dataesr/scientific-paragraphs-categorization huggingface dataset:
Scanning of microarrays was performed with 3 μm resolution using a G2565CA high-resolution laser microarray scanner (Agilent Technologies). Microarray image data were processed with the Image Analysis/Feature Extraction software G2567AA v. A. 11. 5. 1. 1 (Agilent Technologies) using default settings and the GE2_1105_Oct12 extraction protocol. The extracted single-color raw data txt files were analyzed using R and the associated BioConductor limma R package  77, 78  for differential expression analysis. The data set was background corrected and normalized using loess method. Microarray data were deposited in the NCBI's Gene Expression Omnibus (GEO, accession number GSE99901). We used the lmFit function to fit a linear model which included the factors stimulus type (Lawsone and Pam2CSK4) and treatment (stimulated/control) as well as an interaction term.
The p-values were calculated based on moderated t statistics and most differentially regulated genes were retrieved with topTable function from limma package. Genes associated with AhR and Nrf2 activation or keratinocyte differentiation were manually chosen on the basis of literature, and three custom gene lists were created: AhR dependent-genes (Table  1 ), Nrf2-related genes (Table  2 ) and EDC-keratin genes (Table  3 ). Gene set enrichment analysis was performed and visualized using R-package tmod for analysis of transcriptional modules  79. In the first step, CERNO statistical test was applied to the list of genes contained in the linear fit model with tmodLimmaTest function. Next, a ROC curve was plotted for the respective modules using evidencePlot function from tmod package  29, 78.
Genes presenting highest influence on the module enrichment were identified and labeled on the ROC curve. Statistical script in R including all steps of the microarray analysis can be obtained by request. Ingenuity Pathway Analysis (https://www. qiagenbioinformatics. com/products/ingenuity-pathway-analysis/, version 33559992) was performed to identify the top canonical pathways differentially regulated upon 4 h stimulation of HEK cells with Lawsone (10 µM) as compared to DMSO. Pathway analysis was performed using log2 fold changes and p-values obtained from comparisons between the different stimuli. statistical analysis. Statistical analysis was performed with GraphPad Prism v7. 03 (GraphPad software Inc. , USA). P-values were calculated using student's t-test, One-Way or Two-Way ANOVA as stated for each experiment.
Acknowledgements The authors thank  Mary Louise Grossman  and  Souraya Sibaei  for excellent editorial assistance, and  Philippe Saikali  for technical help ( Department of Immunology, Max Planck Institute for Infection Biology, Charitéplatz 1, D-10117 Berlin, Germany ). We also thank  Sabrina Hadam ,  Eva Katharina Barbosa Pfannes  and  Annika Vogt  for fruitful discussions ( Department of Dermatology and Allergy, Clinical Research Center for Hair and Skin Science, Charité-Universitätsmedizin Berlin, Germany ). We acknowledge  Anne Diehl  ( Leibniz-Forschungsinstitut fuer Molekulare Pharmakologie, Berlin, Germany ) for mouse liver protein preparation. The zebrafish mpeg. mCherryCAAXSH378 mpx:GFP i114 line was kindly provided by  Dr. Stephen Renshaw ,  The University of Sheffield, UK  (http://www.
EPR Spectroscopy Experiments The experiments were conducted with purified enzyme NQO2 (20 µg/mL), DMPO (125 mM), tested compounds (125 µM) and BNAH (1 mM). For inhibition assays, we added S29434 (30 µM). All analyses were performed at room temperature. EPR spectra were recorded with a Bruker EMX-8/2. 7 (9. 86 GHz) with a high-sensitivity cavity (4119/HS 0205) and a gaussmeter (Bruker, Wissembourg, France), at X-band. For analyses, we used a flat quartz cell, FZKI160-5 × 0. 3 mm (Magnettech, Berlin, Germany). We performed data processing and spectrum acquisition with WINEPR software.
EPR Spectroscopy Experiments The experiments were conducted with purified enzyme NQO2 (20 µg/mL), DMPO (125 mM), tested compounds (125 µM) and BNAH (1 mM). For inhibition assays, we added S29434 (30 µM). All analyses were performed at room temperature. EPR spectra were recorded with a Bruker EMX-8/2. 7 (9. 86 GHz) with a high-sensitivity cavity (4119/HS 0205) and a gaussmeter (Bruker, Wissembourg, France), at X-band. For analyses, we used a flat quartz cell, FZKI160-5 × 0. 3 mm (Magnettech, Berlin, Germany). We performed data processing and spectrum acquisition with WINEPR software (Bruker). Scanning parameters were: Number of scans, 5; magnetic field, 3460-3560 G; sweep width, 100 G; scan rate, 1. 2 G/s; modulation amplitude, 1 G; sweep time, 83. 88 s; time constant, 40. 96 ms; modulation frequency, 100 kHz; and microwave power, 20 mW.
Negative controls were prepared with a suspension of iRBCs and 0. 5% DMSO. Chloroquine was used as the positive control. Test plates were incubated at 37 • C for 48 h. Afterwards, 100 µL SYBR Green I fluorescent lysis buffer were added to each well and incubated in a dark place at room temperature for 2 h. Fluorescence data were acquired using a fluorescence plate reader (BMG Fluostar Galaxy Labtech) with excitation and emission wavelengths at 485 nm and 518 nm, respectively. The fluorescence values (after subtraction of the background fluorescence of the non-parasitized RBCs) were plotted against the log of the drug concentration, and analyzed by non-linear regression (sigmoidal dose response/variable slope equation) to yield the IC 50 (50% inhibitory concentration) that served as a measure of the anti-malarial activity.
5 mg/mL in RPMI) was then added to each well. Cells were incubated for 2 h at 37 • C and 5% CO 2. After this time, the MTT solution was removed and DMSO (100 µL) was added to dissolve the resulting formazan crystals. Plates were shaken vigorously (300 rpm) for 5 min. The absorbance was measured at 570 nm with a microplate spectrophotometer (Eon BioTek). DMSO was used as blank and doxorubicin (purchased from Sigma Aldrich) as positive control. CC 50 were calculated by non-linear regression analysis processed on dose response curves, using TableCurve 2D V5 software (San Jose, CA, USA). CC 50 values represent the mean value calculated from three independent experiments.
Acknowledgements We thank  ANR DynBioReact  (Grant No. ANR-14-CE06-0029-01 ),  CNRS program INFINITI  (project  ASTROCOL ), project  N°2013-0562-T ,  Chiraux IRUV (RTRA)  for financial support and Project  AYA2017-87515-P  from the  Government of Spain. R. S. thanks  Universidad de Valladolid  for a visiting professor fellowship. The authors thank  J. M. Ortega , and the  CLIO  and  SMAS  staff for the useful assistance during the experiment at the  FEL (Free Electron Laser)  and  CLIO (Centre Laser Infrarouge d'Orsay)  facilities in Orsay. The authors appreciate fruitful discussions with  P. Maître  and  M. Mostafavi.
Numerical modelling The FEM modelling was performed using Abaqus® software (Abaqus® Standard 6. 13, Dassault Systèmes, Vélizy-Villacoublay,France). The mechanical behavior of the powder was modeled using Drucker-Prager Cap model as generally performed in the literature  (Brewin et al. , 2007; Cunningham et al. , 2004; Diarra et al. , 2012; Michrafy et al. , 2002; Wu et al. , 2005). The parameters were characteristic for aCP and were taken from a previous publication  (Diarra et al. , 2015). The press was numerically represented by a die and two punches, and these materials were considered as deformable with elastic properties of steel (E= 220GPa and ν=0. 3). Exact simulation setup will be detailed below.
Interestingly, the present data tend to demonstrate that the transition from solid-like to micelle-like particles with polymer composition is abrupt and similar to already reported data for comb-polymers. (Rabanel et al. , 2015a)  However, these differences in structure did not affect the encapsulation of the active compound due to the strong lipophilic character of the latter (logP = 7. 5). Drug loading and drug yield were found independent of the polymer composition (Table  2 ). In addition, DSC analysis was performed to identify the physical state of the drug according to the polymer used. No fusion peak appeared in the DSC thermograms suggesting that cholecalciferol is present in an amorphous state in all the suspensions (Figure  S5 , SI).
Acknowledgments AL is grateful to the  Faculty of Pharmacy of Université de Montréal  for their financial support and to the  Région Auvergne-Rhône-Alpes  for their mobility grant ( Explo'RA Doc ) and. JF is grateful to the  Arthritis Society  and the  University of Montreal  for PhD grants. AGA is supported by the  TransMEdTech Institute. PH is thankful for the financial support of  FRQNT. XB acknowledges the financial support from the  Canada Research Chair Program  and  NSERC.
Introduction All drugs are dangerous, some may also be useful  [1]  All is poison, nothing is poison, the dose is what separates the drug from the poison  [2]. In an earlier paper, we retraced the history of pharmacovigilance, with some views to its future  [3]. Five years later, the world has changed a little. In addition to traditional spontaneous reporting by healthcare professionals, patient reporting has become mainstream. The use of social media has exploded, and is being mined in the hope of possibly identifying new safety issues  [4]. Data resources such as countrywide healthcare systems databases have become readily available, and hospital based data repositories or electronic health records are opened new possibilities. The objective of the present paper is to review these advances and try again to predict the future, the next chapter in pharmacovigilance.
After previous chapters of spontaneous reporting, analysis of individual case reports, reporting patterns and report databases, will the next chapter be that of big data and pharmacoepidemiology in pharmacovigilance? To set the frame of the reflection, the management of drug safety, or pharmacovigilance, follows a series of steps: identification of a signal, ascertainment of the signal, which then becomes an alert; quantification of the alert, which can become an alarm, and management of the alarm, which more or less follow the usual clinical management of disease: suspicion (symptoms), diagnosis (confirmation), evaluation (extent and severity), and treatment (followed by measure of the treatment's success). Each of these steps require different methods and proficiencies, just as there is no single exam or test that will do all in clinical medicine (Fig.
1 ). These different steps can be put in relation with the methods cited: a) spontaneous reporting and evaluation of individual case reports; b) analysis of reporting patterns and of spontaneous reporting databases; c) use of population-based resources. This paper is based on the science of pharmacovigilance, the identification and prevention of adverse drug reactions, as a medical expertise, that of drug-induced diseases. Pharmacovigilance has become very much a regulatory issue, but this paper will not discuss the relevance of regulations or definitions, of International Council for Harmonisation of Technical Requirements for Pharmaceutical Use (ICH) section clinical safety data management: data elements for transmission of individual case reports (E2B), individual case safety reports (ICSR) or medical dictionary for drug regulatory activities (MedDRA), Food and Drug Administration (FDA) or European Medicines Agency (EMA).
Spontaneous reporting The history of drug safety was changed when in 1961 McBride published in the Lancet a short letter stating "in recent months I have observed that the incidence of multiple severe abnormalities in babies delivered of women who were given the drug thalidomide during pregnancy as an antiemetic of a sedative, to be about 20%. /. Have any of your readers seen similar abnormalities in babies delivered of women who have taken this drug during pregnancy?" [5]  This led to the understanding of the usefulness of the reporting of adverse reactions by observant professionals, the creation of national and international adverse drug reaction (ADR) reporting systems  [6] , and the concept of pharmacovigilance  [7]  At the time there were no population databases, and very little computing power.
Individual case reports Individual case reports describe what happened to a patient in relation to a drug. The standard report is based on an identified reporter, an identified patient, a drug and an event  [9] [10] [11]  That is the regulatory basic dataset, which is unfortunately essentially useless for assessment purposes, when limited to this minimal information set. For an event to be really evaluable for an association with a given drug in a given patient, what Jeff Aronson calls "anecdotes as evidence"  [12]  one needs to know patient characteristics (age, sex, concomitant disorders, concomitant medication, genetic susceptibilities), drug characteristics (dose, duration of treatment, indication for use, dechallenge and rechallenge [if any]), and for the event, the start date (including of first symptoms, not just of diagnosis), the diagnostic certainty (including relevant lab tests) and the exclusion of other potential causes for the event, with dates in relation to possible dechallenge and rechallenge  [12] [13] [14].
In addition, new causes may appear over time, so that a report with no other identifiable cause than the drug of interest might well result from another cause that will appear in due time. The data items needed to assess individual case reports have been collated in recommendations for datasets in published reports  [13]. These different data items can be combined and used in many different ways, feeding algorithms, A classic one is the French Imputability model, where data is fed into a 3-way table  (Table 1 ) to result in partial and global imputability scores  [6, 15, 40]. Bayesian approaches or scores, or computer assisted probabilities  [17-19, 29, 36, 41]. There are also event specific causality approaches such as Roussel Uclaf causality assessment method (RUCAM) for drug-induced liver injury  [33, [42] [43] [44] , or similar applications for skin reactions  [45] , haemolytic anaemia  [46].
Event masking by other drug-event associations has also been described, even in the absence of interactions  [58]. The choice of the comparator can also be crucial. This can be the whole database, which is probably not appropriate if risk factors for the event are not equally distributed in the whole population, or the comparators may be drugs used in similar indications, or other drugs in the same family. If possible, these should be drugs marketed at the same time, to avoid distortion by the Weber effect, and in the absence of any identified alert, concerning the drug of interest or the comparator  [56]. These biases need careful consideration, and are open to manipulation: increasing the reporting of non-specific (even serious) or well-known reactions will increase "b", thus masking the emergence of new signals.
And it might still be the case when there is a flurry of rare serious events known to be drug-related, strongly associated with a single new drug entity. In other cases, when the events are rare and may have several causes, and the drugs have been long on the market, no decision should be made without a careful analysis of population data and pharmacoepidemiology. The management of identified problems falls under the chapter of risk management, which cannot and should not rely only on spontaneous reporting of individual reactions, but on systematic evaluation using the resources available for population analytics.
Primary data collection In primary data collection, studies are devised ad hoc, much as in clinical trials, and specific information, such as quality of life or blood samples for drug concentrations, or deoxyribonucleic acid (DNA) samples can be acquired. Field studies may be obligatory when the data needed is not in the claims or EHR databases, such as the side of an ocular injection, or the presence of lifestyle characteristics, or again the reasons for which a drug may have been stopped. Because these studies involve contact with patients and the generation of primary data, they are subject to patient safety requirements and informed consent. The rules for reporting adverse events will also not be the same as for secondary data (EMA good pharmacovigilance practices chapter 6, www. encepp.
eu). Studies may also combine data from an ad-hoc field study and claims databases, either directly where patients are identified, characterized and recruited by prescribers, but then followed in claims databases  [69] [70] [71] [72] , or indirectly, by verifying in the field study potential association of confounders with prescribing. In the absence of an association (e. g. a drug is not preferentially prescribed in smokers), then that potential confounder is just a risk modifier and can be neglected in database studies.
Secondary sources of data In secondary data sources, all the data is already present at the time of the study, and it would generally not be possible to enrich the dataset, though new developments in clinical data repositories might change this in the near future. These data sources might be -Claims databases (e. g. système national des données de santé (French national health data system -SNDS], Medicare,…)  [73]  Claims databases contain a recording of all healthcare encounters that are covered by the healthcare system or insurance company. This may include outpatient medical consultations, drugs or devices dispensed, lab test or imaging, paramedical interventions, but also hospital admissions including diagnoses and procedures. In some countries there are outpatient diagnoses, or records of chronic conditions.
The data in claims databases are collected prospectively and concern all the information for all the patients covered by the healthcare system. This might be lifelong for the whole population as in France or in Nordic Countries, or limited to specific areas (in Germany, Italy or Canada), or to specific ages, social status and resources (as in the US), which may limit the usefulness or representativeness of such claims databases  [74, 75]. These data might be linked to specific registries such as cancer, diabetes or rare disease registries, or death registries including or not its cause. The advantage of the claims databases is that the data are collected systematically and prospectively, independently from any specific study, which avoids a number of potential biases. Depending on the data sources, they may contain very diverse information allowing for precise information on various potential confounders.
For instance, if they do not contain such lifestyle information as smoking or body mass index (BMI), they do contain information on their medical consequences, such as chronic bronchitis, sinus infections, including use of antibiotics, peripheral arterial disease, tobacco cessation aids or devices, specialist consultations, etc. and BMI may be related to diabetes (identified directly or through its treatment), osteoarthritis and interventions such as knee or hip replacement, and the use of drugs in these indication, but also bariatric surgery and other interventions related to obesity. All these variables can be included in modern statistical analyses such as high-dimensional propensity scores and disease risk scores  [76, 77]. -Electronic health records Databases such as clinical practice research datalink (CPRD) are based on the voluntary recording by participating physicians of the clinical details of the patients they follow.
This will include outpatient diagnoses and prescriptions (not dispensing), results of lab tests and other exams (if entered) and results of specialist visits, or hospital discharge summaries, as well as lifestyle characteristics. The quality and completeness of the data depends on the heath care professional's input. Ideally this will be done by a healthcare records management software, the data being transmitted to the database after anonymization. In this case the data will actually be used for patient management. Quality and completeness of the data needs to be regularly verified, and missing data may be an issue. The completeness of data may also be an issue and depend on the healthcare system. If the general practitioner (GP) is the overall curator of all the patient's healthcare, the data may be presumed complete, though hospital data may not always be fully transcribed, nor might some specialist visits  [78].
-A third approach to secondary use of data is the concept of chart review, where patient files are examined for the presence of specific events such as indicators of cancer progression, which will usually not be found in the claims data  [79-81. Chart reviews may concern patients treated with specific drugs, or recorded drug exposure before events such as liver transplantation  [82, 83]. Finally different data sources may be combined in datahubs, that would combine information for claims databases and from clinical records, in-hospital or outpatient, as well as data from registries, including results of lab tests or description of DNA sequencing or target information, as well as information from the emerging wearable devices  [84].
Exposure-based methods In this approach, new users of a drug will be included in a cohort, and followed for ulterior events. This would traditionally be done in field studies, using so-called registries (which are not), or phase-IV studies. In such studies with primary data collections, new users of a new drug are identified and described for factors associated with the prescription, including lifestyle factors, and followed for common events, such as drug cessation and the reasons thereof, or common adverse reactions. Except in very high-risk patients such as in cancerology, the event rates for serious events would be too low to allow quantification, in these studies of limited size. They may however provide invaluable information on less-serious common events that would not warrant hospitalization, and would not be captured in claims databases.
On the other hand, using the very large claims databases will provide for the detection and follow-up of the very first users of the drug, describe these, and how use might change over time. They will provide event rates for events resulting in hospital admissions (serious adverse reactions), or that may have therapeutic markers (such as the use of antidepressant drugs to identify depression) using prescription symmetry analysis  [85] [86] [87]. These cohort studies can be very large, and will provide unbiased whole population event rates  [88]. These event rates may be considered in the absolute, for instance in the absence of nondrug related occurrences of the event, or be compared to those observed with other drugs with the same indication. In that case, the same methods used for comparative effectiveness studies can be applied.
Matching The comparability of cohort groups may be enhanced by matching on variables known to be predictive of the outcome, and also associated with the use of the drugs of interest. These variables will be the most likely to be confounding variables, or to be associated with confounding variables. Presently the most extreme matching methods use high-dimensional propensity scores (hdPS), which are determined with several hundred variables among the thousands present in the datasets. They result in groups that are identical or very close on all measured variables, including those that are not part of the hdPS itself  [76, 77, [91] [92] [93]. Among the benefits of these new users cohort studies are the possible comparisons with pivotal clinical trials for outcomes that were identified in these trials, including efficacy outcomes so that the applicability and representativeness of these trials can be appreciated  [94].