dataesr/scientific-paragraphs-categorization huggingface dataset:
Production of Enantiopure R-Sulforaphane The myrosinase catalyzed hydrolysis of highly purified GRA (purity 95%, 3. 10 g, 6. 192 mmol) was performed in a phosphate buffer/dichloromethane biphasic system. Enantiopure R-sulforaphane (1. 09 g, 6. 130 mmol, 99% yield) was recovered from the dichloromethane phase as a light yellow oil and characterized as reported in the following sections. 25  D -76 (c 1. 3, CHCl 3 ); lit. data: -78. 2 (c 0. 6, CHCl 3 )  [19]. GC-MS, t R = 18. 7 min; EIMS 70 eV m/z (rel. int. ): 72 (100), 160 (64), 55 (43), 39  (15) , 45  (13) , 64  (12) , 63  (10) , 41  (10) , 114  (8) , 74  (6). The observed data are in agreement with literature values  [20]. IR (cm -1 ): 3426 (O-H from H 2 O adsorbed), 2923, 2867 (C-H), 2179, 2100 (N=C=S), 1451, 1349 (C-H), 1260 (C-N), 1021 (S=O), 739 (C-H), 688 (C-S), in accordance with literature data  [21].
Statistical analysis Survey responses were transferred to SAS 9. 3 software (SAS Institute, Cary NC). Descriptive analyses used mean values 6 standard deviation (SD) and proportions for quantitative and qualitative variables, respectively. We first conducted a bivariate analysis using Pearson's x2 test or Fisher's exact test for qualitative variables and Student's t-test or Mann Whitney parametric test for quantitative variables. A backward logistic regression model was built to assess the multivariate associations between residents' exposure index to the pharmaceutical industry per month and residents' particulars. The exposure index was dichotomized according to the median value, superior or inferior to 1. 5. Independent variables associated with a p value under 0. 25 in bivariate analysis and known confounding factors (gender and ad hoc training regarding conflict of interest), whatever their significance level, were included in the initial model.
Introduction BOC and TPV are potent NS3/4A protease inhibitors for the treatment of chronic hepatitis C (HCV) genotype 1 infection. BOC and TPV are both substrates and strong inhibitors of the CYP3A, therefore presenting a wide interindividual pharmacokinetic variability and multiple drug interactions especially with ARV such as lopinavir/r, darunavir/r or efavirenz, thus restricting options for concomitant ARV therapy. We evaluated plasma concentrations of coinfected and monoinfected patients treated with BOC and TPV and the PK data of patients treated with non recommended ARV.
Study design This pharmacokinetic study was conducted in the Mbarara National Referral Hospital (MNRH) antenatal clinic in Uganda and nested into a larger efficacy study published elsewhere. 15 A non-compartmental analysis of quinine plasma concentration -time data after the first dose has previously been published. 16 The trial was registered at ClinicalTrials. gov (NCT00495508) and patients in the quinine pharmacokinetic study arm were recruited from 19 February to 23 July 2008. Ethical approval was obtained from the Mbarara University Faculty of Medicine Research and Ethics Committee, the Mbarara University Institutional Ethics Committee, the Uganda National Council for Science and Technology (ethics committee) and the 'Comites de Protection des Personnes' (Ile de France XI, France). Inclusion criteria were an estimated gestational age (EGA) of at least 13 weeks (confirmed by ultrasound or fundal height and the Dubowitz score at delivery if ultrasound was not available), residence in the Mbarara Municipality (a radius of 15 km from MNRH) and P.
Pharmacokinetic analysis NONMEM v. 7. 2 (ICON Development Solutions, Ellicott City, MD, USA) with a G-Fortran compiler (Free Software Foundation, Boston, MA, USA) on a Windows 7 operating system (Microsoft Corporation, Seattle, WA, USA) was used for modelling and simulation. The first-order conditional estimation method with interaction and subroutine ADVAN5 TRANS1 was used during model-building. 17 Post-processing and automation was performed using Perl-speaks-NONMEM v. 3. 5. 3,  18, 19 Xpose v. 4 20 and R v. 2. 15. 1 (The R Foundation for Statistical Computing, Vienna, Austria). The objective function value (OFV) (computed as minus twice the log likelihood of the data), physiological plausibility and goodness-of-fit (GOF) diagnostics were used to evaluate competing models. A fall in OFV (DOFV) of 3.
The distribution of the estimated covariate effects for gestational age and trimester were obtained from 200 individual bootstrap runs (stratified by trimester of pregnancy) and visualized using a box and whisker plot (GraphPad Prism v. 6. 00; GraphPad Software). Eta and epsilon shrinkages were calculated to assess the reliability of the individual parameter estimates and the GOF diagnostics. 22 A stratified (trimester) bootstrap (n ¼ 1000) was performed to calculate the nonparametric CIs and relative standard errors of the parameter estimates. The predictive power of the model was examined using visual and numerical predictive checks, i. e. 2000 simulations of each individual plasma sample. 23 The 95% CIs of the simulated 5th, 50th and 95th percentiles were overlaid with the 5th, 50th and 95th percentiles of the observed data for a visual predictive check.
A limitation of the current study was its relatively small sample size (n ¼ 22). However, dense data were collected for all women, which produced a good description of the concentration -time profiles in the women studied. A drawback of small sample sizes is the lack of power to detect true pharmacokinetic differences between groups and they also might not estimate the variability between patients accurately. Due to the small sample size, a parsimonious P value of 0. 001 was used in the formal backward elimination step of the covariate building in order to avoid false positives. Pharmacokinetic sample collection over the entire duration of treatment offered an advantage to evaluate the disease effect over the treatment course. The best-performing disposition model consisted of two distribution compartments.
Background: Observational pharmacoepidemiological (PE) studies on drug safety have produced discrepant results that may be due to differences in design, conduct and analysis. Purpose: The pharmacoepidemiology work-package (WP2) of the Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium (PROTECT) project aims at developing, testing and disseminating methodological standards for design, conduct and analysis of pharmacoepidemiological studies applicable to different safety issues using different databases across European countries. This article describes the selection of the safety issues and the description of the databases to be systematically studied. Methods: Based on two consensus meetings and a literature search, we selected five drug-adverse event (AE) pairs to be evaluated in different databases.
Results: The selected drug-AE pairs are: 1) inhaled long-acting beta-2 agonists and acute myocardial infarction; 2) antimicrobials and acute liver injury; 3) antidepressants and/or benzodiazepines and hip fracture; 4) anticonvulsants and suicide/suicide attempts; and 5) calcium channel blockers and malignancies. Six European databases, that will be used to evaluate the drug-AE pairs retrospectively, are also described.
INTRODUCTION Randomised Clinical Trials (RCT) of drug adverse events do not optimally reflect real life situations: small sample sizes, highly selected populations and short duration of exposures  [1]. During the past decades, it has been realized that adverse drug-events (AE) need to be further evaluated in pharmacoepidemiological (PE) studies  [2]. PE methods were, however, still in development and therefore had the potential for reporting biased results. An example is the falsely reported relationship of breast cancer to use of the blood pressure lowering drug reserpine  [3]. The growing availability of large routine electronic health record databases has made it possible to study less frequent and less severe AEs. An example is the risk of deep venous thrombosis in users of third generation oral contraceptives  [4].
Alhough (pharmaco)-epidemiological methods have progressed, the challenge of studies of low absolute and relative risks associated with medications may have pushed pharmacoepidemiology to the borders of what can reliably be detected beyond the level of background noise  [5]. Furthermore, efforts focusing on evaluation of type A AEs (those with dose dependent and predictably augmented pharmacological effects) and intended effects of drugs have increased the potential for bias  [6]. Study conduct and design choices are one of the factors contributing to the diversity and discrepancy of study results. For instance, using the same database (the UK Clinical Practice Research Datalink  [7]  and including a large number of patients, two studies that were independently conducted reached very different conclusions  [8, 9].
Within the same source study population, discrepant results between studies can be explained by small differences in study design such as different definitions of exposure time windows, confounder selection and age matching  [9, 10]. Moreover, exposuretime-dependent hazard functions can substantially affect comparisons between different studies of the same drug  [11]. The use of different statistical methods to adjust for confounding is another explanation for dissimilar study results  [12]. For instance, in a database study and in simulation studies, systematic differences were found in effect estimates when propensity scores were used compared to logistic regression or Cox-proportional hazards regression  [13, 14]. Immortal time bias has been suggested as another important source of variability in results between observational studies on drug effects  [15].
Furthermore, several studies that have evaluated the same data source have drawn different conclusions about the plausibility of a pharmacological explanation of an observed association. Among these are: use of inhaled corticosteroids and risk of hip fracture  [16, 17] , use of beta-blocker and risk of hip fracture  [18, 19] ; use of oral bisphosphonates and risk of cancer of oesophagus  [20, 21] ; and more recently, use of proton pump inhibitors and risk of hip fracture  [22] [23] [24] [25]. The influence of methodological variation should be minimized and quantified, in order to interpret differences in associations between drugs and AEs that arise between types of data sources and healthcare systems in the different countries. A clear interpretation of differences in results between studies performed in the same database, and between different databases, is currently not completely feasible due to these methodological differences.
This situation poses difficulties for all stakeholders, such as regulatory agencies, industry, healthcare professionals and patients. Difficulties in interpreting individual and/or groups of observational studies limit their usefulness for decision making on the benefit-risk balance of drugs. These experiences highlight the need to increase understanding of the implications of different methodological choices by investigators and for a framework on PE methodology across different data sources. To understand and subsequently validate differences caused by methodological and nonmethodological (data related) factors we have selected five different drug-AE pairs, to be analysed in five different European databases based on a common protocol that includes extensive sensitivity analyses. The Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium (PROTECT) study is a collaborative European project that addresses limitations of current methods in the field of pharmacoepidemiology and pharmacovigilance  [26].
PROTECT is a multinational consortium of 29 partners including academics, regulators, small and medium enterprises (SMEs) and European Federation of Pharmaceuticals Industries and Associations (EFPIA) companies, coordinated by the European Medicines Agency (EMA) with GlaxoSmithKline (GSK) as deputy coordinator. The "Framework for pharmacoepidemiology studies" work-package (WP2) of PROTECT, co-led by Utrecht University and Pfizer, aims at developing, testing and disseminating methodological standards for the design, conduct, and analysis of PE studies applicable to different safety issues using different data sources. This article presents the rationale, design and the first results of the WP2 of PROTECT initiative.
Selection of Drug-AE Pairs Criteria for the selection of key AEs to evaluate in different databases included: 1) the AE selected having resulted in (major) regulatory decisions such as drug withdrawal or major summary of product characteristics (SmPC) changes; 2) public health impact aspects including seriousness of the event (prioritise more serious events); having variable incidence rates (both rare and common events); and prevalence of drug exposure (commonly used drugs and infrequently used drugs); 3) possibility to investigate a broad range of relevant methodological issues including feasibility to ascertain events in electronic healthcare databases (events both easy and difficult to ascertain); hazard functions (acute and long-term effects, delayed/transient effects); setting of drug use (in-/outpatient use); type of use (short/long-term, as needed); and different indications of use.
Databases All PROTECT partners who manage or have access to electronic healthcare or reimbursement databases were asked to describe characteristics of these databases. Databases incorporated medical and registry-based data sources, such as the Danish national registries, the Dutch Mondriaan project, the British CPRD and The Health Improvement Network (THIN) databases, the Spanish BIFAP project and the German Bavarian claims database. In addition, the French PGRx case-referent system will be made available to investigate and/or confirm some of the drug-AE pairs. All partners were sent a questionnaire in order to systematically collect the information. Parameters included information on period of data collection, coding systems, accessibility procedures and an extensive list of specific categories for longitudinally collected data such as drug prescribing/ dispensing, clinical data, laboratory test data and life style parameters.
Analytical Approach Common study protocols to study each of the drug-AE pairs have been developed and comply with the ENCePP methodological standards (including the ENCePP checklist) and were submitted to the ENCePP registry of studies  [28]. These protocols include different study designs such as cohort, case-control, and case-cross-over design. All studies are retrospective, based on existing data from the databases described above. We will use data from the period 2001-2009. Inclusion for entry in the cohort studies is that subjects would have to have at least 1 recorded prescription or dispensing of the drug of interest. This approach reduced confounding by indication and still allows comparing between subjects that are on the drug at a certain time during follow-up versus subjects that are not currently on the drug but used the drug in the past.
Operational definitions of exposures and outcomes are harmonized as much as possible and varied in a range that reflects the possibilities and limitations of the available databases. For the outcome of liver injury a automated algorithm has been developed taking into account diagnostic codes and laboratory tests. Detailed code lists are available upon request. Exposure will be analysed time-dependently in all studies and some confounders will also be classified time-dependently if appropriate. Different methods for the selection of and control for confounding variables will be applied. Not all databases have the same level of detail with regard to confounders. We will conduct an analysis for each drug-ae pair that includes a minimum set of confounders that all databases have available. In subsequent sensitivity analyses we will also assess the impact of further adjustment for confounders that are available in some, but not all databases.
The Databases General features of the databases participating in PROTECT are presented in Table  4. The six databases contain data from patients from five different European nations: the Danish national registries, the Dutch Mondriaan database, the British CPRD and THIN databases, the Spanish BIFAP project, the German Bavarian claims database. The Danish registries have national coverage, while other databases contain regional data or a representative sample of a total population. All the databases were quite representative of their nation. Most of the databases were established more than 10 years ago with regular and expanding data collection and validation history. Routine checks on quality are performed in all databases. The majority of databases include GP data and two (Danish and CPRD) include registries for and linkages to mortality, cancer, and secondary care data.
Three (Danish registries, Mondriaan and Bavarian claims) out of six databases include or had linkages to claims data. A particular characteristic of the Bavarian Claims database is the availability of information on prescriptions and diagnoses in quarters of a calendar year. The exact dates of prescribing and diagnoses are not available. Therefore, we decided to use this database for descriptive purposes only and refrained from conducting association studies for which this information is pivotal. For some databases, linkage to other national registries requires additional procedures and financial compensation. Table  4  briefly describes the participating databases.
DISCUSSION AND UPCOMING STUDIES IN PROTECT We prioritised five drug-AE associations that are highly relevant from the perspective of various stakeholders including regulatory agencies, patients and the pharmaceutical industry. These associations allow investigation of the   [34]  For biologicals  [41]  5-year fatality rate: 43%-71%  [53]  Tumour  15  [56]  to 11  [54]  /1000 30  [56]  to 81  [54]  /1000 As needed/long term use Anticonvulsants 17  [56]  to 22  [55]  /1000 Chronic Calcium channel blockers 45  [55]  to 70  [54]  /1000 Chronic SSRI = selective serotonin reuptake inhibitor TCA = tricyclic antidepressants [ ] = number indicating the reference including these data influence of variation in methodology. Furthermore, we characterised seven routine electronic healthcare databases from five European countries that will be used for the evaluation of the selected drug-AE associations.
Main Methodological Issues Hazard Function RR > 2 for cardiovascular events vs. placebo  [57]  ORs 1. 7 -7. 3 (new users) for MI vs. non-users  [57]  Systematic review (RCT) (Case-control) RR = 2. 5 for respiratory deaths vs. placebo  [58]  Meta-analysis (RCT) salmeterol: all cause mortality Peto OR =1. 3 vs. placebo  [59]  non-fatal AE OR = 1. 2 vs. placebo  [59]  formoterol : non-fatal serious AE OR = 1. 6 vs. placebo  [60]  Cochrane database systematic review (RCT)  on utilizing electronic healthcare data records and biomedical databases for the early detection of AEs. In the EU-ADR project a list of 23 events were judged as important in pharmacovigilance and three AE (acute myocardial infarction, acute liver injury, and suicidal behaviour/attempt) on this list have also been prioritised in our project.
The OMOP project has also defined a list of health outcomes of interest (HOI) and drug pairs to be further investigated. As previously mentioned we included two of these pairs (DILI and antimicrobials, hip fracture and benzodiazepines) in our prioritised list of five drug-AE pairs. Although these projects have a different focus than those of WP2 of PROTECT, the overlap in prioritised AEs (and drugs) will facilitate comparisons. The strengths of our approach include the development of a common study protocol (that includes variation in methodology e. g. different designs) for five drug-AE associations that will be studied in different databases. In addition, some of our findings will be confirmed in specific registries such as PGRx  [32]. Our approach will allow us to distinguish between variation in results due to variation in methodology and those due to database differences.
Analysing these discrepancies will provide guidance regarding the optimal methodology for certain safety issues and the optimal selection of appropriate data source(s). The experience obtained in the PROTECT database network will improve the possibilities for multinational database studies for various safety issues, including the investigation of rare serious AE. Finally, other research activities of WP2 of PROTECT will further improve the methodological guidance on pharmacoepidemiological studies. These include an evaluation and improvement of methods to control for confounding such as propensity scores and instrumental variables in simulation studies, and drug utilisation research. A limitation of our approach may be the scope of the drug-AE pairs and selected healthcare databases. Our findings may not be extendable to other safety issues or other databases that we do not study.
However, our selection of drug-AE pairs includes common drug safety issues presenting different methodological challenges. The different types of databases (GP, claims, and registries) owned by PROTECT partners, also make extrapolation of our findings to wider ranges of data sources possible. Furthermore, our findings will be validated by testing different drug-AE pairs in the same databases and confirmation of drug-AE association in specific registries that include more detailed information on outcomes and potential confounding factors. In conclusion, WP2 of PROTECT will assess the influence of methodological parameters on the association between selected AEs and drug class of interest. The selected AEs include resulted in (major) regulatory decisions such as drug withdrawal or SmPC changes or allow the investigation of a broad range of relevant methodological issues.
The anticipated results of this project include the creation of a European database network and further development of methodological standards for the conduct of (multi-) national PE studies. Methodological standards will be included when appropriate in the EMA-based ENCePP guidance on methodological standards. Increasing methodological standards and registration of study protocols may decrease discrepancies in results from these studies, increase transparancy and thereby increase the usefulness and reliability of these studies for benefit-risk assessment and decision-making of marketed drugs in Europe and beyond.
Funding The research leading to these results was conducted as part of the  PROTECT consortium (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium , www. imi-protect. eu)which is a public-private partnership coordinated by the  European Medicines Agency. The  PROTECT  project has received support from the  Innovative Medicine Initiative Joint Undertaking  (www. imi. europa. eu)under Grant Agreement n°  115004 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/ 2007-2013 ) and EFPIA companies' in kind contribution. In the context of the  IMI Joint Undertaking (IMI JU) , the  Department of Pharmacoepidemiology, Utrecht University , also received a direct financial contribution from  Pfizer.
838 nm). Using an ‫ف‬ 200 µm diameter beam-stop, twodimensional X-ray scattering patterns were recorded in the range of scattering vectors q = 2 /d = 4 (sin )/ from 0. ). The measured angular resolution in 2 was 0. 075°, which corresponds to resolutions ⌬ q of 0. 1 nm Ϫ 1 in the SAXS region and of 0. 01 nm Ϫ 1 in the WAXS region. The data collection procedure consisted of a series of transverse scans (perpendicular to the SC plane), at least in triplicate for each sample, with a step size of 2 µm. Two-dimensional scans (transverse and lateral) were also performed to check the homogeneity of the structure at the micrometer scale. The step size for lateral scans was 7 µm. Time exposure for recording a pattern was limited to 1 or 3 s in order to collect data prior to the fi rst radiation damage effects, which were observed after 5 s to 10 s exposure.