dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We reported that ARF is respectively 5. 7 and 3. 3 more frequently reported with febuxostat and allopurinol than with other drugs. Due to the potential consequences of AKI in terms of mortality and renal impairment  [36, 54] , physicians should take the present signal into account when prescribing febuxostat or allopurinol. Pharmacovigilance databases are very valuable tools for post-marketing surveillance and can be also used to monitor treatment effectiveness in phase IV clinical studies  [55]. Our present results pave the way for randomized phase IV trials designed to assess the likely causal relationship between AKI and febuxostat or allopurinol.
Acknowledgements The authors acknowledge the  Uppsala Monitoring Centre (UMC)  which provided and gave permission to use the data analyzed in this present study. The authors are also grateful to the National Pharmacovigilance Centers which contributed to data. It must be borne in mind that the interpretation of data in the present study represents the opinions of the authors only and not those of the  WHO or ANSM (Agence Nationale de Sécurité du Médicament et des produits de santé, France).
Introduction Cruzain is the major papain-like cysteine protease of Trypanosoma cruzi, the protozoan responsible for Chagas disease. This enzyme is indispensable for the survival and propagation of the parasite and, therefore, is considered a potential drug target for the disease control  [1] [2] [3]. Toxicity and inefficiency of the available chemotherapy  [4] [5] [6] , fueled the pursuit for alternative drugs, which in turn, has led to the discovery of many cruzain inhibitors. A tangible evidence of the latter, is the occurrence of twenty-five crystal structures of cruzain in complex with different competitive inhibitors in the Protein Data Bank (PDB)  [1, [7] [8] [9]. On the other hand, numerous experimental studies and computational predictions have been performed to characterize the cruzain binding site and specificity, facilitating the design of active-site directed drugs  [8] [9] [10] [11] [12].
Virtual screening 298 709 compounds from the files 21_p0. 0. sdf and 21_p0. 1. sdf contained in Lead Now library of ZINC database (http://www. zinc. org, accessed on March, 30 th , 2016) were filtered in order to eliminate the potentially reactive and non-lead compounds. After this step, the protonation states of the remaining 209 422 compounds were defined according to the following criteria: i) all basic groups were protonated and ii) all acid groups were deprotonated. For simplicity, no tautomers were included in our compound library. Subsequently, each compound was parametrized using the MMFF94 force field and subjected to energy minimization (EM) until convergence, defined by an energy gradient tolerance of 0. 05 kcal Å -1 mol -1. All previous steps were conducted with MOE platform  [54].
Finally, the compound database was converted into pdbqt format with AutoDockTools  [55]. VSs against the three representative structures of each selected allosteric site (Fig 2  , step  3) were carried out in parallel with AutoDock Vina v1. 12 software  [56]. The number of generated poses was set to ten and docking simulation were initiated using a random seed. The exhaustiveness of the search and energy range between the best and the worst binding modes were set to the default values (8 and 3 kcal/mol, respectively) The grid box was defined according to the size of each cavity (S1 Fig), using AutoDockTools  [55]. Twenty complexes with the highest Vina-score pose (S vina ) were selected per representative structure, thus generating 60 preliminary hits for each cavity (Fig 2  , step 3 ). Subsequently, all these complexes were subjected to short MD simulations in order to re-score binding poses using Molecular Mechanics Generalized Born Surface Area (MM-GBSA) free energy calculation (Fig 2  , step 4 ).
Ligand parametrization Ligand parametrization was required in order to perform MD simulations of cruzain complexes. A less accurate parametrization using the AM1-BCC method  [57]  implemented in Antechamber was used for re-ranking the compounds selected from VS (Fig 2  , step 4 ). After this intermediate rescoring step, a most rigorous charge derivation was conducted for the most promising compounds (e. g. , top 5 compounds). The compounds selected for long simulations (Fig 2  , step 6 ) underwent steepest descents EM until gradient convergence with the Universal Force Field (UFF) parameters using Avogadro  [58]. The minimized structures were subsequently subjected to geometry optimization at B3LYP/6-31G(d,p) level using Gaussian 09 package  [59]. The electrostatic potential (ESP) for each optimized structure was then generated by single-point (SP) calculations using the HF/6-31G(d) method and Merz-Kollman (MK) scheme  [60].
Protein structures retrieval and systems setup and minimization Twenty-five tridimensional (3D) structures of cruzain in complex with competitive inhibitors were obtained from Protein Data Bank (PDB)  [63]. Subsequently, all cruzain structures (including each protein chain reported in each PDB file) were compared with each other through the calculation of pairwise Root Mean Square Deviation (RMSD) of the backbone atoms. This analysis was performed in order to find structures with highest structural differences after removing heteroatoms (Fig 2  , step 1 ). Other parameters taken into account for the selection of cruzain structures were the experimental resolution (<2. 5 Å) and their usage in In the first step, five cruzain apo structures were selected and subjected to 200 ns of MD simulations.
EM was performed with pmemd. MPI program of Amber14 package  [61]. This procedure was conducted in two steps: (i) solute atoms restrained with a harmonic constant of 10. 0 kcal�mol -1 �Å -2 , and (ii) unrestrained system. In both cases, 1500 steps of steepest descent followed by 500 steps of conjugated gradient EM were performed. To study the potential impact of the allosteric ligands on the functionality of the orthosteric site in terms of affinity, MD simulations with a mimetic substrate of cruzain were performed. Cruzain-peptide complexes were built following the strategy reported by Durrat et al. [11] , i. e. , by superimposing the average structure from MD simulations of the cruzain apo form with the crystal structure of human procathepsin K (PDB: 1BY8). According to the available information on specificity of cruzain binding site, the propeptide of procathepsin K was mutated using the mutagenesis plugin of Pymol v1.
A time step of 2 fs was used for numerical integration of the Cartesian equation of motion and coordinate files were recorded every 20 ps. For cruzain apo-form, 200 ns of MD simulations were performed for each of five different structures selected in pairwise-RMSD analysis. Besides, the complexes involving the top-scoring ligands from the Vina-score ranking list were selected for shorter 10 ns MD simulations in complex with cruzain (Fig 2  , step 4 ). Finally, five independent 200 ns production runs were carried out for each complex selected from MM-GBSA results (Fig 2  , step 6 ). The pmemd. cuda program  [74]  was employed to run the production simulations in identical conditions to those used during NPT equilibration step but without restraints.
Trajectories analysis The cpptraj module of Ambertools14 package  [61, 78]  was used to determine trajectory parameters such as RMSD, RMSF, average structure, interatomic distances and hydrogen bonds. The calculation of RMSD values for side chain and backbone atoms was performed employing the snapshots from production runs with respect to corresponding starting frame. Hydrogen bonds established between each ligand and cruzain were calculated with the default geometric definition of cpptraj. This geometric criteria establish a distance cutoff of 3. 0 Å between acceptor and donor heavy atom, and the acceptor-hydrogen-donor angle must be � 135˚  [61, 78]. Figures of protein cavities density and complexes were generated with Pymol v1. 8 program  [67].
Binding free energy calculations MM-GBSA and MM-PBSA are computationally efficient methods for estimating the binding free energy (ΔG bind ) of protein-ligand complexes  [79] [80] [81] [82]. Here, the effective free energy (ΔG eff , neglecting the configurational entropic contribution) values of all protein-ligand complexes were calculated using the MMPBSA. py program  [83]. The single-trajectory approximation was used for this calculation, employing the MD simulations corresponding to the 60 complexes selected per cavity (Fig 2  , step 4 ). The GB-neck2 model (igb = 8) with mbondi3 radii was used for estimating the polar solvation energy (ΔG GB )  [61]. The ΔG eff values were averaged over all snapshots, thus generating a new score that allowed us to obtain a re-ranked list of the most promising candidates (Fig 2  , step 5 ).
Coordination propensity calculation The calculation of coordination propensity (CP) coefficient was performed with home-made scripts using the formula reported in previous works (Eq 1)  [87, 88]. This methodology is based on the construction of a matrix whose elements, termed coordination propensity parameters, are calculated as follows: CP i;j ¼ hðd i;j À hd i;j iÞ 2 i ð1Þ where d i, j is the distance between the Cα atoms of residues i and j in each snapshot and the brackets indicate the average distance over the trajectory. The CP matrices obtained from independent replicas were averaged for each system. The values obtained for the apo form were subtracted from those obtained for the holo form ðhCP apo i À hCP holo iÞ and reported for each complex.
In this work, the network construction and the processing of GC and contact map matrices were made with Bio3d package  [90] , employing the methodology proposed by Sethi et al. [53]. The distance used for contact maps calculation was 5. 0 Å, as previously used in other studies  [91] , in which it produced the smallest number of communities for the studied systems. The cut-off of GC value taken for network building was 0. 5. For every system, the pairwise product between the average GC and contact map matrices corresponding to each simulation replica ðhGC ij i � hCmap ij iÞ was used to generate the final average matrix employed in network community calculations. The graph community structure was built using the Girvan-Newman clustering method, which is essentially based on the edge betweenness partition criterion  [92].
Construction of energy interaction networks based on cruzain ensembles from MD simulations Protein energy networks (PENs) are a specific case within the protein structural networks (PSNs) approach  [48, [95] [96] [97] [98] [99] [100]. PENs construction is based on the calculation of pairwise interaction energies between protein residues in equilibrium ensembles, usually generated from MD simulations. These energies are considered the 'strength' between the nodes (residues) and are mapped as edges onto the protein structure. Subsequently, the strength values are generally normalized in order to weight or deduce the information transfer rate from one node to another. Finally, these data are used in network analysis such as shortest path identification and node centrality calculations. In this work, the interaction energies were calculated employing the pair-wise energy decomposition function of MMPBSA.
py program  [83]. For network construction, we followed the protocol proposed by Vijayabaskar et. al  [95] , where only van der Waals and electrostatic interactions of non-continuous residues were taken into account. Furthermore, all energies were normalized between zero and one, the more negative values being closer to one, and the more positive ones, closer to zero. Subsequently the betweenness centrality was calculated with the igraph package  [101]. The normalized data were transformed into path length through the following formula: w ij ¼ À logjInorm ij j, where, Inorm ij is the normalized interaction energy between residue i and j and w ij is the path length. Hence, the information transfer rate will be greater for the more favorable interactions, as the latter will have shorter path lengths.
Secondly, the combination of Autodock Vina score and MM-GBSA free energy calculations allowed us to propose some compounds with the potential to bind the predicted allosteric pockets. Our results suggest that site 3 is more druggable than site 1, as none of the selected compounds from the database seemed to form a stable complex with cruzain through the latter site, as shown through long MD simulations. Therefore, the in silico design of ligands displaying affinity for site 1 remains challenging and the usage of larger compound databases and other transient conformations of the pocket are likely to be required. On the other hand, compounds 1 and 2 were proposed here as promising scaffolds of cruzain allosteric modulators. We verified that the aliphatic chains of the site 3 residues mediated the main protein-ligand interactions within the groove.
Acknowledgments The authors are thankful to  Bruno Fernandes  for technical support and to  Prof. Jorge Chahine  for his helpful discussions. We thank to  Prof. Leandro Cristante de Oliveira  for kindly provide computer time at  Centro Nacional de Processamento de Alto Desempenho em São Paulo (CENAPAD)  (grant  613 ). Also, we thank to mesocenter of Strasbourg for running time allocation. Finally, we thank to LMDM at  UFRJ , and to the research group of  Prof. Vitor Leite  for their support with computational resources.
Statistical analysis The statistical analysis (on CFS) was performed using GraphPad Prism 6. 0b. The treatment effect was assessed on CFS, which was considered the primary outcome. The treated groups were compared with the DED untreated group. Tukey's multiple comparisons test was used and the statistical significance was set at a p < 0. 05. Results are presented as mean ± SD. The relative standard deviation (RSD) was used as a measure of the dispersion of the data around the mean. Gene expression data were log-transformed and an unpaired t-test with unequal variance was then used to assess the significance (<0. 05) in the fold changes between the DEDuntreated and DED-treated mice, and a Spearman correlation test was used to identify the relationships between gene expression and CFS scores.
Discussion 7] [28] [29] [30] [31] [32]  The different antiinflammatory treatments were able to decrease CFS scores at day 6 and day 10 for the three CsA-containing eye drops. Interestingly, at day 10, 0. 5% LE did not maintain the reduction observed at day 6. The reason for this lack of CFS reduction between days 6 and 10 is not clear. However, using the same model 1% methylprednisolone treatment decreased CFS by 6. 2% at day 10; and no improvement in CFS decrease was observed from day 6 to day 10 either. 19 It was, therefore, interesting to explore the effect of these treatments on the modulation of the inflammatory markers in the cornea (Table  3 ). Inflammatory markers (n = 34) were chosen to be followed in this study, based on literature data and their relevance in DED pathology. For this purpose, we used NanoString's technology that directly counts the number of mRNA copies of a target gene without needing an amplification step.
This study suggests that the modulation of TLR4, TGF-β1, IL1, and IL6 pathways in the cornea might be implicated in the clinical improvement seen for CFS in this mouse model. However, it cannot be excluded that other genes, with lower levels of expression or with modulations that did not reach significance in this study, also play a role in improving the corneal epithelium. One limitation of this study was that only a small number of animals per group were analyzed, with sometimes important variation in the level of expression; hence, particularly for the genes with the lowest levels of expression, this number of animals may be too low. On the other hand, the NanoString gene expression data will have to be confirmed by qRT-PCR and by a proteomic analysis. Some additional studies are needed to better understand the role of the modulation of the inflammatory genes followed in the present study and assess the direct contribution of the different genes in such a complex multifactorial disease as DED.
55 ppm). Data are reported as: multiplicity (ap = apparent, br = broad, s = singlet, d = doublet, t = triplet, q = quartet, quint = quintet, sext = sextet, m = multiplet, dd = doublet of doublets, ddd = doublet of doublet of doublets, dddd = doublet of doublet of doublet of doublets, qd = quartet of doublets, dt = doublet of triplets, dm = doublet of multiplets, td = triplet of doublets, quintd = quintet of doublets), coupling constants (in Hz) and integration. In cases where compounds were isolated as mixtures of regioisomers, signals corresponding to protons of the major regioisomer were integrated as integer values matching the number of protons in the molecule. Non-integer integration values correspond to signals of protons of minor regioisomers or to overlapping signals of regioisomers.
ACKNOWLEDGEMENTS J. M thanks the  European Research Council (ERC)  (grant agreement n°  639170 ) and the  ANR LabEx "Chemistry of Complex Systems " ( ANR-10-LABX-0026 CSC ). F. N. thanks the  Région Grand-Est  for a fellowship. V. D. V thanks the  French Ministry of Higher Education  for a fellowship. J. Y. thanks the  China Scholarship Council  for a fellowship. The authors thank  Dr. Lydia Karmazin  and  Dr. Corinne Bailly  for X-ray crystallography data and  Mr. Cyril Antheaume  for HRMS data.
Objectives In this context, the French Ministry of Industry funded and launched the Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project. The objective of ADR-PRISM was to make available the contents about ADR, informal and embedded in forums and discussions on the Web, to the actors involved in drug safety (drug companies, agencies, and pharmacovigilance experts). In the end, the tool developed in the ADR-PRISM project should generate hypotheses about new or poorly documented adverse events (AEs). To reach its goals, the ADR-PRISM consortium gathers a company developing text mining softwares (Expert System), a company specialized in pharmaco-epidemiology (Kappa Santé), 3 academic research groups providing expertise in medical informatics and statistics (National Institute of Health and Medical Research & Cordeliers Research Centre: umrs 1138 team 22 dedicated to Information Sciences to support Personalized Medicine and Laboratory in Medical Informatics and Knowledge Engineering in e-Health, and Biomedicine informatics, Service Catalogue and Index of French Language medical websites SIBM-CISMeF), 2 experts in pharmacovigilance (regional center of pharmacovigilance), as well as Vidal group that supplies the drug database used in most drug prescription systems in France.
From a natural language processing (NLP) perspective, we considered ADR as relationships between drug and AE concepts. On the basis of that, the NLP-based Skill Cartridge for pharmacovigilance developed by Expert System for ADR-PRISM includes a relation extraction module based on (named) entity recognition combined with rules and regular expressions. Before applying it to large collections of forum discussions, we designed a protocol to assess the performance of this ADR information extraction module. The objective of this study is to present this protocol.
Eligibility criteria • Posts from the Kappa Santé Detec't database that contain at least one drug's or molecule's (active substance) name, and posts randomly selected from the rest of the Kappa Santé Detec't database; a list of drugs and adverse events (AEs) of interest is established by drug safety experts; and ADR: any explicit and positive relationship between a drug and a potential AE where either the drug or the AE or both belong to the list.
Design This project is based on retrospective data collected among threads of discussion accessible on social media (messages from French health forums). A total of 2 experts in pharmacovigilance helped delineating the project. The objective is twofold: (1) focus on certain pharmaceutical products of interest and identify the related ADRs and (2) detect the emergence of general potential problem in public health. On this basis, the extraction of ADR information is expected to (1) perform well on specific concepts for use case study and (2) be able to extract all potential concepts correctly for screening purpose. To evaluate the ADR information extraction, we therefore build up the global process in 2 phases (Figure  1 ). In phase 1, we implemented an iterative process of validation and improvement of the Skill Cartridge for pharmacovigilance.
Ethics This research did not involve experiment on either humans or animals. Ethics and guarantee of data privacy constituted an integral and dedicated working group set up for the ADR-PRISM project. To comply with national regulations, we first registered data collection to the French Data Protection Agency (CNIL for Commission Nationale Informatique et Liberté), which is known as a normal notification in technical terminology, on December 23, 2015. We later submitted an authorization request on March 30, 2016, regarding data analysis and validation of the approach adopted about ethics and confidentiality to the same agency. ADR-PRISM's consortium detailed approach is explained in the study by Bousquet et al  [15]. The ADR-PRISM project was supported by an ethics advisory board, which was composed of scientists with different scientific expertise: drug safety, public health, and ethics.
Resources We used 3 lexicons to represent drug information and AEs. We codified AEs with the Medical Dictionary for Regulatory Activities (MedDRA) v15. 1  [16]  classification. Racine Pharma, maintained by the SIBM-CISMeF  [17] , provided an extensive source of drug names that covered all medications available on the French market, including brand names and active ingredients. Racine Pharma entries were mapped to the Anatomical Therapeutic Chemical (ATC) system [18], which was used as a classification system for drugs. The corpus of messages was extracted from the Detec't database  [19] , a database developed by Kappa Santé that collects messages from several French forums using a Web crawler. We limited ourselves to a list of 5 major health forums in French obtained using netscoring  [9].
Drugs and Adverse Events Extraction Regarding drugs, Expert System has developed a named entity recognition module capable of identifying words or tokens (occurrences) listed in Racine Pharma and extracting their positions from forum posts. We mapped the extracted expressions from Racine Pharma to the chemical substance level (5th level) of the ATC (last updated version: December 19, 2016)  [18]. Considering that the same active ingredient could be found under different trade names, we pooled all mapped expressions within the same ATC chemical subgroup (4th level) to define the drug concepts. Expert System has developed another module based on MedDRA to identify the AEs and extract their position in the posts. Fuzzy-matching and enrichment of thesaurus with colloquial terms enabled to take into account the characteristics of the posts on health forums.
Drug and Adverse Event Relationship Extraction (Skill Cartridge for Pharmacovigilance) An NLP module has been developed by Expert System to capture the specific information regarding the relationship established between a drug and an AE by the post's author. The module combined a set of rules and regular expressions, with a Patient lexicon constituted to ensure that the post's author set out a situation of a person taking the drug and experiencing the symptom and excludes general information regarding a drug or an AE. This lexicon contains terms such as I, me, my, cause,.