dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). In addition, the MetaPrint2D-React tool is used to compute a normalized occurrence ratio score (NOR) that reports the frequency of a reported SOM in the metabolite database. The NOR score for a given atom in a query molecule is defined as the ratio between the number of atoms identified as a reaction centre sharing a similar chemical environment with the query atom in Meta-print2D-React database and the total number of atoms (identified both as reaction centre and non-reaction centre) with a similar chemical environment in Metaprint2D-React database. Therefore, each predicted metabolite is associated with a score ranging from 0 to 1, which corresponds by extrapolation to its formation frequency. WhichCyp  (Rostkowski et al. , 2013 ) is a software predicting the selectivity for CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4.
It predicts which isoenzyme either binds or metabolizes a molecule using simple yes/no classification models. All molecules predicted by Meta-print2D-React having potentially P450-mediated transformations are used as input to WhichCyp tool to predict which CYP isoform binds to the molecule. Xenosite P450 Metabolism 1. 0  (Zaretzki et al. , 2013 ) is a software predicting SOM of a molecule for 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 3A4 CYP isoforms. All molecules predicted by Metaprint2D-React having potentially P450-mediated transformations are used as input to Xenosite P450 Metabolism 1. 0 tool. The software is used to identify SOM and allows filtering the P450-mediated reactions predicted by MetaPrint2D-React in addition to Way2Drug tool. Xenosite P450 Metabolism 1. 0 computes a probability score varying between 0 and 1 (a high probability to be a SOM is characterized by a high score).
Way2Drug SOMP  (Rudik et al. , 2015)  is a software predicting SOM of a molecule for 1A2, 2C9, 2C19, 2D6 and 3A4 CYP isoforms and UGT. All molecules predicted by Metaprint2D-React having potentially P450or/and UGT-mediated transformations are used as input to Way2Drug SOMP tool. The software computes a probability score varying between 0 and 1 (a high probability to be a SOM is characterized by a high score), which determines the probability that a labeled atom is the SOM of the appropriate enzyme. Xenosite UGT 2. 0  (Dang et al. , 2016 ) is a software predicting SOM of a molecule for UGT. All molecules predicted by Metaprint2D-React having potentially UGT-mediated transformations are used as input to Xenosite UGT 2. 0 tool. Xenosite UGT 2. 0 is used to identify SOM associated with a probability score varying between 0 and 1 (a high probability to be a SOM is characterized by a high score).
Datasets In addition to the dataset containing the 30 HAAs, three datasets were used to calibrate the workflow and validate the prediction. The HAAs dataset contains the 30 HAAs including AαC, MeIQx, and PhIP  (Ni et al. , 2008; Turesky and Le Marchand, 2011; Oz and Kaya, 2011)  (Table  1 ). They are classified in two categories that include Pyrolysis Heterocyclic Aromatic Amines and Aminoimidazoaren Heterocyclic Aromatic Amines depending on their formation temperature. The SOM calibration dataset contains 18 molecules with aromatic amines that can undergo hydroxylation and/or glucuronidation reactions (Supplementary Material Table  4 )  (Beland, 1990; Castañeda-Acosta et al. , 1999; Schut et al. , 1984; Weisburger and Weisburger, 1958; N Authors Listed, 1989; Committee on Amines, 1981; Hammons et al.
, 1985; Parkinson and Ogilvie, 2010; Kadlubar et al. , 1977; Orzechowski et al. , 1992; Butler et al. , 1989; Franklin, 1998; Frederick et al. , 1985; Morton et al. , 1980; Eiermann et al. , 1998; Hiroi et al. , 2002; Timbrell and Marrs, 2009; Alonen et al. , 2005; Jiang et al. , 2015; Baughman et al. , 2009; Domínguez-Romero et al. , 2012; Turesky, 2006) , which are the two main classes of reactions involved in HAAs metabolism. These 18 molecules are involved in 59 reactions of 5 different types (C-Hydroxylation, N-Hydroxylation, C-Glucuronidation, N-Glucuronidation and O-Glucuronidation) occurring mostly on different aromatic amines. This dataset is used to calibrate the parameters of all software tools. The DNA adduct calibration dataset contains 30 manually curated aromatic amines, that are known to form DNA adducts (Supplementary Material Table  5 ).
This dataset is used to define different thresholds for Xenosite Reactivity 2. 0 in order to classify metabolites according to their DNA reactivity. The validation dataset contains 30 known derivatives of AαC, MeIQx, and PhIP (Supplementary Material Table  6 ). This dataset is used to Fig. 2. Evaluation of filtration thresholds. (a) Analysis of the ratio between the number of predicted metabolites remaining after filtering and the number of experimentally determined metabolites, as a function of the Metaprint2d-React NOR Score. Each reaction and the corresponding metabolites of the SOM calibration dataset are predicted using basal configuration. (b) Analysis of the ratio between the number of predicted metabolites remaining after filtering and the number of experimentally determined metabolites as a function of XenositeP450 metabolism 1.
0 and Way2Drug for CYP scores; (c) Analysis of the ratio between the number of predicted metabolites remaining after filtering and the number of experimentally determined metabolites as a function of XenositeUGT 2. 0 and Way2Drug for UGT scores. In b and c, the method consists of maximizing the parameters without losing in sensitivity. The best set of parameters is the set where all metabolites present in the SOM calibration dataset are not filtered. validate the full pipeline after its calibration with the different thresholds learned thanks to the other datasets. It allows estimating the sensitivity and the precision of the pipeline.
Tool parameters optimization The calibration of the different thresholds of tools used the SOM calibration dataset, the DNA adduct calibration dataset and the validation dataset. To that goal, the Metaprint2D-React filtration procedure (based on a NOR-base threshold), the CYP filtration procedure (involving thresholds for Xenosite Metabolism 1. 0 and Way2Drug) and the UGT filtration procedure (involving thresholds for Xenosite UGT 2. 0 and Way2Drug) are applied to the datasets by parsing the complete range of thresholds available for the tools. Prediction of reactions with MetaPrint2D-React. We used MetaPrint2D-React to compute the predicted products (and their associated NOR score) for all reactions that are known to produce a compound listed in SOM calibration dataset. Our computations permitted to predict 28 of the 30 known metabolites from AαC, MeIQx and PhIP (Table  6 ).
The scores of the Way2Drug SOMP and the Xenosite UGT 2. 0 software applied to each SOM of metabolites from SOM calibration dataset and associated with a UGT-mediated transformation were computed and plotted (see Fig. 2 ). This computation confirmed that no tool is sensitive enough to discriminate the experimentally determined SOMs (some have a score equal to 0 according to one software prediction). By contrast, this analysis demonstrated that for all SOMs, the scores for Xenosite UGT Metabolism 1. 0 and Way2Drug SOMP were greater than 0. 54 and 0. 36, respectively. These parameters are the optimal ones to keep all the validated metabolites from SOM calibration dataset. Therefore, we considered that for any metabolite resulting from a UGT-mediated reaction, a site is a SOM for a glucoronidation if its Xenosite Metabolism 1.
0's score is greater than 0. 54, or its Way2Drug SOMP's score is greater than 0. 36. Prediction of SOM for P450 isoform-mediated bioproducts. The scores of the Way2Drug SOMP and the Xenosite P450 Metabolism 1. 0 software applied to each SOM of metabolites from SOM calibration dataset and associated with a P450 isoform-mediated transformation predicted by WhichCyp, were computed and plotted (see Fig. 2 ). This computation confirmed that no tool is sensitive enough to discriminate the experimentally determined SOMs. By contrast, this analysis demonstrated that for all SOM, the scores for Xenosite Metabolism P450 1. 0 and Way2Drug SOMP were greater than 0. 39 and 0. 35, respectively. These parameters are the optimal ones to keep all the metabolites of the SOM calibration dataset. Therefore, we considered that for any metabolite resulting from a P450-mediated reaction, a site is a SOM for hydroxylation if its Xenosite Metabolism 1.
0 score is greater than 0. 39, or its Way2Drug SOMP score is greater than 0. 35. DNA reactivity classification A similar approach based on the study of experimentally determined compounds was used to calibrate and evaluate the prediction of DNA adduct reactivity with Xenosite Reactivity 2. 0. To that goal, the predicted DNA reactivity (in terms of probability) of a set of 30 manually-curated aromatic amines that are known to form DNA adducts was calculated. Our computations showed that 50% of the tested metabolites from DNA adduct calibration dataset have a probability between [0. 85,1] to form DNA adducts, 20% have a probability which ranges between [0. 7,0. 85], 20% have a probability which ranges between [0. 2,0. 7] and 10% have a probability ranging between [0. 05,0. 2]. None of metabolites have a probability lower than 0.
, 2016)  and might be regarded as relevant genotoxic candidates. These predictions are in agreement with data from the International Agency for Research on Cancer which classified TrP1, TrP2, GluP2 and MeαC as probable carcinogens (class 2b). In contrast, HAAs characterized by a lower predicted genotoxicity belong to the Aminoimidazoarene Heterocylclic Aromatic Amines family suggesting that this family has a lower carcinogenicity than the pyrolytic HAAs. In order to evaluate the impact of the 5 major reactions that include Hydroxylation, Glucuronidation, Acetylation, Sulfation and Oxidation reactions on the formation of metabolites reactive with DNA, we classified the reactions and their combination according to the DNA reactivity of the metabolites induced. As shown in Fig. 7 , five classes were defined including null, very low, low, medium and high DNA reactivity.
, 2016; Schut and Snyderwine, 1999). A major result of the present work is the comparative analysis of metabolic networks predicted for the 30 HAAs. We provide a database storing the predicted metabolic graphs of all HAAs through an interactive interface EPPIGRAPH that facilitates exploration and analysis. In addition, we show that the classification of HAAs according to the predicted score of genotoxicity is correlated to their distribution in the two major classes of HAAs. While the formation of all HAAs mainly results from heating condition of meats or combustion of tobacco, the structure of HAAs depends on temperature. Jagerstad et al. 1998 defined the thermic HAAs, IQ-type HAAs (imidazoquinoline or imidazoquinoxaline) or aminoimidazoazaarenes that are formed at temperatures between 100 and 300 degree C and the pyrolytic HAAs or non-IQtype HAAs that are formed at high-temperature (> 300 degrees).
Our workflow classified thermic HAAs as lower genotoxic agents (level of genotoxicity from 37 to 45%) and pyrolytic HAAs as higher genotoxic agents (from 46 to 55%). Such data are in agreement with literature  (Gibis, 2016; Turesky and Le Marchand, 2011)  and demonstrate the robustness of the workflow thereby providing identification of potential new genotoxic derivatives. Combined with the prediction of reactivity, our approach provides hints on the nature of metabolites (intermediates or final). As an important perspective, the prediction of graphs of metabolic derivatives will provide good candidates for subtle mass spectrometry identification based on a priori knowledge of the chemical formula of the metabolic candidates. To conclude, we developed a new efficient workflow to build metabolic networks and to predict metabolites reactive with DNA.
Total RNA (1 µg) was reverse transcribed to cDNA using random hexamers and iScript reverse transcription supermix (Bio-Rad) according to the manufacturer's instructions. qRT-PCR was run with iQ SYBRGreen Supermix (Bio-Rad) in a LightCycler R 480 System apparatus (Roche). Reaction mix consisted of 1:10 diluted cDNA in 5 µl nuclease-free water, 300 nM each forward and reverse primer for il-1β (for TCTAATGCCTTC CCCAGGGC; rev GACCTGTCTTGGCCGAGGAC) and the three house keeping genes hprt1 (for CAGTCCCAGCGT CGTGATTA; rev TGGCCTCCCATCTCCTTCAT) rpl4 (for GACCAGTGCTGAGTCTTGGG; rev GTATTCACTCTG CGGTGCCA) and ppia (for GCTGGACCAAACACAAACGG; rev CCAAAGACCACATGCTTGCC) and iQ SYBRGreeen Supermix (Bio-Rad) in a total reaction volume of 15 µl. After 45 cycles of amplification 45 cycles (95 • C 5 s; 62 • C 5 s) quantification was performed with Bio-Rad Laboratories CFX Manager software.
Statistical Analysis Data were expressed as arithmetic mean ± standard error of the mean (SEM). Statistical analyses were performed with Prism 4. 0 software (GraphPad) and R software (3. 4. 1 version, Rcmd pluging). Analyzes were performed on data from 2 to 5 independent experiments. Paired non-parametric two-tailed K-Sample Fisher-Pitman Permutation test was used to analyse data, with a Monte Carlo resampling approximation, except for Figure  4E  where Mann-Whitney t-test was applied. Represented p-values are: * p < 0. 05; * * p < 0. 01, and * * * p < 0. 001.
ACKNOWLEDGMENTS We warmly thank all members of the  Plate Forme d'Infectiologie Experimentale  ( UE N • 1277 ) mouse facility from the  INRA Val de Loire Center  for their expertise and kindness. We are grateful to  Alain Bessis  ( Ecole Nationale Supérieure, Paris, France ) for the generous gift of itgam -/-mice. We also warmly thank  Agnès Coste  ( Université de Toulouse, France ) for sending bones from clec7a -/-mice and littermate controls as well as  Attila Mócsai  and  Tamás Németh  ( University School of Medicine, Budapest, Hungary ) for the shipment of bones from MRP8-Cre + Syk flox/flox and LysM-Cre + Syk flox/flox mice and littermate.
FUNDING This work was supported by grants from the  French Agence Nationale pour la Recherche  (No. ANR-2011-BSV3-0001 PGLECT ) and  Région Centre Val de Loire  (No. 32000584   Inflammation et Infection ). AA was recipient of a fellowship from  European Marie-Slodowska-Curie Actions  ( PIEF-GA-2012-329818 ) and AR by a grant from the  Agence Nationale de la Recherche  under the  Carnot Program France Future Elevage.
Results. -At-risk prescribing of NSAIDs in patients treated by diuretics or renin-angiotensin system inhibitors always remained higher than 14% over the study (maximum: 19%; 2014 quarter 4: 15. 4%). Off-label prescribing of contraindicated anti-dopaminergic antiemetics with dopaminergic antiparkinson drugs was marginal (maximum: 2. 2%; 2014 quarter 4: 0. 5%) but represented at least 5. 5% of antiemetic prescriptions. Despite the rise in antipsychotic prescriptions in pediatrics, no dramatic increase in misuse related to age was observed during the study period (2007 quarter 1: 16. 1%; 2014 quarter 4: 11. 1%). The highest degree of misuse was observed for aripiprazole and for second-generation antipsychotics other than risperidone and aripiprazole. Conclusion. -This study provides a simple tool to monitor drug misuse or potential misuse using information from a health insurance database.
Introduction Drug misuse implies drug use for a purpose out of the scope defined by the summary of the product characteristics (SmPC)  [1]. For example, fenfluramine-phentermine-related valvular heart damage mostly occurred in off-label contexts of use  [2, 3] , while the more recent dramatic epidemic of opiate addiction was also partially related to off-label use  [4, 5]. To ensure that a drug has a positive impact on population health, the Regulator must ensure that drug misuse remains limited and that drug use globally complies with marketing authorizations and guidelines, specially when it involves adverse risks. Healthcare reimbursement databases provide an opportunity to perform such a monitoring program of prescribing practices  [6]. Such an overarching view could allow the early detection of emerging trends in drug misuse and provide information that would help the Regulator implement specific risk minimization strategies.
The French healthcare insurance system has been previously used for assessing drug abuse and dependence; for instance, indicators have been developed to detect ''doctor-shopping'' or ''medical nomadism''  [7]. These databases could as well be helpful outside the scope of drug addiction, but previous studies have been restricted to a defined geographical area  [8]. This nationwide study aimed at assessing and monitoring specific drug misuse or potential misuse selected on the basis of associated risks or the targeting of frail populations. It focused on drugs used widely and on others taken more marginally. The following drugs were studied: non-steroidal anti-inflammatory drugs, antiemetics in Parkinson's patients, antipsychotics in pediatrics, direct anticoagulant agents, asthma and oral antidiabetic drugs.
Data source The study was conducted using a French reimbursement database (Échantillon généraliste de bénéficiaires  [EGB] ). The EGB is a 1/97th representative sample of the population covered by the national healthcare insurance system (approximately 90% of the whole population, irrespective of socio-economic status) obtained by random sampling. The database consists of the anonymous and exhaustive recording of all reimbursements of patients' health expenditure, including drugs. It also contains hospital discharge summaries with diagnoses and dates, and registration status for a list of 30 severe chronic diseases eligible for full reimbursement of healthcare. Indications for prescribing, doses prescribed and the duration of treatment are not available in the database. Details on the EGB database have been described elsewhere  [9].
Modele + THERAP 320 1-8 Monitoring of drug misuse in a claims database 3 January 1st 2007 to December 31st 2014. Drug misuse is defined as ''situations where a medicinal product is intentionally and inappropriately used not in accordance with the terms of the marketing authorization''  [10]. This definition includes use of drugs that are prescribed outside the terms of the SmPC, e. g. for an unapproved age group; it also includes use of drugs prescribed in circumstances increasing the risk of adverse drug reactions, such as drug-drug interactions (related to concurrent medications) and drug-disease interactions (related to comorbidities). Potential misuse was considered for drugs used in conditions for which misuse can have occurred, but cannot be ascertained from the information available in the database; the interest of monitoring drug potential misuses resides in the monitoring of situations of at-risk prescribing it permits.
Antipsychotics in children: drug use associated with a non-assessed benefit-risk balance in pediatrics Most of the first-and second-generation antipsychotics have received marketing authorization in youths aged less than 18 years, with a varying minimum authorized age. To date, knowledge about the safety and effectiveness of antipsychotic use in children is limited. However, antipsychotic use in children has increased substantially  [15] , thus raising concerns given their known metabolic and cognitive adverse effects  [16, 17]. For each approved antipsychotic in France during the study period, we determined whether the SmPC stipulated use in children and, if so, the minimum authorized age. For antipsychotics not recommended for use in children and adolescents due to a lack of data to support use in pediatrics, we set the minimum age at 18 years except if this was otherwise specified in the SmPC (e.
Data analysis For each drug misuse or potential misuse criterion investigated and each quarter considered, both the prevalence of use of the drugs of interest among the source population and the prevalence of drug misuse among the corresponding study population (see definition of criteria) were estimated. Subjects from the study population and users of the misused or potential misused drug were characterized in terms of age (median and interquartile range, IQR) and sex for all quarters. All quarterly-estimated prevalence rates were quantified together with their two-sided 95% confidence interval (95%CI). All analyses were performed using SAS ® software (SAS Institute, version 9. 4, North Carolina, USA).
Discussion and conclusion Drug misuse refers to drug use in situations where the benefit-risk balance may be undetermined or negative, and the monitoring of prescribing practices is a major challenge. This study based on a representative reimbursement healthcare database demonstrates that a tool to monitor prescribing compliance with marketing authorizations could be implemented. It could assess the number of patients meeting defined drug misuse or potential misuse criteria within each period in a database and monitored over time. We found that at-risk prescribing of NSAIDs in patients treated by diuretics or RASIs tended to improve over the study period, although the volume remained high. Diuretics and RASIs were widely used and prescribing of NSAIDs concerned nearly 16% of patients treated by diuretics or RASIs in 2014.
The contraindication information for patients with Parkinson's disease included in the SmPC of these drugs should therefore be made more explicit. The results are also reassuring regarding antipsychotic use in children. Despite the rising rates of antipsychotic prescription in youths, no dramatic increase in misuse related to age was observed. Risperidone and cyamemazine were prescribed predominantly but misuse related to age was infrequent. Indeed, the highest degree of misuse was observed for aripiprazole and for second-generation antipsychotics other than risperidone and aripiprazole. These findings plead in favor of continuing to monitor prescribing practice for these drugs and conducting research aiming at better understanding in which conditions they are prescribed. This would require precise medical data that are not provided in the EGB database.
If it was however, the size of the EGB database would not allow much more insight for these drugs, despite the fact that Monitoring of drug misuse in a claims database 7 this database is representative of the French population. Also, the prevalence of use (and potentially of misuse) of antipsychotics appeared higher in boys than in girls; this is mostly consistent with the higher prevalence of antipsychotic indications in boys compared to girls  [15]. As these drugs are not commonly used, quarterly variations in drug misuse should be interpreted with caution owing to the small number of users. In such settings of research, i. e. for drugs associated with a lower prevalence of use, the approach and database we used provide less conclusive results regarding the assessment of drug misuse. The study has several limitations.
As we used reimbursement data, we cannot be sure that individuals to whom drugs were reimbursed all used them in the period following the date of reimbursement; the actual use of drugs and patterns of use can be obtained only directly from patients but surveys based on collected data are limited in terms of sample size and recall bias. Our aim was not to perform an exhaustive assessment of the misuse of the drugs studied and the analysis did not consider dosage, frequency, route of administration, or duration of treatment. However, the database allows envisioning modifying the definitions used across time and the choices that we made in defining drug misuse should not be considered definitive, so other criteria could be included. Finally, while the definition of misuse will always be an issue for debate in drug monitoring, it is essential to use a database that is fully representative of the drugs studied and can ensure such representativeness over time.
Acknowledgments We are grateful to the DRUGS-2M study group participants for their cooperation:  Abdelilah Abouelfath ,  Marie-Agnès Bernard ,  Jérôme Dupont ,  Ana Jarne ,  Jérémy Jové ,  Clémentine Lacueille ,  Simon Lorrain ,  Yohann Mansiaux ,  Arnaud Panes ,  Romain Sylvestre.