dataesr/scientific-paragraphs-categorization huggingface dataset:
The IMPs will be stored in a secure place with restricted access and temperature monitoring. The IMP delivery (shipping), return, and disposal will be performed in accordance with the specification document (available on request). Controls will be implemented at the trial site to ensure documented compliance with these requirements. As tapentadol is one of the IMPs in this study, all sites need to be licensed according to local law for the receipt, storage, handling, and administration of narcotics. General unblinding will only take place after the trial has been completed and the database is locked. Except for single-subject unblinding in emergency situations, investigator unblinding will only be carried out after database lock. Unblinding will be performed by the statistician.
Collection of pharmacodynamic data All participating centers have the equipment and expertise required for acquisition of the pharmacodynamic data. Signal processing of the raw PD data to extract the parameters of interest will be done centrally at UCLouvain using the Letswave 6 Matlab toolbox for the analysis of EEG data (http://letswave. org; UCLouvain). EEG/ERP-derived pharmacodynamic data The EEG will be recorded continuously during each biomarker assessment time-point, using a multichannel EEG amplifier and digitizer as detailed in a separate operational manual. Setting-up the EEG recording system will require 10-20 min. Actual collection of the pharmacodynamic data will require approximately 30 min. During the entire assessment, subjects will be seated in a comfortable chair. The assessment consists in the serial acquisition of (1) ongoing EEG at rest, (2) PEPs elicited by stimulation of the left forearm, (3) LEPs elicited by stimulation of the left forearm, (4) auditory-evoked potentials (AEPs), (5) LEPs elicited by stimulation of the right forearm, and (6) PEPs elicited by stimulation of the right forearm.
Dedicated users (e. g. , the investigator, designated persons at the trial site, authorized sponsor representatives, and from other parties involved, e. g. , data management) will be trained and receive access rights according to their role in the trial. All users will have access to the system and be able to review their data on an ongoing basis. After completion of the subject's CRF, the CRF will be signed electronically by the investigator. With the investigators signature, it is confirmed that the data in the CRF are checked, complete, accurate, and in alignment with the source data. Changes to the CRF after initial signing by the investigator will need re-signing. With database lock, the edit rights to the CRFs will be removed, but the investigator will retain access to view the CRFs.
Pharmacodynamic data Copies of the raw pharmacodynamic source data will be electronically transferred to UCLouvain. The data will be stored on secured servers, which have backup onto a second server located in a physically distinct room, also using a secured storage protocol. Signal processing of the raw data to extract the parameters of interest will also be done centrally at UCLouvain using the Letswave 6 matlab toolbox for the analysis of EEG data (http:// letswave. org; UCLouvain; hosted on the Github repository). All primary, secondary, and exploratory EEG biomarker endpoints derived from these analyses, as well as the non-EEG biomarker endpoints will be transferred to the relational database hosted at the Heidelberg University computing center. The obtained endpoint parameters will be entered by UCLouvain into a relational database located at the Heidelberg University computing center.
Bioanalytical laboratory unblinding The bioanalytical laboratory will be unblinded in order to analyze multiple time-points from the specific active treatments and single time-points from corresponding placebos. The randomization list will be sent to the Bioanalysis and ADME Development Department at ESTEVE PHARMACEUTICALS, S. A. (ESTEVE) in order to analyze plasma drug levels of the active treatments (tapentadol, pregabalin, and lacosamide; multiple time-points) and placebo (single time-point). These analyses will be conducted when the clinical study is finished but the dataset is not yet locked. Therefore, the bioanalytical laboratory will recode the subject's numbers to generate blinded results so that the investigator and the sponsor can review the results and remain blinded.
Analysis sets The "all enrolled set" includes all subjects enrolled into the study who have provided at least some data. The "full analysis set" consists of the subjects in the all enrolled set that have been randomized. The "safety set" includes all subjects of the full analysis set that have received at least one dose of any study medication. Further analysis populations (e. g. , per protocol set) may be defined in the statistical analysis plan. Unless otherwise specified in the respective sections below the following populations will be used for the analyses. Demographic and baseline characteristics analyses will be based on the all enrolled set and, optionally, the full analysis set or safety set. Efficacy data (primary and secondary endpoints) will be analyzed based on the full analysis set using the intent-to-treat principle, i.
Pharmacometric analyses The data collected in this trial together with the data from the other three IMI2-PainCare-BioPain-RCTs and the preclinical studies of the BioPain project will be subject to pharmacometric analyses with the intention to validate biomarkers that can translate from preclinical to clinical readouts. As both drug concentrations and biomarker responses are measured at several time-points post-drug administration, the relationship between drug levels and select biomarkers will be explored and modeled. The pharmacometric analysis will be described in a separate pharmacometric analysis plan and will consist briefly in developing population pharmacokinetic/pharmacokinetic (PK/PD) models estimating the primary PK parameters (i. e. , apparent volume of distribution, and total plasma clearance), the primary PD parameters (i.
Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c} The analysis method of the mixed effect model already takes missing data into account and missing data issues are also partly addressed by the sensitivity analyses. More thorough and complete descriptions of missing data handling will be provided in the statistical analysis plan. Plans to give access to the full protocol, participant leveldata, and statistical code {31c} This will be determined at the end of the project period of IMI-PainCare.
Composition of the data monitoring committee, its role and reporting structure {21a} A data monitoring committee is not foreseen. Data management will be performed by the Interdisciplinary Center for Scientific Computing at University Heidelberg. Documentation of the responsibilities and delegation thereof will be maintained in the trial master file. All aspects of the data management process (including data validation and query management, medical coding, ECG data, biomarker data) and data lock procedures are described in the data management plan.
Reporting will be done electronically on the SUSAR's form for the reporting for non-commercial researchers to the CA, and the same form can be printed and sent out in PDF format to the ethical committee. The guidelines from the CA for reporting of adverse reactions in clinical trials will be followed. During the entire trial duration, the sponsor will provide all information needed to the local ethics committees and competent agencies (Belgian Federal Agency for Medicines and Health products FAMHP). Once a year during the entire trial period, the sponsor will prepare a list of all suspected SAEs, which have occurred during the trial period, and a report on the trial subjects' safety. List and report will be submitted to the CA and the IEC. All adverse events shall be reported at the end of the trial in the final registration of trial results in EudraCT and in the report to the ethical committee if requested.
Dissemination plans {31a} A final report integrating trial results will be prepared. The Principal Investigator will provide the competent authority/ies and relevant IEC(s) with a summary of the trial results in accordance with applicable regulatory requirements. The results of this trial will be publicly disclosed (EudraCT). The results (or parts thereof) of this trial will be published according to the Grant Agreement and Consortium Agreement of IMI-PainCare (grant No 777500).
Discussion IMI2-PainCare-BioPain-RCT3 is one of four RCTs aiming at validating biomarkers of drug effects on nociceptive processing using the same trial design and IMPs. In addition to the primary efficacy analysis, we will conduct exploratory data analyses aimed to elucidate the inter-relationships between all endpoints investigated in this trial and the other trials. If there is evidence for a relationship among the different biomarkers, in part induced by a common underlying mechanism, we will seek to establish a latent variable measurement model aimed to quantify the underlying mechanism. This latent variable might be interpreted as a summary score aggregating the information of multiple markers. In more detail, the hierarchical modeling will aim to: -Elucidate interdependence pattern among potentially correlated markers.
Acknowledgements We would like to thank  Nicolas Marco Arino  ( University of Navarra ),  Suktae Choi  ( TEVA Pharmaceutical Industries ),  Francesca Fardo  ( Aarhus University ),  Marcel Froehlich  ( Grünenthal GmBH ),  Angelika Häfner  ( Grünenthgal GmBH ),  Kimberley Jackson  ( Eli Lilly and Company ),  Feras Khalil  ( Grünenthal GmBH ),  Caterina Leone  ( Sapienza Univesity ),  Louisien Lebrun  ( UCLouvain ),  Robert Nemeth  ( Grünenthal GmBH ),  Zahra Nochi  ( Aarhus University ),  Richard Rogers  ( University of Oxford ),  Erik Schnetter  ( University of Heidelberg ), and  Sara Shnider  ( TEVA Pharmaceutical Industries ) for their contributions to the study protocol. Finally, thank  Jane Meijlink  ( International Painful Bladder Foundation ) for providing feedback on the manuscript.
Protocol version  {3} 3. 0 (15/05/2019 ) Funding  {4}  This project has received funding from the  Innovative Medicines Initiative 2 Joint undertaking  under grant agreement No  777500. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation program  and  EFPIA. by  Rolf-Detlef Treede  ( Heidelberg University ). Design of the study was led by the sponsor and coordinator, and involved all partners of the BioPain subtopic of the  IMI2-PainCare consortium  ( WP5 ,  WP6 ,  WP7 ). The sponsor will coordinate data collection at all sites and extract all biomarker parameters from the source data. Final analysis of study endpoints will be coordinated by  Rolf-Detlef Treede  and involve all partners of the BioPain subtopic of the  IMI2-PainCare consortium.
Funding {4} This project has received funding from the  Innovative Medicines Initiative 2 Joint undertaking  under grant agreement No  777500. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation program  and EFPIA. Disclaimer: The statements and opinions presented here reflect the author's view and neither IMI nor the  European Union ,  EFPIA , or any Associated Partners are responsible for any use that may be made of the information contained therein. www. imi-paincare. eu;www. imi. europa. eu. The funding body ( Innovative Medicines Initiative , including partners from the  European Federation of Pharmaceutical Industries and Associations [EFPI A])  defined the general objectives of the study through the Call  H2020-JTI-IMI2-10B , subtopic  3B.
With respect to SDB, individuals had predominant CSA  [16]. Study exclusion criteria were amyloidosis, hypertrophic cardiomyopathy and diuretic dosage more than doubled within the 4 weeks prior to randomisation  [16]. Additional exclusion criteria for this sub-analysis of the SERVE-HF major sub-study were insufficient ECG data (e. g. technical interference) and unclear rhythm or no consensus in expert round ( supplementary table  S1 ). The sub-study protocol was approved by the appropriate local or regional ethics committee  [16]. The trial was conducted according to Good Clinical Practice and the Principles of the Declaration of Helsinki 2002. All participants gave written informed consent.
ECG measurementscardiac arrhythmias ECG data were derived from full overnight PSG. The nocturnal 1-lead-Holter ECG was monitored from a single precordial lead with sampling frequency of 250 Hz. This frequency is acceptable for analysis of ventricular and supraventricular arrhythmias  [18]. A single centre study with 167 patients suggested that PVC burden is similar in the morning (clock time 06:00-13:59), the afternoon (14:00-21:59) and at night-time (22:00-05:59)  [19]  in patients with HFrEF and SDB. Thus, nocturnal PVC burden assessed from nocturnal ECG is likely to provide a representative estimate of the total (24-h) PVC burden. Data sets were visually analysed by two trained investigators (LG, JB) according to standard definitions  [20] , with low interobserver variability for PVCs comparable with previous important analyses of nocturnal ECGs in cohorts with PSG  [18, 21]  (intraclass correlation coefficients (95% confidence interval) for a random sample of 20 sleep studies: 0.
Thus, high ventricular arrhythmia burden should be a legitimate surrogate for higher grade ventricular arrhythmias. Specific antiarrhythmic drugs were not assessed in this analysis, although the majority were taking amiodarone in our population  [38]. Intrathoracic pressures, tidal volumes and markers of autonomic dysfunction were not assessed. Sleeping position, which may have an effect the number and nature (obstructive/central) of apnoeas and hypopnoeas, was not systematically assessed. A strength of the current study is that PSG and ECG data were obtained on the same night, in contrast to other publications  [14, 19, 24]. Therefore, possible confounders such as time-related effects should be minimal. In conclusion, 44% of patients with HFrEF and CSA had a high burden of ventricular arrhythmia, and this was more likely in males, those with CSR ⩾20% of the recording time, when systolic blood pressure was lower and in the absence of antiarrhythmic medication.
Acknowledgements: Medical writing assistance was provided by  Nicola Ryan , independent medical writer, funded by  ResMed. The authors thank the  CRI (Clinical Research Institute)  for their expertise in overseeing the SERVE-HF trial. Author contributions:  C. Fisser  and  M. Arzt  were responsible for conceiving and designing the study and its hypotheses, acquiring study funding, collecting, analysing and interpreting the data, and writing and revising the manuscript prior to submission. C. Fisser ,  J. Bureck  and  L. Gall  were involved in the collection, analysis and interpretation of data, and were responsible for drafting and revising the manuscript prior to submission. V. Vaas,  J. Priefert ,  S. Fredersdorf ,  F. Zeman ,  D. Linz ,  H. Woehrle ,  R. Tamisier ,  M. R. Cowie  and  H.
Interestingly, there is a higher incidence of vertebral fractures among newly GC-treated patients (<6 months) but a relative decline in vertebral fractures after longer exposure to GC (>6 months)  [22]. Moreover, based on a database of newly GC-treated rheumatoid arthritis patients aged 18-64 years (mean age 49 years), Balasubramanian et al. demonstrated a higher fracture risk among younger, newly-treated patients and showed a 2-2. 5-fold increased fracture risk for patients receiving higher doses of GCs compared to low doses receivers. Whether these effects on bone are involved in glucose homeostasis or insulin sensitivity dysregulations induced by GCs is unknown in humans, but will be presented in the paragraph on animal models of GC treatment. In humans, it is well known that a long-term GC treatment induces weight gain and adipose tissue redistribution in connection with major changes in glucose and lipid metabolism.
RNA extraction and RT-qPCR Total cellular RNA was extracted using TRI Reagent (Molecular Research Center, Inc. , Cincinnati, OH, USA). Total RNA samples (2 µg) were reverse-transcribed using High Capacity cDNA Reverse Transcription Kits (Thermo Fisher Scientific, Waltham, MA, USA) for real-time quantitative polymerase chain reaction (qPCR) analyses; the primers used for the SYBR Green assays are listed in Extended data Table  1. Amplifications were performed using a Stratagene Mx3005P system (Agilent Technologies, Santa Clara, CA, USA). The qPCR data were normalized to the level of Psmb6 (proteasome 20S subunit beta 6) transcripts, and analyzed using LinRegPCR. v2015. 3 to get the starting concentration (N0) which is calculated as follow: N0 = threshold/(Eff meanCq ) with Eff mean: mean PCR efficiency and Cq: quantification cycle.
Microarray data analyses Microarray data were analyzed using R (R Development Core  Team, 2008)  and Bioconductor packages: www. bioconductor. org, v 3. 0,  (Gentleman et al. , 2004)  as described in GEO accession GSE165156. Raw data (median signal intensity) were filtered, log2 transformed, corrected for batch effects (microarray washing bath and labeling serials) and normalized using quantile method  (Bolstad, et al. 2003). A model was fitted using the limma lmFit function  (Smyth, 2004). Pair-wise comparisons between biological conditions were applied using specific contrasts to extract genotype, Time and Genotype:Time interaction effects. A correction for multiple testing was applied using Benjamini-Hochberg procedure  (BH, Benjamini et al. 1995)  to control the False Discovery Rate (FDR).
Other statistical analyses. Statistical analyses were performed using GraphPad Prism version 8 for Mac OS X (GraphPad Software, San Diego, CA). Two-way ANOVA was performed followed by appropriate post-hoc tests (Sidak's multiple comparisons test). P< 0. 05 was considered significant. Multiple T-test was also performed followed by appropriate correction (Benjamini, Krieger and Yekutieli). P< 0,05 was considered significant. For circadian analysis, Nonlinear Regression (sine wave with nonzero baseline) was performed followed by extra sum-of-squares F test in order to compare amplitude (A) , PhaseShift (φ) and Baseline (M= Midline estimating statistic of rhthym). Differences were considered statistically significant when P< 0,05. Figure Legends.
1% Tween-20 in TBS) for at least 1. 5 h at room temperature. Thereafter, the blocking solution was removed, and the blots were rinsed three times in the wash buffer. The membranes were incubated with primary antibodies against CYP1A1 (overnight) and β-actin (40 min). Then, the membranes were washed with TBST buffer (0. 1%) before being incubated with mouse anti-rabbit (1. 5 h) or anti-mouse (50 min) secondary antibodies for CYP1A1 and β-actin, respectively. After the membranes were washed three times with TBST buffer, they were developed in a Chemidoc Touch Imaging System (Bio-Rad Laboratories), using an ECL kit. Signal quantification was performed using the ImageLab Software system version 6. 0. 1. 34 (Bio-Rad Laboratories). β-actin was used as an internal control to normalize sample loading on the gels.
Multivariate Analysis Multivariate analyses were performed to assess the temporal changes of IH in the cysteine-related thiolomic profile. The dataset consisted of 11 fractions (total, free total, free oxidized, free reduced, and protein-bound Cys; total, free total, and protein-bound CysGly; total, free total, and protein-bound GSH) × 74 observations (rats). Data was mean-centered and scaled to unit variance before statistical analysis. Principal Component Analyses (PCA) and Geometric Trajectory Analysis  [43]  were performed to evaluate the influence of time and condition (Nx or CIH) on the thiolome. Due to the design of the study (rats are different at each time point), Partial Least Square Discriminant Analysis (PLS-DA) was performed at each time point to disclose the most relevant metabolites discriminating Nx and CIH.
Both PCA and Geometric Trajectory Analysis were performed with R environment (http://www. r-project. org/ R version 3. 6. 1, accessed on 2 July 2021), using the "Rcpm" and "pcaMethods" packages. Visualizations were made using "ggplot2" and "gridExtra" packages. PLS-DA models were performed with SIMCA software (MKS Umetrics, Umeå, Sweden Umetrics, version 16. 0. 1).
The highest cystine concentration induced a positive effect in the earlier timepoint, but a repression of Hif1α was detected at 24 h in all tested concentrations (Figure  7H ). To sum up, we investigated the impact of CIH in the following endpoints: BW, food and water intake, kidney status, AhR activation, and cysteine-related thiolomic profile. Our data showed that the differences between Nx and CIH regarding AhR and thiols were evident in short-and long-term IH (established HTN), mostly in opposite directions. At pre-HTN, we found similar endpoints between Nx and CIH, except for food intake and CysGly protein-bound fraction. In short-term IH and comparatively to Nx, we found that CYP1A1 expression was lower, protein S-thiolation and total GSH were increased, and there was a reduction in food consumption.
This was followed by incubation with 1:300 DyLight 488 streptavidin for 60 min at room temperature. Cells were then mounted on slide with Vectashield mounting medium containing DAPI (Sigma-Aldrich, Saint Quentin Fallavier, France). As controls, the primary or the secondary antibodies were omitted. Immunolabelling was observed with confocal microscopy (LSM700, Zeiss) with DAPI, DyLight 488 and texas red filter sets. The images were processed with the ZEN software (version 2010).
Radioactivity was measured with a Wizard gamma counter (Perkin Elmer) Data were analysed by using the PRISM software (GraphPad Software Inc. , San Diego, CA, USA) and results were presented as the mean of duplicate measurements. The density of binding sites (B max ) and the dissociation constant of the radioligand (K D ) values were calculated according to the method of Scatchard (F test in GraphPad Prism, version 5).
Western blot analysis Ten μg proteins denatured by heating at 100˚C for 5 min were resolved by 10% SDS/PAGE and transferred to a Hybond-C nitrocellulose membrane. Membranes were blocked with 5% non-fat dry milk diluted in PBS supplemented with 0. 1% Tween 20 and incubated with anti-pERK1/2 (1:3,000) or anti-ERK 1 (1:2,000) overnight at 4˚C. After washes, membranes were incubated with either anti-rabbit horseradish peroxidase-conjugated (1:5,000) or biotinylated anti-mouse antibody (1:250) plus peroxidase-conjugated streptavidin (1:10,000) for 1 h at room temperature. Immunoreactivity was revealed using an enhanced chemiluminescence reaction ECL. Intensity of the bands was quantified by densitometry using the TotalLab Quantsoftware (TotaLlab, Version 12. 2, Newcastle-upon-Tyne, UK).
Statistical analyses All results were analyzed by using the PRISM version 5 software (GraphPad Software Inc. , San Diego, USA). Data were expressed as means ± SEM. Statistical analysis was performed with Kruskal-Wallis tests followed by the Dunn's multiple comparison tests with statistical significance set at P<0. 05. � P<0. 05, �� P<0. 01, ��� P<0. 001.
Participants The required sample size was calculated using G*Power3 software  [15]  for rmANOVA. In the experiment, one group sample went through two conditions (the helmet and the desktop conditions) giving one two-level factor. Type I error value was set to 0. 05 and type II error value was set to 0. 9. Effect size index was obtained from the study by Freudenthaler et al. who compared the blink frequency in two conditions: general conversation and visual display unit use  [12]. The calculated value of effect size index of Cohen's d of 0. 997 corresponded to effect size index Cohen's f of 0. 5  [16]. Other studies showing the dependence of blink rate on various parameters produced even higher effect size estimates. Given the abovementioned values, 13 subjects were necessary to achieve the required power.