cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11847636.v1
title: HBCx_22_TamR_scChIPseq_mm10.bam.bai

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847366
title: HBCx_95_scChIPseq_mm10.bam.bai

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847369
title: Metadata supporting data files in the published article: The acute effects of adjuvant radiation and chemotherapy on peripheral blood epigenetic age in early stage breast cancer patients

description: In this study, the authors examined epigenetic changes in peripheral whole blood cells in early stage breast cancer (BC) patients undergoing surgery followed by adjuvant radiotherapy, or surgery followed by adjuvant chemotherapy and radiotherapy. <b>Data access: </b>The dataset RISEdataTab1.xlsx supporting Table 1 of the published article is publicly available in the figshare repository as part of this data record. Methylation data supporting Figure 1 and Supplementary Figure 2 of the published article are publicly available in the <i>Gene Expression Omnibus </i>repository, via accession https://identifiers.org/geo:GSE140038. The filenames and types are as follows: NormalizedBetaNoob.csv, GSM4151814-GSM4151957 (in idat format), and Data Dictionary.docx.<br> <b>Study approval and patient consent</b>:This study was approved by the UCLA Medical Institutional Review Board. Informed consent was obtained from all participants. <b>Study aims and methodology</b>:The aim of this study was to examine the acute effects of adjuvant radiation and chemotherapy on peripheral blood epigenetic age in early stage breast cancer patients. Women were eligible for the study if they had been recently diagnosed with Stage 0-IIIA BC and had not yet started adjuvant or neoadjuvant therapy with radiation, chemotherapy, or endocrine therapy. Assessments were conducted before onset of adjuvant therapy, after completion of radiation and/or chemotherapy, and over an 18-month follow-up. The current analysis focuses on a subset of women (n=72) who had blood samples available for epigenetic analyses at baseline and post-treatment. The women selected were treated with radiation alone (n=37), and with chemotherapy followed by radiation (n=35), to evaluate individual and combined effects of those treatment exposures. All women had completed surgery prior to the baseline assessment. Four measures of epigenetic age acceleration were examined: intrinsic (IEAA), extrinsic (EEAA), phenotypic (PEAA), and Grim (GEAA), based on weighted averages of methylation levels at 353, 71, 513, and 1,030 CpGs, respectively, with adjustment for chronologic age. Details of the epigenetic clock, DNA extraction/methylation experiments, and statistical analyses are provided in the supplementary methods.
DOI: 10.6084/m9.figshare.11847363
title: HBCx_22_scChIPseq_mm10.bam.bai

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847366.v1
title: HBCx_95_scChIPseq_mm10.bam.bai

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847369.v1
title: Metadata supporting data files in the published article: The acute effects of adjuvant radiation and chemotherapy on peripheral blood epigenetic age in early stage breast cancer patients

description: In this study, the authors examined epigenetic changes in peripheral whole blood cells in early stage breast cancer (BC) patients undergoing surgery followed by adjuvant radiotherapy, or surgery followed by adjuvant chemotherapy and radiotherapy. <b>Data access: </b>The dataset RISEdataTab1.xlsx supporting Table 1 of the published article is publicly available in the figshare repository as part of this data record. Methylation data supporting Figure 1 and Supplementary Figure 2 of the published article are publicly available in the <i>Gene Expression Omnibus </i>repository, via accession https://identifiers.org/geo:GSE140038. The filenames and types are as follows: NormalizedBetaNoob.csv, GSM4151814-GSM4151957 (in idat format), and Data Dictionary.docx.<br> <b>Study approval and patient consent</b>:This study was approved by the UCLA Medical Institutional Review Board. Informed consent was obtained from all participants. <b>Study aims and methodology</b>:The aim of this study was to examine the acute effects of adjuvant radiation and chemotherapy on peripheral blood epigenetic age in early stage breast cancer patients. Women were eligible for the study if they had been recently diagnosed with Stage 0-IIIA BC and had not yet started adjuvant or neoadjuvant therapy with radiation, chemotherapy, or endocrine therapy. Assessments were conducted before onset of adjuvant therapy, after completion of radiation and/or chemotherapy, and over an 18-month follow-up. The current analysis focuses on a subset of women (n=72) who had blood samples available for epigenetic analyses at baseline and post-treatment. The women selected were treated with radiation alone (n=37), and with chemotherapy followed by radiation (n=35), to evaluate individual and combined effects of those treatment exposures. All women had completed surgery prior to the baseline assessment. Four measures of epigenetic age acceleration were examined: intrinsic (IEAA), extrinsic (EEAA), phenotypic (PEAA), and Grim (GEAA), based on weighted averages of methylation levels at 353, 71, 513, and 1,030 CpGs, respectively, with adjustment for chronologic age. Details of the epigenetic clock, DNA extraction/methylation experiments, and statistical analyses are provided in the supplementary methods.
DOI: 10.6084/m9.figshare.11847357
title: HBCx_95_CapaR_scChIPseq_mm10.bam

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847363.v1
title: HBCx_22_scChIPseq_mm10.bam.bai

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847354
title: HBCx_95_scChIPseq_mm10.bam

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847357.v1
title: HBCx_95_CapaR_scChIPseq_mm10.bam

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847360
title: HBCx_22_TamR_scChIPseq_mm10.bam

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847354.v1
title: HBCx_95_scChIPseq_mm10.bam

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847360.v1
title: HBCx_22_TamR_scChIPseq_mm10.bam

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847351
title: HBCx_22_scChIPseq_mm10.bam

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847351.v1
title: HBCx_22_scChIPseq_mm10.bam

description: The samples correspond to the mouse cells from patient-derived xenograft (PDX) originating from two different human donors. The HBCx-22 and HBCx-22-TamR datasets correspond to mouse cells from a pair of luminal ER<sup>+</sup> breast PDXs: HBCx-22, responsive to Tamoxifen and HBCx-22-TamR, resistant to Tamoxifen. The HBCx-95 and HBCx-95-CapaR correspond to a triple-negative breast cancer (TNBC) tumor model of acquired resistance to chemotherapy. Row names of the matrices are 50,000-bp binned regions; Column names are barcodes corresponding to single cell; Cells contains raw counts, in mm10 reference. The BAM file were produced by a in-house pipeline, available at https://github.com/vallotlab/scChIPseq_DataEngineering
DOI: 10.6084/m9.figshare.11847195
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847195.v1
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847177
title: Identified targets of fungal milRNAs

description: All psRNATarget identified fungal and hop targets of validated novel milRNAs of the <i>Verticillium nonalfalfae</i> fungi.
DOI: 10.6084/m9.figshare.11847195.v2
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847195.v3
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847195.v4
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847195.v5
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847195.v6
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847195.v7
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847195.v9
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847123.v1
title: Use of oral antidiabetic drugs in Japanese working-age patients with type 2 diabetes mellitus: dosing pattern for metformin initiators

description: <b>Objective:</b> To determine the pattern of antidiabetic drug use, with a particular focus on the metformin dose, among patients with type 2 diabetes mellitus (T2DM) in a Japanese working population. <b>Methods:</b> We used an administrative claims database linked to yearly health check-up data from large corporations. Data were collected for T2DM patients who began using an antidiabetic drug between 2014 and 2017 (<i>n</i> = 20,401). We evaluated the type of antidiabetic drug used and the characteristics of the patients using each type of drug. Among the metformin users, we assessed the titration in its dose or treatment during the 12 month period after initiation at 3 month intervals. <b>Results:</b> Among 20,401 new antidiabetic users, the most frequently used agents during the study period were dipeptidyl peptidase-4 inhibitors (DPP4is; 47.4%), followed by biguanides (18.5%) and sodium glucose cotransporter-2 inhibitors (SGLT2is; 6.7%). Most patients who initiated with metformin were prescribed 500 mg or less daily (72.9%); only 2.0% were prescribed a daily dose of &gt;1000 mg. Moreover, 27% remained on the same daily dose during the 1 year follow-up, whereas another 29.9% discontinued their antidiabetic treatment altogether. <b>Conclusions:</b> A unique pattern of prescription was observed amongst Japanese patients with T2DM, and DPP4is, rather than metformin, were predominantly used as the first-line treatment. SGLT2is were infrequently prescribed. Metformin was prescribed at a daily dose of ≤500 mg in many patients. Greater efforts are needed to assess the comparative effectiveness of these treatment strategies.
DOI: 10.6084/m9.figshare.11847195.v8
title: Mirror, Ephydatia muelleri Supplementary Files, Kenny et al. 2020,

description: Mirror of supplementary files and assembly from Kenny et al 2020. More details from https://spaces.facsci.ualberta.ca/ephybase/<br><br>
DOI: 10.6084/m9.figshare.11847102.v1
title: Changes in cardiac output with hemodialysis relate to net volume balance and to inferior vena cava ultrasound collapsibility in critically ill patients

description: Cardiac output may increase after volume administration with relative intravascular volume depletion, or after ultrafiltration (UF) with relative intravascular volume overload. Assessing relative intravascular volume using respiratory/ventilatory changes in inferior vena cava (IVC) diameters may guide volume management to optimize cardiac output in critically ill patients requiring hemodialysis (HD) and/or UF. We retrospectively studied 22 critically ill patients having relative intravascular volume assessed by IVC Collapsibility Index (IVC CI) = (IVCmax-IVCmin)/IVCmax*100%, within 24 h of cardiac output measurement, during 37 intermittent and 21 continuous HD encounters. Cardiac output increase &gt;10% was considered significant. Net volume changes between cardiac outputs were estimated from “isonatremic volume equivalent” (0.9% saline) gains and losses. Cardiac output increased &gt;10% in 15 of 42 encounters with IVC CI &lt;20% after net volume removal, and in 1 of 16 encounters with IVC CI ≥20% after net volume administration (<i>p</i> = 0.0136). All intermittent and continuous HD encounters resulted in intradialytic hypotension. Net volume changes between cardiac output measurements were significantly less (median +1.0 mL/kg) with intractable hypotension or vasopressor initiation, and net volume removal was larger (median −22.9 mL/kg) with less severe intradialytic hypotension (<i>p</i> 10% more frequently with least severe intradialytic hypotension and decreased with most severe intradialytic hypotension (<i>p</i> = 0.047). In summary, cardiac output may increase with net volume removal by ultrafiltration in some critically ill patients with relative intravascular volume overload assessed by IVC collapsibility. Severe intradialytic hypotension may limit volume removal with ultrafiltration, rather than larger volume removal causing severe intradialytic hypotension.
DOI: 10.6084/m9.figshare.11847081
title: Varenicline: mode of action, efficacy, safety and accumulated experience salient for clinical populations

description: <b>Objective:</b> Varenicline, a selective partial agonist of the α4β2 nicotinic acetylcholine receptor, is a smoking cessation pharmacotherapy that more than doubles the chance of quitting smoking at 6 months compared with placebo. This article reviews salient knowledge of the discovery, pharmacological characteristics, and the efficacy and safety of varenicline in general and in specific populations of smokers and provides recommendations to support use in clinical practice. <b>Methods</b>: Literature searches for varenicline were conducted using PubMed, with date limitations of 2000–2018 inclusive, using search terms covering the discovery, mechanism of action, pharmacokinetics, efficacy and safety in different populations of smokers, alternative quit approaches and combination therapy. Selection of safety and efficacy data was limited to clinical trials, meta-analyses and observational studies. <b>Results:</b> Standard administration of varenicline is efficacious in helping smokers to quit, including smokers with cardiovascular disease and chronic obstructive pulmonary disease. Furthermore, varenicline efficacy may be improved with pre-loading, a gradual quitting approach for smokers unwilling or unable to quit abruptly, and extended treatment in smokers who have recently quit to help maintain abstinence. Initial concerns regarding the association of varenicline with increased risk of neuropsychiatric and cardiovascular adverse events have been disproven after extensive clinical evaluations, and the benefit-risk profile of varenicline is considered favorable. <b>Conclusions:</b> Varenicline is efficacious and safe for all adult smokers with a range of clinical characteristics. Evidence suggests that approaches offering greater flexibility in timing and duration of treatment may further extend treatment efficacy and clinical reach.