cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12212570
title: Supplementary Material for: Glomerular Transcriptome Profiles in Focal Glomerulosclerosis: New Genes and Pathways for Steroid Resistance

description: <b><i>Background:</i></b> Patients with focal segmental glomerulosclerosis (FSGS) characterized by steroid-resistant nephrotic syndrome (SRNS) are prone to progress to ESRD. Mechanism for the FSGS patients’ response to steroid treatment is still unknown and currently, it is impossible to predict the steroid resistance before treatment of patients with FSGS. <b><i>Methods:</i></b> To identify biomarkers and potential therapeutic targets of FSGS patients with SRNS, patients diagnosed as kidney biopsy-proven FSGS and nephrotic syndrome (NS) were prospectively enrolled. They were divided into 2 groups, steroid-sensitive NS and SRNS based on their treatment response. Cortical regions were selected from biopsied renal tissues, and glomeruli were isolated under an inverted microscope. RNA was prepared from the isolated glomeruli and further used for microarray analysis. Followed by multiple analyses, the top 6 highest and lowest, and a selected panel of differentially expressed genes obtained and their related pathways were validated via real-time PCR, western blot, and measurement of reactive oxygen species (ROS). <b><i>Results:</i></b> In SRNS group, we discovered that the most significant up-regulated pathway was primarily related to cellular amino acid and derivative metabolic process. Meanwhile, the most significant down-regulated pathway was primarily involved in anatomical structure morphogenesis. Moreover, we found <i>NADPH</i> <i>oxidase 4</i> (<i>NOX4</i>), one of the key regulators of renal ROS, at a much higher level in SRNS both at transcriptomic and proteomic levels. We also found the levels of ROS, p-p38 MAPK and matrix metalloproteinase (MMP)-2, which were all regulated by NOX4, were also higher in glomeruli isolated from SRNS patients. At last, we detected stimulated by retinoic acid gene 6 homolog (STRA6), a cell surface receptor formerly known as a gene preventing podocytes from over-proliferative lesion induced by HIV infection and was up-regulated by retinoic acid, expressed at a much higher level in SRNS kidneys. <b><i>Conclusion:</i></b> We found 2 potential mechanisms underline the SRNS, NOX4/ROS/P38 MAPK/MMP-2 pathway and STRA6. Our findings provided new insights into the steroid resistance.
DOI: 10.6084/m9.figshare.12212576
title: Supplementary Material for: New Criterion to Evaluate Acute-on-Chronic Kidney Injury Based on the Creatinine Reference Change

description: <b><i>Background:</i></b> The lack of consensus criteria of acute on chronic kidney injury (ACKI) affects the judgment for its clinical prognosis. <b><i>Methods:</i></b> In this study, we analyzed the data from 711,615 hospitalized adults who had at least 2 serum creatinine (SCr) tests within 30 days. We estimated the reference change value (RCV) of SCr given initial SCr level in adults without known risks of acute kidney injury other than chronic kidney disease (CKD). We proposed a criterion for ACKI based on the RCV of SCr (cROCK), which defined ACKI as a ≥25% increase in SCr in 7 days. We validated cROCK by its association with the risks of in-hospital mortality, death after discharge, and CKD progression in a large cohort of patients with CKD stage 3. <b><i>Results:</i></b> In 21,661 patients with CKD stage 3, a total of 3,145 (14.5%), 1,512 (7.0%), and 221 (1.0%) ACKI events were detected by both cROCK and Kidney Disease Improving Global Outcomes (KDIGO), cROCK only, and KDIGO only, respectively. cROCK detected 40% more ACKI events than KDIGO. Compared with patients without ACKI by both definitions, those with cROCK- but not KDIGO-defined ACKI had a significantly increased risk of in-hospital mortality (hazard ratio [HR] 5.53; 95% CI 3.75–8.16), death after discharge (HR 1.51; 95% CI 1.21–1.83), and CKD progression (OR 5.65; 95% CI 3.05–10.48). <b><i>Conclusions:</i></b> RCV-based criterion (cROCK) for ACKI is clinically valid in that it has a substantially improved sensitivity in identifying patients with high risk of adverse outcomes.
DOI: 10.6084/m9.figshare.12212585
title: Supplementary Material for: Chronic Kidney Disease Awareness and Longitudinal Health Outcomes: Results from the REasons for Geographic And Racial Differences in Stroke Study

description: <b><i>Background:</i></b> The majority of people with chronic kidney disease (CKD) are unaware of their kidney disease. Assessing the clinical significance of increasing CKD awareness has critical public health and healthcare delivery implications. Whether CKD awareness among persons with CKD is associated with longitudinal health behaviors, disease management, and health outcomes is unknown. <b><i>Methods:</i></b> We analyzed data from participants with CKD in the REasons for Geographic And Racial Differences in Stroke study, a national, longitudinal, population-based cohort. Our predictor was participant CKD awareness. Outcomes were (1) health behaviors (smoking avoidance, exercise, and nonsteroidal anti-inflammatory drug use); (2) CKD management indicators (angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use, statin use, systolic blood pressure, fasting blood glucose, and body mass index); (3) change in estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR); and (4) health outcomes (incident end-stage kidney disease [ESKD], coronary heart disease [CHD], stroke, and death). Logistic and linear regressions were used to examine the association of baseline CKD awareness with outcomes of interest, adjusted for CKD stage and participant demographic and clinical factors. <b><i>Results:</i></b> Of 6,529 participants with baseline CKD, 285 (4.4%) were aware of their CKD. Among the 3,586 participants who survived until follow-up (median 9.5 years), baseline awareness was not associated with subsequent odds of health behaviors, CKD management indicators, or changes in eGFR and UACR in adjusted analyses. Baseline CKD awareness was associated with increased risk of ESKD (adjusted hazard ratio [aHR] 1.44; 95% CI 1.08–1.92) and death (aHR 1.18; 95% CI 1.00–1.39), but not with subsequent CHD or stroke, in adjusted models. <b><i>Conclusions:</i></b> Individuals aware of their CKD were more likely to experience ESKD and death, suggesting that CKD awareness reflects disease severity. Most persons with CKD, including those that are high-risk, remain unaware of their CKD. There was no evidence of associations between baseline CKD awareness and longitudinal health behaviors, CKD management indicators, or eGFR decline and albuminuria.
DOI: 10.6084/m9.figshare.12212537
title: Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term labor: Supplementary tables S1-S5

description: Table S1. VIP list for metabolites differentiating controls, GDM and T2DM groupsTable S2. VIP list for metabolites differentiating controls and DM (GDM+T2DM) groups<br>Table S3. Metabolites associated with GDM (or T2DM) vs Controls<br>Table S4. Metabolites associated with gestational age at recruitment<br>Table S5. GLMNET models predicting GDM, GA at delivery and DM (GDM+T2DM)<br>
DOI: 10.6084/m9.figshare.12212549
title: The combined effects of temperature and relative humidity parameters on the reproduction of Stomoxys species in a laboratory setting

description: Statistical differences between different groups of <i>Stomoxys</i> flies incubated under various environmental conditions
DOI: 10.6084/m9.figshare.12212537.v1
title: Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term labor: Supplementary tables S1-S5

description: Table S1. VIP list for metabolites differentiating controls, GDM and T2DM groupsTable S2. VIP list for metabolites differentiating controls and DM (GDM+T2DM) groups<br>Table S3. Metabolites associated with GDM (or T2DM) vs Controls<br>Table S4. Metabolites associated with gestational age at recruitment<br>Table S5. GLMNET models predicting GDM, GA at delivery and DM (GDM+T2DM)<br>
DOI: 10.6084/m9.figshare.12212201
title: Additional file 9 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 9: Table S8. Generalized least squares by maximum likelihood linear models testing for differences in alpha diversity between the families in the hatchery.
DOI: 10.6084/m9.figshare.12212198
title: Additional file 8 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 8: Table S7. Generalized least squares by maximum likelihood linear models testing for differences in alpha diversity between the seawater and hemolymph microbiota within each environment.
DOI: 10.6084/m9.figshare.12212195
title: Additional file 7 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 7: Table S6. Indicator taxa for the oyster environment, family origin and phenotype.
DOI: 10.6084/m9.figshare.12212201.v1
title: Additional file 9 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 9: Table S8. Generalized least squares by maximum likelihood linear models testing for differences in alpha diversity between the families in the hatchery.
DOI: 10.6084/m9.figshare.12212198.v1
title: Additional file 8 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 8: Table S7. Generalized least squares by maximum likelihood linear models testing for differences in alpha diversity between the seawater and hemolymph microbiota within each environment.
DOI: 10.6084/m9.figshare.12212183
title: Additional file 3 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 3: Table S3. Protist OUT table with sequence tag count per sample and taxonomic affiliations.
DOI: 10.6084/m9.figshare.12212192
title: Additional file 6 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 6: Table S5. Orthogonal contrasts used to examine the differences between the oyster families.
DOI: 10.6084/m9.figshare.12212195.v1
title: Additional file 7 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 7: Table S6. Indicator taxa for the oyster environment, family origin and phenotype.
DOI: 10.6084/m9.figshare.12212183.v1
title: Additional file 3 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 3: Table S3. Protist OUT table with sequence tag count per sample and taxonomic affiliations.
DOI: 10.6084/m9.figshare.12212186
title: Additional file 4 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 4: Table S 4. Viral contig table with sequence tag count per sample and taxonomic affiliations.
DOI: 10.6084/m9.figshare.12212192.v1
title: Additional file 6 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 6: Table S5. Orthogonal contrasts used to examine the differences between the oyster families.
DOI: 10.6084/m9.figshare.12212180
title: Additional file 2 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 2: Table S2. Bacteria OUT table with sequence tag count per sample and taxonomic affiliations.
DOI: 10.6084/m9.figshare.12212174
title: Additional file 1 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 1: Table S1. Overview of specificity of primers used to amplify V1-V2 region of 18S rRNA genes.
DOI: 10.6084/m9.figshare.12212186.v1
title: Additional file 4 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 4: Table S 4. Viral contig table with sequence tag count per sample and taxonomic affiliations.
DOI: 10.6084/m9.figshare.12212180.v1
title: Additional file 2 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 2: Table S2. Bacteria OUT table with sequence tag count per sample and taxonomic affiliations.
DOI: 10.6084/m9.figshare.12212168
title: Additional file 13 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 13: Table 12. Permanova based on Bray-Curtis dissimilarities testing for differences in beta diversity between the families in the infectious environment.
DOI: 10.6084/m9.figshare.12212174.v1
title: Additional file 1 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 1: Table S1. Overview of specificity of primers used to amplify V1-V2 region of 18S rRNA genes.
DOI: 10.6084/m9.figshare.12212168.v1
title: Additional file 13 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 13: Table 12. Permanova based on Bray-Curtis dissimilarities testing for differences in beta diversity between the families in the infectious environment.
DOI: 10.6084/m9.figshare.12212156
title: Additional file 8 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 8: Table S7. Simulation results for nine complex pathogenic repeat expansions.
DOI: 10.6084/m9.figshare.12212159
title: Additional file 12 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 12: Table S11. Permanova based on Bray-Curtis dissimilarities testing for effects of the environment, genitors’ origin and selection pressure and their interactions on beta diversity.
DOI: 10.6084/m9.figshare.12212162
title: Additional file 9 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 9: Table S8. Overview of existing methods for detecting repeat expansions based on short-read data.
DOI: 10.6084/m9.figshare.12212150
title: Additional file 7 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 7: Table S6. Simulation results for expression-linked repeat expansions with short motifs.
DOI: 10.6084/m9.figshare.12212156.v1
title: Additional file 8 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 8: Table S7. Simulation results for nine complex pathogenic repeat expansions.
DOI: 10.6084/m9.figshare.12212162.v1
title: Additional file 9 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 9: Table S8. Overview of existing methods for detecting repeat expansions based on short-read data.
DOI: 10.6084/m9.figshare.12212138
title: Additional file 6 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 6: Table S5. Simulation results for 27 repeat expansions with long motifs.
DOI: 10.6084/m9.figshare.12212150.v1
title: Additional file 7 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 7: Table S6. Simulation results for expression-linked repeat expansions with short motifs.
DOI: 10.6084/m9.figshare.12212153
title: Additional file 11 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 11: Table S10. Linear mixed-effects models testing for effects of the environment, genitors’ origin, selection pressure and their interactions on alpha diversity.
DOI: 10.6084/m9.figshare.12212159.v1
title: Additional file 12 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 12: Table S11. Permanova based on Bray-Curtis dissimilarities testing for effects of the environment, genitors’ origin and selection pressure and their interactions on beta diversity.
DOI: 10.6084/m9.figshare.12212132
title: Additional file 5 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 5: Table S4. Simulation results for 22 large pathogenic repeat expansions.
DOI: 10.6084/m9.figshare.12212144
title: Additional file 10 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 10: Table S9. Permanova based on Bray-Curtis dissimilarities testing for differences in beta diversity between the families in the hatchery.
DOI: 10.6084/m9.figshare.12212153.v1
title: Additional file 11 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 11: Table S10. Linear mixed-effects models testing for effects of the environment, genitors’ origin, selection pressure and their interactions on alpha diversity.
DOI: 10.6084/m9.figshare.12212126
title: Additional file 1 of Development and validation of next generation sequencing based 35-gene hereditary cancer panel

description: Additional file 1: Table S1. Known associations between genes in the 35-gene hereditary cancer panel and caner type. Table S2. Prenetics hereditary cancer panel reportable range. Table S3. Quality control metrics. Table S4. Exonic coverage of 35 hereditary cancer genes based on a benchmark sequencing run with 43 samples. Table S5. Summary of Coriell samples (1000 Genome) used for assessment of SNV and indel detection. Table S6. Summary of 53 Coriell samples used for assessment of SNV and indel detection and interpretation. Table S7. Technically challenging variants in the present validation.
DOI: 10.6084/m9.figshare.12212129
title: Additional file 4 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 4: Table S3. Simulation results for 13 small pathogenic repeat expansions.
DOI: 10.6084/m9.figshare.12212132.v1
title: Additional file 5 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 5: Table S4. Simulation results for 22 large pathogenic repeat expansions.
DOI: 10.6084/m9.figshare.12212144.v1
title: Additional file 10 of Oyster hemolymph is a complex and dynamic ecosystem hosting bacteria, protists and viruses

description: Additional file 10: Table S9. Permanova based on Bray-Curtis dissimilarities testing for differences in beta diversity between the families in the hatchery.
DOI: 10.6084/m9.figshare.12212126.v1
title: Additional file 1 of Development and validation of next generation sequencing based 35-gene hereditary cancer panel

description: Additional file 1: Table S1. Known associations between genes in the 35-gene hereditary cancer panel and caner type. Table S2. Prenetics hereditary cancer panel reportable range. Table S3. Quality control metrics. Table S4. Exonic coverage of 35 hereditary cancer genes based on a benchmark sequencing run with 43 samples. Table S5. Summary of Coriell samples (1000 Genome) used for assessment of SNV and indel detection. Table S6. Summary of 53 Coriell samples used for assessment of SNV and indel detection and interpretation. Table S7. Technically challenging variants in the present validation.
DOI: 10.6084/m9.figshare.12212138.v1
title: Additional file 6 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 6: Table S5. Simulation results for 27 repeat expansions with long motifs.
DOI: 10.6084/m9.figshare.12212123
title: Additional file 3 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 3: Table S2. Repeats with long motifs and expression-linked repeats which were used for simulation.
DOI: 10.6084/m9.figshare.12212129.v1
title: Additional file 4 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 4: Table S3. Simulation results for 13 small pathogenic repeat expansions.
DOI: 10.6084/m9.figshare.12212111
title: Additional file 4 of Lag penalized weighted correlation for time series clustering

description: Additional file 4 Cluster assignments and DAVID enrichment for axolotl hLPWC as tab-delimited text files. The cluster assignment files contain the mapped human official gene symbols and do not have a header row.
DOI: 10.6084/m9.figshare.12212117
title: Additional file 5 of Lag penalized weighted correlation for time series clustering

description: Additional file 5 Cluster assignments and DAVID enrichment for axolotl lLPWC as tab-delimited text files. The cluster assignment files contain the mapped human official gene symbols and do not have a header row.
DOI: 10.6084/m9.figshare.12212120
title: Additional file 2 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 2: Table S1. Definitions of repeat expansions.
DOI: 10.6084/m9.figshare.12212123.v1
title: Additional file 3 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 3: Table S2. Repeats with long motifs and expression-linked repeats which were used for simulation.
DOI: 10.6084/m9.figshare.12212111.v1
title: Additional file 4 of Lag penalized weighted correlation for time series clustering

description: Additional file 4 Cluster assignments and DAVID enrichment for axolotl hLPWC as tab-delimited text files. The cluster assignment files contain the mapped human official gene symbols and do not have a header row.
DOI: 10.6084/m9.figshare.12212117.v1
title: Additional file 5 of Lag penalized weighted correlation for time series clustering

description: Additional file 5 Cluster assignments and DAVID enrichment for axolotl lLPWC as tab-delimited text files. The cluster assignment files contain the mapped human official gene symbols and do not have a header row.
DOI: 10.6084/m9.figshare.12212102
title: Additional file 3 of Lag penalized weighted correlation for time series clustering

description: Additional file 3 Cluster assignments and DAVID enrichment for yeast hLPWC as tab-delimited text files. The cluster assignment files contain a header row. The id is the phosphopeptide id from Kanshin et al. [32] and Uniprot is the accession number. There are no enriched terms for cluster 3.
DOI: 10.6084/m9.figshare.12212120.v1
title: Additional file 2 of ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data

description: Additional file 2: Table S1. Definitions of repeat expansions.
DOI: 10.6084/m9.figshare.12212090
title: Additional file 2 of Lag penalized weighted correlation for time series clustering

description: Additional file 2 Cluster assignments and DAVID enrichment for yeast lLPWC as tab-delimited text files. The cluster assignment files contain a header row. The id is the phosphopeptide id from Kanshin et al. [32] and Uniprot is the accession number.