dataesr/scientific-paragraphs-categorization huggingface dataset:
LEAP1 (NCT02559310) was a multicenter, double-blind randomized trial conducted in adult patients with CABP and a Pneumonia Outcomes Research Team (PORT) risk class ≥III  [9]. LEAP1 study patients were randomized 1:1 to receive either lefamulin 150 mg IV every 12 h or moxifloxacin 400 mg IV every 24 h combined with adjunctive linezolid for any patient with suspected MRSA, with subsequent switching to oral lefamulin and oral moxifloxacin  [9, 11, 12]. The results of the study showed that lefamulin was safe and well-tolerated  [11, 12]. It was found to be noninferior to moxifloxacin in the early clinical response (ECR), namely, 96 ± 24 h after the first dose of the study drug in the intent-to-treat (ITT) population that was FDA's primary endpoint (87. 3% vs. 90. 2%, respectively)  [11, 12]. In addition, it was noninferior to moxifloxacin in the European Medicines Agency (EMA)'s coprimary endpoints, i.
e. the investigator assessment of clinical response (IACR) 5-10 days after last dose of the study drug in the modified ITT (mITT) population (81. 7% vs. 84. 2%, respectively) and in the clinically evaluable (CE) population (86. 9% vs. 89. 4%, respectively)  [11, 12]. LEAP2 (NCT02813694) was another multicenter, randomized, double-blind study that compared the safety and efficacy of oral lefamulin (dose of 600 mg every 12 h for 5 days) with that of moxifloxacin (dose of 400 mg every 24 h for 7 days) in patients with CABP and PORT risk class between II and IV  [10, 13]. This study aimed to complement the results of the LEAP1 study  [10, 13]. It was reported that lefamulin was noninferior to moxifloxacin regardless of the PORT risk class for both FDA's and EMA's endpoints, i. e. for ECR in the ITT population which was the FDA's primary endpoint (for PORT II, III, IV: 91.
8%, 91. 0%, and 85. 0% for lefamulin; 93. 1%, 90. 2%, and 85. 7% for moxifloxacin, respectively), and for the EMA's coprimary endpoints of IACR in mITT population (87. 5% vs. 89. 1%, respectively) and in CE-test-of-cure (TOC) population (89. 7% vs. 93. 1%, respectively)  [13]. Serious adverse events did not differ between the two groups, while the rate of discontinuation was low and similar in both groups  [13]. A multicenter, double-blind, randomized, parallel-group, phase II study (NCT01119105) compared lefamulin (100 mg) and lefamulin (150 mg) with a vancomycin (1 g) IV infusion every 12 h for 5 to 14 days for ABSSSIs caused by Gram-positive pathogens  [14]. The presence of MRSA along with other clinical characteristics were comparable between the groups  [14]. It was found that lefamulin had a clinical success rate comparable to vancomycin, i.
Acknowledgements We are grateful to  Dr Kongkanda Chayamarit  ( BKF ) for communicating with C. Picheansoonthon. The first author thanks  Dr. Vichith Lamxay  for his help and guidance during her fieldwork. We wish to thank the staff of the  Ministry of Science and Technology, Lao PDR , especially  Mrs. Keophayvanh DOUANSAVANH , General Secretary of the Cabinet of the  Lao Academy of Science and Technology  and  Mr. Phouthasack VICHITRA , Director of the Cabinet. Also, the staff in Science and Technology at provincial and district level for all their support and help during our fieldwork in Lao PDR. We thank the Franklinia Foundation who contributed to this fieldwork through a grant to the Muséum national d'histoire naturelle for the  Flora of Cambodia, Laos and Vietnam. This paper is part of a project ( AAP3-96 ) supported by the  Sud Expert Plantes Développement Durable programme.
Acknowledgments The authors are grateful to  Mr Assi Jean  from the  Centre National of Floristic (CNF, Abidjan, Côte d'Ivoire ) and  Mr Téré Henry  from the  Centre Suisse of Research (Adiopodoumé, Abidjan, Côte d'Ivoire)  for the plant identification. The authors also thank  Dr Kouamé Assi Mathias  for his helpful contribution in the plant material collection.
Methods: We carried out a study using a random sample of all drugs dispensings registered in a database of the French Health Insurance System between 2010 and 2015. The distribution of the drugs involved was described considering active principles, detailing the 20 most frequent ones for both contraindicated or discommended codispensings (DCs). To investigate whether the frequency of CDI codispensings appeared higher or lower than the expected for the drugs involved, we developed a specific indicator, the Drug-drug interaction prevalence study-score (DIPS-score), that compares for each drug pair the observed frequency of codispensing to its expected probability. The latter is determined considering the frequencies of dispensings of the individual drugs constituting a pair of interest. Results: We analyzed 6,908,910 dispensings: 13,196 (0.
Data Source and Study Population This study was conducted using claims data from the EGB database, a 1/97th dynamic age-and sex-representative sample of the population covered by the French national healthcare system. Briefly, EGB contains individual and anonymous data for 670,000 persons on demographic data such as gender, age and dates of death. It also contains comprehensive data on outpatient drug dispensing, including the dose and the quantity of the drugs dispensed, the date of the dispensing and the characteristics of the prescriber  (Bezin et al. , 2017). No information is available in EGB concerning drugs that are not reimbursed by the French national healthcare system, inpatient drugs, and over-the-counter drugs. In this study, a random sample of 100,000 patients who had received at least one drug dispensing was constituted for each quarter between 01/01/2010 and 31/12/2015.
" Although its PDF format makes it difficult to be used in routine practice by prescribers, the French thesaurus is often mentioned as a main source by clinical decision support systems. Moreover, this thesaurus constitutes an official and powerful tool for the study of the populational exposure to the risk of DDIs from medico-administrative databases. To use this thesaurus we developed an R package to extract the text from the PDF file and structured its content into a more machine readable format (CSV) 2. Because this thesaurus is updated every year we used the thesaurus version corresponding for each year. Using this thesaurus on our data set, we have identified, within all dispensings, DDIs associated to the highest level of risks for patients, i. e. , those related to "discommended" and "contraindicated" drug concomitant uses.
Statistical Analysis The prevalence of dispensing and the corresponding 95% confidence interval was estimated for each pair of CDI codispensed drugs and for each quarter of analysis from the random sample. This prevalence was then extrapolated to the French population using the annual demographic data provided by the French National Institute for Statistics and Economic Studies (Institut National de la Statistique et des Etudes Economiques, INSEE) 3. For this extrapolation, we have taken into account the proportion of drug users in the complete EGB, which is supposed to be representative of the general population. A measure of association we denominated the DIPS-score was calculated for each pair of CDI codispensed drugs to determine whether these drugs are more commonly used together or not.
The result herein presented thus correspond to an underestimated prevalence of concomitant use of drugs Contraindicated or Discommended because of Interactions, as they do not consider concomitant treatments that would result from successive but overlapping dispensings. If willing to estimate this overall prevalence, it would be needed to individualize sequences and overlaps of dispensing in patients, whatever the date of delivery. Additionally, as dealing with a reimbursement database, OTC deliveries cannot be considered for the estimation of the prevalence of concomitant treatments for contraindicated or discommended drug pairs. This is another factor of underestimation for the prevalence of use of drugs Contraindicated or Discommended because of Interactions, that however also not relate to a potential ignorance of the associated risk by physicians.
ACKNOWLEDGMENTS We thank  Dr. Vianney Jouhet  of  CHU de Bordeaux  for helpful suggestions on the revision of an earlier version of the manuscript. A summary of this work has already been published for three conferences: 34th International Conference on Pharmacoepidemiology & Therapeutic Risk Management; 22-26 August, 2018, Prague, Czechia. 22nd  Congrès de la Société Française de Pharmacologie et de Thérapeutique ;  12-14  June, 2018, Toulouse, France. 6th Congrès National Conjoint ADELF/EMOIS; 29-30 March 2018, Montpellier, France.
Histograms of enrichment score for each pathway. Gene number and corresponding p-value to the right of the histograms. Expression profiles of hepatic genes involved in steroidogenesis pathways in CAR-/-male and females (D). 31 probes were selected as differentially regulated in CAR-/-male and females, respectively (p-value interaction (genotype-sex) < 0. 05, n = 6 per group. (E) The DEG lists (FDR = 0,05) of altered mRNAs in the microarray of CAR-/-male and female mouse livers were compared in Correlation Engine (Illumina) to the gene lists of liver signatures for STAT5B (GSE60253), GHR (GSE11396), LXRαβ (GSE38083), AhR (GSE10082), HNF4α (GSE10390) and NRF2 (GSE864) and SREBP1a (GSE53397). Positive correlation was shown by (-log (p-value overlap) > 4).
Liver neutral, phospholipid, and sphingolipid analysis. Hepatic lipid contents were determined at the end of the experiment as described previously  35. 1H-NMR-based metabolomics of liver extracts. Liver polar extract preparations for 1 H-NMR-abased metabolic profiling and statistical analysis of metabolomics data were conducted as described previously  36   Histology. Paraformaldehyde-fixed, paraffin-embedded liver tissue was sliced into 3-μm sections, deparaffinized, rehydrated, and stained with hematoxylin and eosin for histopathological analysis. Staining was visualized with a Leica microscope DM4000 B equipped with a Leica DFC450 C camera (Leica Microsystems). The scores of steatosis and inflammation were calculated according to the Kleiner method  17  and was the sum of three scores using the entire slice field: (a) the first score was defined according to the surface covered by lipid droplets (i.
Gene expression studies. Total RNA was extracted with TRIzol ® reagent (Sigma-Aldrich). For real-time quantitative polymerase chain reaction (qPCR), total RNA samples (2 μg) were reverse-transcribed using a High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Courtaboeuf, France). Primers for SYBR Green assays are presented in Supplementary Table  1. Amplifications were performed on an ABI Prism 7300 Real Time PCR System (Applied Biosystems). qPCR data were normalized by TATA-box binding protein mRNA levels and analyzed with LinRegPCR (2015. 3 version). Microarray gene expression studies. Gene expression profiles were obtained for six liver samples per group at the GeT-TRiX facility (GénoToul, Génopole Toulouse Midi-Pyrénées, France) using Sureprint G3Mouse GE v2 microarrays (8 × 60 K; design 074809; Agilent technologies) as previously described  36.
Acknowledgements We thank  Prof. David Moore  ( Houston, Texas, USA ) and  Dr. Urs A. Meyer  ( Biocenter, University Basel, Switzerland ) for providing the CAR knock out mice. We thank all members of the EZOP staff, particularly  Colette Bétoulières  for her careful help in animal care and technical assistance with animal experiments. We thank the staff members of the following GenoToul core facilities for technical assistance:  MetaToul/Lipidomic ,  Anexplo/ Histopathology , and  Phenotyping. Dr. Laila Mselli-Lakhal  is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. This article is part of the thesis " Rôle dimorphique du récepteur nucléaire CAR dans la régulation de l'homéostasie énergétique et des perturbations métaboliques induites par un mélange de pesticides " by  Céline Lukowicz.
Docking. Modeling and simulations were performed using 2018. 0101 (Chemical Computing Group, Inc. ) using non-aged X-ray crystal structure of hIDE PDB code 4NXO with BDM44768 ligand. A monomer including solvent was selected and compounds were docked. Before analysis, ligands, solvents and proteins were protonated at 300 °K, pH 7. 0 with a salt concentration equal to 0. Thermal shifts were calculated using ΔTAgg50, which is the difference in temperatures between the control and treatment, at which 50% signal intensity (protein denaturation) was observed.
2-2 min, 2 to 98% B, 2-2. 5 min, 98% B, 5. 5-2. 6 min, 98 to 2% B, 2. 6-4 min, 2%B. Flow rate was 0. 6 mL/min. Column oven and autosampler temperatures were maintained at 40°C and 10°C respectively. The column outlet of the LC was either connected to Xevo TQD triple Quadrupole Mass Spectrometry (Waters) for MS/MS detection in positive electrospray ionization (ESI) mode. The source temperature was 150 °C, desolvation temperature 600 °C, cone gas flow 50 L/h and desolvation gas flow 1200 L/h. The analytes were monitored in multireaction monitoring mode (MRM). Analyses were processed using MassLynx software (Waters®). The ratio between cellular and medium fraction peak areas permitted to compare the membrane permeability capacities of drugs.
Acknowledgements The authors acknowledge financial support from  INSERM, University of Lille ,  Institut Pasteur de Lille ,  Region Nord Pas de Calais, FEDER, Etat  ( 0823007 ,  0823008 ,  07-CPER 009-01 ,  2007-0172-02-CPER/3 ) , the  European Union  under the  European Regional Fund (ERDF) , by the  Hauts de France Regional Council  (contrat  N°17003781 ), the MEL (contract  N°2016_ESR_05 ), and the  French State  (contract  N°2017-R3-CTRL-Phase 1 ). andInstitut  Universitaire de France. P. H. is a recipient of a fellowship from  INSERM.
Identification of Putative Antileishmanial Compounds Based on Liquid-liquid Extraction The first seven ranked compounds from the OPLS regression analysis were putatively annotated by matching compounds in the Dictionary of National Products (DNP) database (CRC press, v27. 2). Compounds belonging to the Myrtaceae family and the Psidium genus were prioritized to narrow down the possibilities. For each compound, some candidates were ranked and proposed based on their similarity score according to the comparison between experimental MS/MS fragmentation and the in silico spectra of candidates. This process resulted in the annotation of seven candidates belonging to the Psidium genus, and all were classified as triterpenoid compounds. Annotation results were supported UV spectra of each peak (Table  1 ; column "UV").
Identification of Significant Features Molecular formulae of significant features were calculated with MS-FINDER 3. 24  [18]. Various parameters were used in order to reduce the number of potential candidates, such as exclusive selection of the elements C, H, and O; mass tolerance fixed to MS1:0. 01 Da and MS2:0. 2 Da; and the isotopic ratio tolerance set to 20%. Only natural product databases focused on plants were selected from the DNP (CRC press, v27. 2). Compounds from the Psidium genus or Myrtaceae family were prioritized. The results were presented as a list of compounds sorted according to the score value of the match. This value encompassed uncertainty on accurate mass, the isotopic pattern score, and the experimental MS/MS fragmentation mirrored to in silico matches. Only chemical identities with a final score above five were retained.
Supplementary Materials: The following are available online, Figure  S1 : Permutation plot of the OPLS correlation model between antileishmanial activity and the liquid chromatography-mass spectrometry dataset; Figure  S2 : MS/MS fragmentation pattern of standard compound 1, 2, 5, 6 versus identified peaks in crude extract chromatograms. ; Table  S1 : 1H-NMR and 13C-NMR data (DMSO-d6) of compound 1. LC-MS raw files and treated data spreadsheet has been archived to zenodo with the linked DOI: 10. 5281/zenodo. 3555529.
Additionally to this effect, aggregation of vesicles is also visible in the bilayer structure of the scattering curve around 0. 1 Å -1 in the form of appearing peak structures. A second peak at = d d 2 2 1 (see Eqn. 1) denotes lamellar stacking. Whereas the cholesterol containing systems exhibit a similar position of the Bragg peaks with a correlation length of ≈72 Å, the cholesterol-free system containing aescin exhibits a rather disordered structure and has a shifted maximum position to smaller repeating distances (D X,RD ≈ 62. 8 nm). Smaller D X,RD values indicate more compact packing of sheets and vice versa. Additionally, SAXS curves were analysed by the standard IFT analysis (solid lines in the same figure) by using the GIFT software from O. Glatter  58. The fitting procedure is described elsewhere  27.
The expression of Z n (q) in Eqn. 5 describes the interference effects for aggregates consisting of more than one bilayer. = - + - 〈 〉 + + - + Z q w w w q D x S x S ( ) 1 1 2 cos( ) (1 ) (5) N N N N N 2 2 1 In this expression = + - 〈 〉 S q a N w w q D ( ) / [1 2 cos( )] N N 2 2 with = a w 4 N 2 - + 〈 〉 w w q D 2( )cos( ) 3 - 〈 〉 + w N q D 4 cos( ) N 2 + - 〈 〉 + w N q D 2 cos[ ( 1) ] N 3 + + 〈 〉 + w N q D 2 cos[ ( 1) ] N 1. The non-integer number of bilayer stacks N L is calculated as linear combination of the lower and higher values = + - + N x N x N (1 ) ( 1) L N N. The final expression used for describing the SANS data is a summation of I q ( ) ves and I q ( ) LS (Eqn. 6) with N as adjustable scale factor and a and b denoting fractions of the vesicle and LS model, respectively. = ⋅ ⋅ + ⋅ + I q N a I q b I q b ackground ( ) [ ( ) () ] (6).
Results of the SANS data analysis. The parameters are the aescin content x(aescin), number of bilayers N L rounded to whole numbers, the neutron scattering length density (NSLD), the core radius R C,N and the fractions a and b (with a + b = 1), according to Eqn. 6. N L NSLD/10 -6 Å -2 R C,N /Å σ(R c )/% a b N L NSLD/10 -6 Å -2 R C,N /Å σ (R c )/ did not vary significantly for the samples or were kept constant and are therefore not mentioned explicitly in Table  1. Those parameters are the scale factor (≈0. 019), the background (0. 15), the bilayer spacing D (≈72 ± 2 Å), the relative standard deviation of the Gaussian layer distance distribution σ D /〈 〉 D (0. 2 ± 0. 1), and the bilayer thickness t (t(10 °C) = 40 ± 1 Å and t(40 °C) = 35 ± 1 Å). The comparison of the model approximations with the scattering data in Fig.
9  shows that most of the curves follow the data with a reasonably good agreement. Hence, this model seems to represent both, the vesicular and the multilayered character of the samples, even though some of the samples show deviations at very low q and around 0. 01 Å -1. It was found that the scattering curves cannot be sufficiently described when only assuming a stacking of two units. This is most likely due to other underlaying effects which have significant influence on the scattering intensity or possible (but not expected) formation of mulilayered vesicles, as indicated by the SANS data. On the one hand deformation and therewith elongation of the vesicles (shifting of the slope at low q towards -1) occurs during the aggregation. On the other hand, scattering from an inhomogeneous bilayer or formation of larger vesicles may increase the scattering intensity arbitrarily at low q  57.
6-4 Å -1 at a sample to detector distance of 0. 12 m. The cholesterol-free samples were measured 10 × 1 s and the steroid-containing ones 25 × 1 s with a CCD Rayonix LX-170HS detector and a wavelength of 1 Å. The first data treatment was done with the beamlines data reduction program package  69. The samples were measured in a flow-through Kapton capillary (1 mm, Good-Fellow GmbH, Bad Nauheim, Germany) which is temperature-controlled in a Linkam stage (Linkam Scientific, Tadworth, UK). The sample scattering was normalized by sample thickness, the incident intensity, transmission, background, and acquisition time. Water was used for absolute intensity calibration. Small-angle X-ray scattering (SAXS). SAXS was performed for DMPC SUVs and SUVs with 1 mol% aescin. Supplementary cholesterol was varied between 0 and 10 mol%.
Acknowledgements The authors gratefully acknowledge the financial support by the  Deutsche Forschungsgemeinschaft (DFG)  ( INST 215/432-1 FUGG, HE2995/7-1 ). Moreover, they thank the  ILL  for allocation of beamtime at the D11 beamline (proposal: 9-13-702 ) and the  Synchrotron ESRF  for allocation of beamtime at the ID02 beamline (proposal:  SC-4393 ). The authors thank  Prof. T. Koop  for placing the DSC at their disposal and  P. Geisler  for help with the fitting of the SANS data. Y. Hannappel  and  O. Wrede  are gratefully acknowledged for help during SAXS experiments. We acknowledge support for the Article Processing Charge by the  Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University. This work benefited from the use of the SasView application, originally developed under  NSF  award  DMR-0520547.
Pharmacoepidemiology: thirty years of methodological creativity Almost twenty years before the officialization of the ISPE, visionary researchers started to set up large surveillance programmes. The most famous and historically important was the Boston drug collaborative surveillance program (BDCSP) established in 1966 (five years after the thalidomide drama) in Boston (USA) by Prof. Hershel Jick. It was the first group to conduct large-scale epidemiologic research to quantify the risk of adverse reactions of drugs prescribed in the framework of actual practice. The BDCSP utilized in-hospital monitoring and played a major role in the development of pharmacoepidemiologic methods. It adopted systematic computer recording in 1970 and can therefore be considered as the father of modern research databases  [18].
It greatly improved the knowledge about this rare but serious disease, its incidence and risk factors. Overall, it contributed to the banishment of amphetamine-like appetite suppressants from the European market  [23]. Analyzing in details populations as large as millions of individuals and deriving robust results from observation was an unprecedented challenge. De facto, an often-forgotten fact is the contribution of pharmacoepidemiology to the development of new methods and approaches nowadays widely utilized in other fields of epidemiology and public health. Among dozens of examples, one can cite the disproportionality analyses of large databases for signal generation, the self-controlled case series (SCCS)  [24] , the case-population-approach  [25]  and, more recently, the matching and adjustment by using propensity scores or high-dimensional propensity scores  [26, 27].
USA), which has been systematically recording hospital discharge diagnoses and prescriptions on computers since 1972 [20]. In the late 80s, computers started to become rather common in general practice, notably in the United Kingdom where the VAMP Health group designed software to help general practitioners to file their patients' records. A joint effort between the VAMP and the BDCSP group led to the dream of any researcher: the first large database fed by data extracted from the routine activity of thousands of practitioners. The general practice research database (GPRD) was born and remains one of the rare examples of a tool specifically conceived for pharmacoepidemiologic research. In 2012, the GPRD became the clinical practice research datalink (CPRD) with larger ambitions and different stakeholders.