an area under the curve (AUC) consistently above 0.73, indicating robust discriminative ability and calibration. In comparison to the traditional Kidney Donor Risk Index (KDRI), which achieved a lower concordance index of 0.57, the UK-DTOP model marked a significant improvement, underscoring its superior predictive accuracy. The advanced capabilities of the XGBoost model were further highlighted through calibration assessments using the Integrated Brier Score (IBS), showing a score of 0.14, indicative of precise survival probability predictions. Additionally, unsupervised learning <i>via</i> k-means clustering was employed to identify five distinct clusters based on donor and transplant characteristics, uncovering nuanced insights into graft survival outcomes. These clusters were further analyzed using Bayesian Cox regression, which confirmed significant survival outcome variations across the clusters, thereby validating the model’s effectiveness in enhancing risk stratification. The UK-DTOP tool offers a comprehensive decision-support system that significantly refines pre-transplant decision-making. The study’s findings advocate for the adoption of AI-enhanced tools in healthcare systems to optimize organ matching and transplant success, potentially guiding future developments in global transplant practices.
DOI: 10.15468/dl.h899vv
title: Occurrence Download

description: A dataset containing 4725 records in GBIF matching the query:
SELECT
 '41' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= -28.773895969281842
 AND decimalLatitude &lt;= -24.552553756517728
 AND decimalLongitude &gt;= 111.09096484983384
 AND decimalLongitude &lt;= 119.08601953811215
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 4725 records were derived from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003397-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26969393
title: Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study

description: IgA nephropathy (IgAN) is intimately linked to mucosal immune responses, with nasopharyngeal and intestinal lymphoid tissues being crucial for its abnormal mucosal immunity. The specific pathogenic bacteria in these sites associated with IgAN, however, remain elusive. Our study employs 16S rRNA sequencing and machine learning (ML) approaches to identify specific pathogenic bacteria in these locations and to investigate common pathogens that may exacerbate IgAN. In this cross-sectional analysis, we collected pharyngeal swabs and stool specimens from IgAN patients and healthy controls. We applied 16SrRNA sequencing to identify differential microbial populations. ML algorithms were then used to classify IgAN based on these microbial differences. Spearman correlation analysis was employed to link key bacteria with clinical parameters. We observed a reduced microbial diversity in IgAN patients compared to healthy controls. In the gut microbiota of IgAN patients, increases in <i>Bacteroides</i>, <i>Escherichia-Shigella,</i> and <i>Parabacteroides</i>, and decreases in <i>Parasutterella</i>, <i>Dialister</i>, <i>Faecalibacterium</i>, and <i>Subdoligranulum</i> were notable. In the respiratory microbiota, increases in <i>Neisseria</i>, <i>Streptococcus, Fusobacterium</i>, <i>Porphyromonas</i>, and <i>Ralstonia</i>, and decreases in <i>Prevotella</i>, <i>Leptotrichia</i>, and <i>Veillonella</i> were observed. Post-immunosuppressive therapy, <i>Oxalobacter</i> and <i>Butyricoccus</i> levels were significantly reduced in the gut, while <i>Neisseria</i> and <i>Actinobacillus</i> levels decreased in the respiratory tract. <i>Veillonella</i> and <i>Fusobacterium</i> appeared to influence IgAN through dual immune loci, with <i>Fusobacterium</i> abundance correlating with IgAN severity. This study revealing that changes in flora structure could provide important pathological insights for identifying therapeutic targets, and ML could facilitate noninvasive diagnostic methods for IgAN.