dataesr/scientific-paragraphs-categorization huggingface dataset:
We identified significant differences in the frequency of CN changes between SMM and MM providing further support with their role as drivers. However, these differences may be explained by the different composition of molecular subgroups in the asymptomatic disease stages. For example, there are fewer t(4;14) patients in the SMM data set compared to MM, and therefore the frequency of CN changes associated with this translocation, such as del(16q), may be impacted by this. Knowledge of the molecular events underlying the genetic complexity of the early truncal stages of MM has largely been extrapolated from the study of NDMM. Here, we have used sequential samples from the early disease stage of SMM to improve the resolution of the definitions of sub-clonal structure. In order to understand the mechanistic basis driving mutational abnormalities at these early truncal stages of the disease, we used the mutation data to identify signatures and studied their relationship over time.
Methods Patients and samples. Eighty-two previously untreated SMM patients according to IMWG 2014 criteria  8  were included in the study from the University of Arkansas for Medical Science after written informed consent. The median followup was 5. 18 years (95% CI 3. 53-6. 59) from diagnosis. An additional 223 previously published  30  newly diagnosed MM patients were used for comparison. Clinical data were collected and checked for consistency. A summary of patients' characteristics in each cohort may be found in Table  1. Ten early MM (EM) patients with new early myeloma criteria only (bone marrow plasma cell ≥60%, SFLC ratio ≥100, or more than one Focal lesion on MRI), and 17 MGUS were used for additional comparison. CD138 + plasma cells were isolated from bone marrow aspirates by magneticactivated cell sorting using the AutoMACS Pro (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany) or RoboSep (STEMCELL Technologies, Vancouver, Canada).
Plasma cell purity was determined by flow cytometry and only samples with >85% purity were used in this study. DNA from peripheral blood or stem cell harvest was used as a matched non-tumor control sample for each patient to exclude germline variants. Nucleic acids were isolated using the AllPrep DNA/RNA or Puregene kits (Qiagen, Hilden, Germany). Data processing, variants calling, filtering, and annotation Targeted panel. Samples were processed as previously published  30. In brief, we performed targeted sequencing on 125 genes and chromosomal regions that had previously been shown to be relevant to the biology, prognosis, and treatment of MM. This included the tiling of the Ig regions and MYC to identify Ig translocations, the V, D, and J rearrangements  27, 31, 32  , and MYC abnormalities.
The panel was divided into a translocation panel and a mutation/copy-number panel to provide high depth coverage for mutation analysis (0. 6 Mb) while providing lower depth sequencing of translocation regions (4. 2 Mb). Each patient had their tumor and control DNA sequenced, to identify somatic mutations, CN changes, and translocations. The median mean coverage of each panel may be seen in Supplemental Table  3. Sequencing data were analyzed as previously described  30. The pipeline is available on Github 32. Whole-exome sequencing. 62 samples (9 controls and 53 samples) underwent custom-enriched exome sequencing. 200 ng of DNA was used to prepare libraries using the HyperPlus kit (Kapa Biosystems) and hybridized to a modified SeqCap EZ MedExome (Roche Nimblegen), which included Ig and MYC regions for translocation detection.
Cross sample contamination was assessed by SNP mismatch analysis. The median coverage was 93× (IQR 68-128) and 100× (IQR 95-103) for tumors and controls, respectively. The files were demultiplexed using bcl2fastq and aligned to the Ensembl hg38 reference genome using BWA mem (v. 0. 7. 12) (variants were called using Strelka (v. 1. 0. 14) and single nucleotide variants (SNVs)) were filtered using fpfilter (https://github. com/ckandoth/variant-filter). A 10% cut-off was used to filter Indels. Annotation was performed using Variant Effect Predictor (v. 85). Sequenza v3. 0. 0 33 was used to detect somatic copy-number aberration and estimate tumor purity and ploidy. Finally, intra-and inter-chromosomal rearrangements were called using Manta (v0. 29. 6) using the default settings and the exome flag specified.
We attempted to reconstruct the clonal population structure of WES samples that had adequate CNA (n = 44) using Pyclone  34. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. All samples with appropriate CNA were used. Data were visualized using the Fish-plot package  35. Ultra low-pass whole-genome sequencing. Sixty-nine SMM and 116 MM samples underwent ultra low-pass WGS. Libraries for tumor DNA and control DNA were prepared as described above using the HyperPlus kit (Kapa Biosystems). Before hybridization to the panels, library DNA was removed and sequenced directly using paired tumor and control libraries, which were pooled for sequencing on the NextSeq500 using 75-bp single-end reads.
Gene expression scores. Total RNA from plasma cells was used for gene expression profiling (GEP) using U133 Plus 2. 0 microarrays (Affymetrix). CEL files were normalized using GCRMA  36  for the application of the updated TC algorithm. MAS5 normalization was also performed when necessary, e. g. , for calculation of GEP4 and NF-κB scores. All expression data were normalized using R Bioconductor and transformed to the UAMS TT2 and TT3 NDMM standard according to a variant of M-ComBat  37  Droplet Digital PCR (ddPCR) Primers. Primers were used to amplify the IGHG3-MYC translocation and the nontranslocated IGH locus. Primers and probes may be found in Supplemental Table  5. ddPCR reactions. 25 µL reaction mixtures were prepared containing primers (40×, ThermoFisher Scientific), 10 ng template, and ddPCR™ Supermix (2×, Bio-Rad).
Acknowledgements We thank all the patients and their families for their contributions to this study. We acknowledge the continued support from the  Multiple Myeloma Research Foundation  and the  Perelman Family Foundation. BAW and GJM received grant support through a  Translational Research Program  award from the  Leukemia & Lymphoma Society  ( 6020-20 ). E. M. B. received funding from the  Fédération Françaisssssse de Recherche sur le Myélome et les Gammapathies  under the aegis of the  Fondation de France. We would like to acknowledge  Philip Farmer  and  Michael Rutherford  for assembling the database at  UAMS.
Using information from a U. S. electronic health records database, the real-world LIGHT-NING study  [36]  predicted the rate of severe hypoglycaemia with Gla-300 across various patient subgroups with high hypoglycaemia risk, including both insulin-naı ¨ve patients aged C 65 (N = 20885) and C 75 years (N = 10325) and patients switching from another basal insulin analogue aged C 65 (N = 15837) and C 75 years (N = 5654). In all subgroup analyses, Gla-300 was associated with lower rates of severe hypoglycaemia compared to first-generation basal insulin analogues, such as Gla-100 and insulin detemir, irrespective of prior insulin therapy status  [36]. Similarly, another post-hoc analysis, investigating the association of baseline patient characteristics with key outcomes reported from the EDITION 1, 2 and 3 trials, found that the comparable glycaemic control of Gla-300 versus Gla-100 with less hypoglycaemia seen in the EDITION studies was observed, irrespective of age, body mass index, age at T2DM onset or duration of T2DM  [37].
It has also been speculated that older patients may have a different pathophysiological form of T2DM than younger ones, as found in a data-driven cluster analysis conducted among 8980 adults with newly diagnosed diabetes in which four subgroups of T2DM were identified with significantly different patient characteristics and risk of diabetic complications  [42]. One of these subgroups was labelled as mild age-related diabetes; patients in this cluster are older, with modest metabolic derangements and a lower HbA1c at diagnosis compared to patients in other clusters, such as severe autoimmune diabetes and severe insulindeficient diabetes  [42]. At baseline, more than two-thirds of the patients had been previously treated with at least one non-insulin anti-hyperglycaemic treatment. Interestingly, in a post-hoc analysis [43] of patient-level data from the EDITION 3 RCT and de-identified data from the Clinformatics real-world claims database, Gla-300 therapy initiated in insulin-naı ¨ve patients with T2DM uncontrolled on oral antidiabetic drugs (OADs) was associated with reductions in prior OAD therapy without compromising glycaemic control, while preserving the hypoglycaemic benefit of Gla-300 versus Gla-100  [43].
The ADA  [4]  currently recommends simplification of treatment regimens in older patients with T2DM to reduce the risk of hypoglycaemia and polypharmacy. Thus, since the post-hoc analysis [43] of data from EDITION 3 and from the Clinformatics real-world database suggests that patients treated with Gla-300 could step down OAD use without jeopardising glycaemic control and with a reduced hypoglycaemia risk, these findings, in line with those of REALI, could have important ramifications for clinical decision-making in older T2DM populations regarding regimen simplification. Limitations of the REALI pooled analysis include the unbalanced and uncontrolled number of patients across the different age subgroups and the lack of assessments of cognitive function, functional capacity or frailty. REALI is also a post-hoc analysis, rather than a dedicated prospective trial in older individuals with T2DM.
CONCLUSION Gla-300 was found to be effective with a good safety profile in both younger and older patients with uncontrolled T2DM, indicating that it may be a suitable therapeutic option in elderly patients who represent a vulnerable population that is prone to hypoglycaemia. Compliance with Ethics Guidelines. This analysis did not involve primary data collection by the authors, and all post-hoc analysed data were anonymised; consequently, separate ethical approval was not required. Protocols for all included studies were approved by the appropriate ethics committees, and the studies were conducted according to Good Clinical Practice and the Declaration of Helsinki. Data Availability. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
0) 112 (5. 5) 211 (6. 9) 154 (8. 3) 12 (2. 7) HbA1c (%) 9. 10 ± 1. 37 8. 90 ± 1. 33 8. 80 ± 2. 08 8. 49 ± 1. 05 8. 46 ± 0. 94 FPG (mg/dL) 185. 5 ± 59. 8 183. 5 ± 55. 6 182. 1 ± 54. 4 177. 6 ± 51. 7 184. 0 ± 56. 1 Data are expressed as the mean ± SD or the median with first and third quartiles (Q1-Q3) in parentheses, unless otherwise indicated N refers to all patients from the pooled REALI database included in the age subgroup mentioned; means and percentages are calculated based on data available for each variable CV cardiovascular, eGFR estimated glomerular filtration rate, GLP-1 glucagon-like peptide-1, NPH neutral protamine Hagedorn, SGLT-2 sodium glucose co-transporter-2 a.
Search strategy We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  [22]  statement to design and report our meta-research, where applicable (supplementary methods 1). A systematic literature search was performed in Pubmed and Cochrane COVID-19 study registry for peerreviewed medical articles. Additional search using bioRxiv, medRxiv, and arXiv platforms was performed to include preprints for additional analysis. The literature search was performed between 1 November 2019 and 1 May 2020. "COVID-19", "SARS-CoV-2" and their synonyms were used for the searches. The detailed search strategy for each database is provided in supplementary methods 2. The protocol of the study is available at https://osf. io/5zjyx/.
Finally, all excluded references were re-checked by two reviewers (MR, AL). The following data from each included article were extracted: (1) study basic information: journal, title, publication date, family name and country of first author, (2) categorization index: type of publication and topics. For the original research articles that underwent quality assessment, the following information were recorded: number of patients, primary and secondary outcome, patient consent and 151 total items related to the quality assessment tools (see dedicated chapter in the methods section). Endnote (Endnote X9, Thomson Reuters, Philadelphia, PA, USA), Excel (Excel 2019, Microsoft, Redmond, WA, USA) and NoteExpress (Version 3. 2, Beijing Aegean Software Co. , Ltd. , Beijing, China) software were used for the screening, categorization, and appraisal of medical articles.
Dynamics of COVID-19 publications We aimed at investigating the dynamics of COVID-19 publications. To do so, we recorded the day of publication for each publication, and used the smooth function from stats R package, to represent the cumulative numbers of medical articles according to the time of publication. The number of COVID-19 confirmed cases worldwide was extracted from the publicly available database of the University of John Hopkins: COVID-19 Map -Johns Hopkins Coronavirus Resource Center  [23].
Categorization of publications We aimed at categorizing the included medical articles by type of publications and related topics. Six types of publication were pre-specified following definitions of BMC Medical Research Methodology: original article, research letter, review, systematic review, case reports or case series, and publication without original data (gathering viewpoint, editorial, perspective, expert opinions). To categorize the topics of all included articles, an original list of topics was developed before the review started (supplementary Table  1 ). Each study was categorized in up to three topics.
In peer-reviewed medical studies, publications without original data dominated the exponential growth of COVID-19 literature with 261. 9 ± 61. 1 articles published every week (Fig. 2b ), followed by original articles, case reports, reviews, research letters and systematic reviews with 69. 3 ± 22. 3, 43. 2 ± 9. 0, 39. 9 ± 11. 9, 49. 3 ± 9. 9, and 3. 0 ± 2. 5 articles respectively published every week (Fig. 2b ). In preprints, original articles represented 2699 (88. 5%) of articles, reviews 74 (2. 4%), publications without original data 59 (1. 9%), case reports 33 (1. 1%), research letters 126 (4. 1%) and systematic reviews 57 (1. 9%) (supplementary Figures  1C  and 2 ). We then assessed the distribution of countries among all COVID-19 publications. The top five COVID-19 publishing countries were China with 2717 (25.
Critical appraisal of original articles We assessed the quality of research of the 713 clinical, peerreviewed original articles (i. e. excluding preprints), comprising observational and interventional studies. We used a total of 9 validated tools totaling 151 items to address all types of study. The datasets corresponding to these analyses can be downloaded at https://www. paristransplantgroup. org/covid-19-related-medical-research. html. The detail of the assessment tools used for each study type is presented in supplementary method 3. Basic characteristics of these studies are presented in Table  1. The detailed assessment of study quality according to study type is shown in supplementary Figures  6, 7 , 8 and 9. Among original articles, cross-sectional studies (N = 306, 42. 9%) and simulation-based studies (N = 185, 25.
A linear envelope scaling can be obtained by mixing data with diverse values of σ in different fault environments, which can explain the observed linear scaling based on a global compilation of slow earthquakes  32, 33. In addition, another theoretical relation, T ∝ 1/V c (Methods A7), predicts that as the critical slip rate increases the cubic scaling of SSEs can be shifted vertically toward that of earthquakes, which can reconcile the separation between the cubic scaling of SSEs and earthquakes. To explore a universal scaling relation in the global dataset that is consistent with fracture mechanics theory, I calculate the rupture speed and peak slip rate of the SSEs and tsunami earthquakes observed globally  36, [44] [45] [46] 59. The  upture speed is estimated by v r = L/T , with an uncertainty of a factor of 2 for bilateral ruptures.
I use the boundary element software QDYN  75  for the quasi-dynamic SSE simulation, where the fault is infinitely long and the fault slip is horizontally periodic with a prescribed length, 11W. To avoid the interaction of the periodic fault segments, a buffing segment of length 5. 5W is set, where the frictional behaviour is rate-strengthening with a > b. Sufficient numerical resolution is guaranteed by setting a small grid size (∆x), that is, L c /∆x = 8. The simulated time is set long enough to capture the whole rupture propagation. For each single-rupture model, the rupture time on each node of faults is determined by a criterion of slip rate, 10V i , and the rupture speed is computed based on the along-strike gradient of the rupture time. A2. Fully dynamic earthquake rupture simulations The 2.
Objective: The International Cooperative Growth Study, NutropinAq ® European Registry (iNCGS) (NCT00455728) monitored long-term safety and effectiveness of recombinant human growth hormone (rhGH; NutropinAq ® [somatropin]) in paediatric growth disorders. Methods: Open-label, non-interventional, post-marketing surveillance study recruiting children with growth disorders. Endpoints included gain in height standard deviation score (SDS), adult height, and occurrence of adverse events (AEs). Results: 2792 patients were enrolled. 2082 patients (74. 6%) had growth hormone deficiency (GHD), which was isolated idiopathic in 1825 patients (87. 7%). Non-GHD diagnoses included Turner syndrome (TS) (n=199), chronic renal insufficiency (CRI) (n=10), other non-GHD (n=498), and missing data for three participants.
Trial Design The iNCGS was an international, multicentre, open-label, noninterventional post-marketing surveillance study (ClinicalTrials. gov NCT00455728) that was initiated in 2005 and conducted at centres in seven European countries (France, Germany, Austria, Italy, Spain, UK and Romania). Data were collected until December 2016. The primary objective of the study was to collect long-term safety information on rhGH during treatment of paediatric growth disorders.
Assessments and Outcomes Data could be collected until the patients reached adult height, i. e. , epiphyseal closure. During the enrolment visit, the Investigator checked patient eligibility, obtained the signed Informed Consent Forms, and recorded demographic information and family medical history, disease history and aetiology. Treatment with rhGH was initiated at the baseline visit, with the exception of patients who had already begun treatment before enrolment. At the baseline visit, relevant medical information and history, treatment dose, schedule of rhGH and adverse events (AEs) were collected. Patient assessments included height, weight, body mass index (BMI), pubertal status, concomitant medications, vital signs, laboratory measurements and left hand/wrist X-ray for bone age determination.
Investigators were asked to report non-serious related AEs and all serious AEs (regardless of relationship to study treatment). Other endpoints included change from baseline in body mass index SDS and IGF-1 concentrations, pubertal status and laboratory test results and/ or vital signs data. The primary outcome measure was the number of patients with AEs. The reference growth charts used were those used as standard in the participating countries: Sempe  (22)  and Programme National Nutrition Santé(PNNS) reference data in France, Spain, Italy and Romania; reference data for the UK  (23) ; and KiGGS  (24)  and Kromeyer  (25)  reference data for Germany and Austria. Treatment duration for each patient was according to the Investigator's judgement based on the clinical needs of the individual patient and their responsiveness.
Statistics The Enrolled population included all patients who were fully informed about the study, who gave written informed consent to participate and with at least one dataset collected in the registry. The Treated population included all enrolled patients who received at least one rhGH injection. The Registry population included all enrolled patients who completed at least one follow-up visit and who received at least one rhGH injection. The Completed population included all patients of the Registry population who attained adult height (with closed epiphyses/ reached adult height as reason for end of registry) or had attained near-adult height (defined as height velocity <1 cm/year at last follow-up and after at least 12 months of treatment at registry closure). The Safety population included all patients who received at least one rhGH injection and with at least one follow-up visit or follow-up safety data.
Statistical analysis was performed using Statistical Analysis System (SAS ® ) Version 9. 4 (SAS Institute Incorporated, Cary, NC, USA). Linear regression analyses were performed to identify predictive factors for change from baseline in height SDS and height velocity at Month 12, Month 24, Month 36 or Month 48 as well as final height SDS in treatment-naïve patients in the Registry population. For descriptive variables, the mean, SD, two-sided 95% CI of the mean, the median, quartiles Q1 and Q3, and the minimum and maximum were recorded. For qualitative variables, the frequency percentage of each modality and the two-sided 95% CI were calculated. Covariates were aetiology, pubertal status, biological mother's height (cm), biological father's height (cm), birth length (cm), target height (cm), height (SDS) and weight (SDS) at baseline, starting dose (mg/kg/day), and age at first rhGH intake (years).
Mean dose (mg/kg/day) and dose adaptation (defined as a change of at least 10% of the dose) during the first and subsequent years were additional covariates at Months 12-48. The threshold for the univariate analyses was p<0. 2; the definition of significance for the multivariate analyses was p<0. 05. Complementary additional analyses were conducted as follows: height SDS at Month 12 for five peak plasma GH levels at baseline were compared by ANOVA, with a 5% level of significance. Data for breakdown by countries (up to the cutoff date of December 2014) were analysed descriptively on baseline characteristics, dose and duration of treatment.
Furthermore, weight and BMI SDS were collected at baseline but data are not available for these values at end of therapy, which could have provided useful information on the theoretical possibility of a long-term metabolic impact of rhGH treatment. Other protocol-defined endpoints and assessments could be debatedfor example, the adult height was compared with predicted adult height but not target height, which may be more important, the final height SDS could have been corrected for mid-parental height, and in the linear regression analysis BMI may be a more relevant covariate than weight SDS. The diagnosis and decision to prescribe rhGH was at the discretion of the treating physician and independent of inclusion in this study, and therefore, diagnostic methodology may not have been applied consistently.
ACKNOWLEDGMENTS The authors thank all patients involved in the study, as well as their caregivers, care team, investigators and research staff in participating institutions. The authors thank  Martin Gilmour, PhD , of  ESP Bioscience Ltd, Crowthorne, UK  for providing medical writing support, which was sponsored by  Ipsen , in accordance with Good Publication Practice guidelines. The authors declare that this study was funded by  Ipsen. Ipsen was involved in study design, data collection and analysis, and preparation and review of the manuscript. The authors take full responsibility for the entire content of this submitted manuscript and approved submission.