cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6508634
title: Supplementary Material for: Molecular Nodal Staging Using miRNA Expression in Lung Cancer Patients by Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration

description: <b><i>Background:</i></b> The limited negative predictive value of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has often been discussed. <b><i>Objective:</i></b> The aim of this study was to identify a highly sensitive molecular biomarker for lymph node staging by EBUS-TBNA. <b><i>Methods:</i></b> Five microRNAs (miRNAs) (miR-200a, miR-200b, miR-200c, miR-141, and let-7e) were selected as biomarker candidates for the detection of nodal metastasis in a miRNA expression analysis. After having established a cutoff level of expression for each marker to differentiate malignant from benign lymph nodes among surgically dissected lymph nodes, the cutoff level was applied to snap-frozen EBUS-TBNA samples. Archived formalin-fixed paraffin- embedded (FFPE) samples rebiopsied by EBUS-TBNA after induction chemoradiotherapy were also analyzed. <b><i>Results:</i></b> The expression of all candidate miRNAs was significantly higher in metastatic lymph nodes than in benign ones (<i>p</i> &lt; 0.05) among the surgical samples. miR-200c showed the highest diagnostic yield, with a sensitivity of 95.4% and a specificity of 100%. When the cutoff value for miR-200c was applied to the snap-frozen EBUS-TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 97.4, 81.8, 95.0, 90.0, and 94.0%, respectively. For restaging FFPE EBUS- TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 100, 60.0, 80.0, 100, and 84.6%, respectively. Among the restaged samples, 4 malignant lymph nodes were false negative by EBUS-TBNA, but they were accurately identified by miR-200c. <b><i>Conclusions:</i></b> miR-200c can be used as a highly sensitive molecular staging biomarker that will enhance nodal staging of lung cancer.
DOI: 10.6084/m9.figshare.6508634.v1
title: Supplementary Material for: Molecular Nodal Staging Using miRNA Expression in Lung Cancer Patients by Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration

description: <b><i>Background:</i></b> The limited negative predictive value of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has often been discussed. <b><i>Objective:</i></b> The aim of this study was to identify a highly sensitive molecular biomarker for lymph node staging by EBUS-TBNA. <b><i>Methods:</i></b> Five microRNAs (miRNAs) (miR-200a, miR-200b, miR-200c, miR-141, and let-7e) were selected as biomarker candidates for the detection of nodal metastasis in a miRNA expression analysis. After having established a cutoff level of expression for each marker to differentiate malignant from benign lymph nodes among surgically dissected lymph nodes, the cutoff level was applied to snap-frozen EBUS-TBNA samples. Archived formalin-fixed paraffin- embedded (FFPE) samples rebiopsied by EBUS-TBNA after induction chemoradiotherapy were also analyzed. <b><i>Results:</i></b> The expression of all candidate miRNAs was significantly higher in metastatic lymph nodes than in benign ones (<i>p</i> &lt; 0.05) among the surgical samples. miR-200c showed the highest diagnostic yield, with a sensitivity of 95.4% and a specificity of 100%. When the cutoff value for miR-200c was applied to the snap-frozen EBUS-TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 97.4, 81.8, 95.0, 90.0, and 94.0%, respectively. For restaging FFPE EBUS- TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 100, 60.0, 80.0, 100, and 84.6%, respectively. Among the restaged samples, 4 malignant lymph nodes were false negative by EBUS-TBNA, but they were accurately identified by miR-200c. <b><i>Conclusions:</i></b> miR-200c can be used as a highly sensitive molecular staging biomarker that will enhance nodal staging of lung cancer.
DOI: 10.6084/m9.figshare.6508253
title: Supplementary Material for: A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus

description: <b><i>Introduction:</i></b> Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. <b><i>Methods:</i></b> Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11–13<sup>+6</sup> weeks’ gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (&lt; 24 weeks’ gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. <b><i>Results:</i></b> Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (<i>p</i> &lt; 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all <i>p</i> &lt; 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89–0.94) overall, and 0.93 (95% CI 0.89–0.96) for early GDM, in a combined multivariate prediction model. <b><i>Conclusions:</i></b> A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM.
DOI: 10.6084/m9.figshare.6508250
title: SFF and Mapping files for demultiplexing

description: Original standard flowgram format (sff) files and mapping files for demultiplexing The provided mapping files contain the barcode and linker primer sequences that were used for sample-wise indexing in 454 pyrosequencing. This information was used to demultiplex the raw pooled sequencing data (provided here as .sff files) into the FASTQ-format files for each sample that are available through the European Nucleotide Archive (ENA) at the European Bioinformatics Institute (EBI) under accession number PRJEB25221.<br><br> <b>Reference</b> <b>Ingham AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM, Müller KG, and Pamp SJ. (2019) </b>Specific gut microbiome members are associated with distinct immune markers in pediatric allogeneic hematopoietic stem cell transplantation. Microbiome 7:131 https://doi.org/10.1186/s40168-019-0745-z
DOI: 10.6084/m9.figshare.6508250.v1
title: SFF and Mapping files for demultiplexing

description: Original standard flowgram format (sff) files and mapping files for demultiplexing The provided mapping files contain the barcode and linker primer sequences that were used for sample-wise indexing in 454 pyrosequencing. This information was used to demultiplex the raw pooled sequencing data (provided here as .sff files) into the FASTQ-format files for each sample that are available through the European Nucleotide Archive (ENA) at the European Bioinformatics Institute (EBI) under accession number PRJEB25221.<br><br> <b>Reference</b> <b>Ingham AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM, Müller KG, and Pamp SJ. (2019) </b>Specific gut microbiome members are associated with distinct immune markers in pediatric allogeneic hematopoietic stem cell transplantation. Microbiome 7:131 https://doi.org/10.1186/s40168-019-0745-z
DOI: 10.6084/m9.figshare.6508238
title: R scripts documenting the statistical data analyses

description: R scripts documenting the statistical data analyses The provided R scripts contain all code that was used for the main analyses steps of the study, and facilitates the reproduction of results presented in tables and figures. The scripts are numbered and named according to the analyses steps and correspond to the .html reports provided as additional files with the journal article. <br><br> <b>Reference</b> <b>Ingham AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM, Müller KG, and Pamp SJ. (2019) </b>Specific gut microbiome members are associated with distinct immune markers in pediatric allogeneic hematopoietic stem cell transplantation. Microbiome 7:131 https://doi.org/10.1186/s40168-019-0745-z <br>
DOI: 10.6084/m9.figshare.6508238.v1
title: R scripts documenting the statistical data analyses

description: R scripts documenting the statistical data analyses The provided R scripts contain all code that was used for the main analyses steps of the study, and facilitates the reproduction of results presented in tables and figures. The scripts are numbered and named according to the analyses steps and correspond to the .html reports provided as additional files with the journal article. <br><br> <b>Reference</b> <b>Masche AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM, Müller KG, and Pamp SJ. (2018) </b>Specific Gut Microbiome Members are Associated with Distinct Immune Markers in Allogeneic Hematopoietic Stem Cell Transplantation.<b> Journal</b>, <b>Vol</b>., doi:XXX. <br>
DOI: 10.6084/m9.figshare.6508238.v2
title: R scripts documenting the statistical data analyses

description: R scripts documenting the statistical data analyses The provided R scripts contain all code that was used for the main analyses steps of the study, and facilitates the reproduction of results presented in tables and figures. The scripts are numbered and named according to the analyses steps and correspond to the .html reports provided as additional files with the journal article. <br><br> <b>Reference</b> <b>Masche AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM, Müller KG, and Pamp SJ. (2018) </b>Specific Gut Microbiome Members are Associated with Distinct Immune Markers in Allogeneic Hematopoietic Stem Cell Transplantation.<b> Journal</b>, <b>Vol</b>., doi:XXX. <br>
DOI: 10.6084/m9.figshare.6508238.v3
title: R scripts documenting the statistical data analyses

description: R scripts documenting the statistical data analyses The provided R scripts contain all code that was used for the main analyses steps of the study, and facilitates the reproduction of results presented in tables and figures. The scripts are numbered and named according to the analyses steps and correspond to the .html reports provided as additional files with the journal article. <br><br> <b>Reference</b> <b>Masche AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM, Müller KG, and Pamp SJ. (2018) </b>Specific Gut Microbiome Members are Associated with Distinct Immune Markers in Allogeneic Hematopoietic Stem Cell Transplantation.<b> Journal</b>, <b>Vol</b>., doi:XXX. <br>
DOI: 10.6084/m9.figshare.6508235
title: Phyloseq objects associated with statistical analyses

description: Phyloseq objects associated with statistical analyses The phyloseq-class experiment-level objects (in the following called: phyloseq objects) were created from the provided data, using code available from the provided R scripts. To facilitate the reproducibility of the individual steps at different stages of the analysis, the provided phyloseq objects can be used as input as specified in the R scripts and do not have to be re-created from the raw data. However, the latter is also possible as all data and R code to create the phyloseq objects are provided.<br> <b>Reference</b> <b>Ingham AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM, Müller KG, and Pamp SJ. (2019) </b>Specific gut microbiome members are associated with distinct immune markers in pediatric allogeneic hematopoietic stem cell transplantation. Microbiome 7:131 https://doi.org/10.1186/s40168-019-0745-z <br>
DOI: 10.6084/m9.figshare.6508232
title: Patient data matrices

description: Patient data matrices The data matrices contain the baseline parameters and clinical variables (i.e., immune markers, immune reconstitution, clinical outcomes) for the cohort of the present study. File “hBD2_with_healthy_controls”: Plasma concentrations of human beta defensin 2 in patients and healthy controls. File “Data_matrix_37_patients”: Data for all 37 patients that were used in the analyses of the current study. File “Data_matrix_37_patients_per_day”: Data for all 37 patients in “daily” format that were used in the analyses of the current study. File “Data_matrix_30_patients”: Data for the subset of 30 patients for which microbiota profiling was performed that were used in the analyses of the current study. File “Data_matrix_30_patients_additional”: Data for the subset of 30 patients for which microbiota profiling was performed including additional patient information. File “Explanation_of_data”: Explanation of abbreviations in data matrices.<br><br> <b>Reference</b> <b>Ingham AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM, Müller KG, and Pamp SJ. (2019) </b>Specific gut microbiome members are associated with distinct immune markers in pediatric allogeneic hematopoietic stem cell transplantation. Microbiome 7:131 https://doi.org/10.1186/s40168-019-0745-z
DOI: 10.6084/m9.figshare.6508205
title: Comprehensive analysis of Long non-coding RNA expression in dorsal root ganglion reveals cell type specificity and dysregulation following nerve injury - Supplemetary Data

description: <b>Comprehensive analysis of Long non-coding RNA expression in dorsal root ganglion reveals cell type specificity and dysregulation following nerve injury - Supplemetary Data</b> <br>S. Data 1: Novel LncRNAs in mouse and rat, in syntenically conserved regions between human, mouse and rat.<br> <br>S. Data 2: Novel LncRNAs in mouse DRG antisense of orthologous genes in mouse and rat. <br>S. Data 3: Novel LncRNAs in rat DRG antisense of orthologous genes in mouse and rat. <br>S. Data 4: Neuron sub-type specificity of ENSEMBL genes and novel LncRNAs. Neuron sub-type index is 1. MHN, 2. MHN (MI, IS), 3. C-LTMR, 4. MHN (IS), 5. MHN (IS), 6. MHN, 7. MHN (NS), 8. MR, 9. MHN, 10. MR. Average log2 expression holds the average expression in the neuron sub-type where the gene or LncRNAs was higher expressed. Expression SEM hold the standard error of the mean for the average expression estimator. <br>S. Data 5: DE analysis results and fpkm values of novel LncRNAs for IPSC-derived sensory neurons vs IPSC. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ cell_line*condition. <br>S. Data 6: Expression data for all antisense LncRNAs in IPSC-derived sensory neurons vs IPSC. <br>S. Data 7: Expression data for all intergenic LncRNAs in IPSC-derived sensory neurons vs IPSC. <br>S. Data 8: Expression data for all LncRNAs antisense of pain genes in IPSC-derived sensory neurons vs IPSC. <br>S. Data 9: Expression data for all intergenic LncRNAs adjacent to pain genes in IPSC-derived sensory neurons vs IPSC. <br>S. Data 10: Novel LncRNAs in human IPSC and mouse, in syntenically conserved regions between human, mouse and rat. <br>S. Data 11: Novel LncRNAs in human IPSC antisense of orthologous genes in human and mouse. <br>S. Data 12: DE analysis results and fpkm values of novel LncRNAs for rat DRG SNT vs Sham. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ condition. <br>S. Data 13: Expression data for all antisense LncRNAs in rat DRG SNT vs Sham. <br>S. Data 14: Expression data for all intergenic LncRNAs rat DRG SNT vs Sham. <br>S. Data 15: DE analysis results and fpkm values of novel LncRNAs for BALB/c and B10.D2 mouse DRG SNI vs Sham. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ sex + strain*condition. <br>S. Data 16: Expression data for all antisense LncRNAs in mouse DRG SNI vs Sham.<br> <br>S. Data 17: Expression data for all intergenic LncRNAs mouse DRG SNI vs Sham.
DOI: 10.6084/m9.figshare.6508205.v1
title: Supplementary Data

description: <b>Comprehensive analysis of Long non-coding RNA expression in dorsal root ganglion reveals cell type specificity and dysregulation following nerve injury - Supplemetary Data</b> <br> <br> S. Data 1: Novel LncRNAs in mouse and rat, in syntenically conserved regions between human, mouse and rat. S. Data 2: Novel LncRNAs in mouse DRG antisense of orthologous genes in mouse and rat. S. Data 3: Novel LncRNAs in rat DRG antisense of orthologous genes in mouse and rat. S. Data 4: Neuron sub-type specificity of ENSEMBL genes and novel LncRNAs. Neuron sub-type index is 1. MHN, 2. MHN (MI, IS), 3. C-LTMR, 4. MHN (IS), 5. MHN (IS), 6. MHN, 7. MHN (NS), 8. MR, 9. MHN, 10. MR. Average log2 expression holds the average expression in the neuron sub-type where the gene or LncRNAs was higher expressed. Expression SEM hold the standard error of the mean for the average expression estimator. S. Data 5: DE analysis results for IPSC-derived sensory neurons vs IPSC. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ cell_line*condition. S. Data 6: Expression data for all antisense LncRNAs in IPSC-derived sensory neurons vs IPSC. S. Data 7: Expression data for all intergenic LncRNAs in IPSC-derived sensory neurons vs IPSC. S. Data 8: Expression data for all LncRNAs antisense of pain genes in IPSC-derived sensory neurons vs IPSC. S. Data 9: Expression data for all intergenic LncRNAs adjacent to pain genes in IPSC-derived sensory neurons vs IPSC. S. Data 10: Novel LncRNAs in human IPSC and mouse, in syntenically conserved regions between human, mouse and rat. S. Data 11: Novel LncRNAs in human IPSC antisense of orthologous genes in human and mouse. S. Data 12: DE analysis results for rat DRG SNT vs Sham. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ condition. S. Data 13: Expression data for all antisense LncRNAs in rat DRG SNT vs Sham. S. Data 14: Expression data for all intergenic LncRNAs rat DRG SNT vs Sham. S. Data 15: DE analysis results for BALB/c mouse DRG SNI vs Sham. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ sex + strain*condition. S. Data 16: DE analysis results for B10.D2 mouse DRG SNI vs Sham. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ sex + strain*condition. S. Data 17: Expression data for all antisense LncRNAs in mouse DRG SNI vs Sham. S. Data 18: Expression data for all intergenic LncRNAs mouse DRG SNI vs Sham. <br>
DOI: 10.6084/m9.figshare.6508205.v2
title: Supplementary Data

description: <b>Comprehensive analysis of Long non-coding RNA expression in dorsal root ganglion reveals cell type specificity and dysregulation following nerve injury - Supplemetary Data</b> <br> <br> S. Data 1: Novel LncRNAs in mouse and rat, in syntenically conserved regions between human, mouse and rat. S. Data 2: Novel LncRNAs in mouse DRG antisense of orthologous genes in mouse and rat. S. Data 3: Novel LncRNAs in rat DRG antisense of orthologous genes in mouse and rat. S. Data 4: Neuron sub-type specificity of ENSEMBL genes and novel LncRNAs. Neuron sub-type index is 1. MHN, 2. MHN (MI, IS), 3. C-LTMR, 4. MHN (IS), 5. MHN (IS), 6. MHN, 7. MHN (NS), 8. MR, 9. MHN, 10. MR. Average log2 expression holds the average expression in the neuron sub-type where the gene or LncRNAs was higher expressed. Expression SEM hold the standard error of the mean for the average expression estimator. S. Data 5: DE analysis results for IPSC-derived sensory neurons vs IPSC. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ cell_line*condition. S. Data 6: Expression data for all antisense LncRNAs in IPSC-derived sensory neurons vs IPSC. S. Data 7: Expression data for all intergenic LncRNAs in IPSC-derived sensory neurons vs IPSC. S. Data 8: Expression data for all LncRNAs antisense of pain genes in IPSC-derived sensory neurons vs IPSC. S. Data 9: Expression data for all intergenic LncRNAs adjacent to pain genes in IPSC-derived sensory neurons vs IPSC. S. Data 10: Novel LncRNAs in human IPSC and mouse, in syntenically conserved regions between human, mouse and rat. S. Data 11: Novel LncRNAs in human IPSC antisense of orthologous genes in human and mouse. S. Data 12: DE analysis results for rat DRG SNT vs Sham. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ condition. S. Data 13: Expression data for all antisense LncRNAs in rat DRG SNT vs Sham. S. Data 14: Expression data for all intergenic LncRNAs rat DRG SNT vs Sham. S. Data 15: DE analysis results for BALB/c mouse DRG SNI vs Sham. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ sex + strain*condition. S. Data 16: DE analysis results for B10.D2 mouse DRG SNI vs Sham. Results are DESeq2 determined for the whole gene set of ENSEMBL genes and novel LncRNAs. Experimental design is ~ sex + strain*condition. S. Data 17: Expression data for all antisense LncRNAs in mouse DRG SNI vs Sham. S. Data 18: Expression data for all intergenic LncRNAs mouse DRG SNI vs Sham. <br>