dataesr/scientific-paragraphs-categorization huggingface dataset:
The EHOP CDW gives the opportunity to get back to the free text reports (unstructured data)  [13]. This allowed us not only to identify relevant information with the help of rules based on structured data, but also to validate and ensure the accuracy of such information. The suggested codes were always manually validated to confirm their accuracy and relevance. Indeed, the objective our system is not to automatically add codes to the EHR, but rather to highlight patient records that could be improved without the need to systematically review all records. Although the algorithm can identify missing comorbidities, the method has some limitations. First, some drug prescriptions are not covered by the Theriaque database, because it only records indications approved by regulatory agencies for the drug original marketing authorizations, but not "off-label" usage  [25].
Yet, doctors do prescribe "off-label" and regulatory agencies recognize this use for patient groups not included in the original approval  [26]. As a result, several discrepancies exist between the "official" indication for a drug in the Theriaque database and the actual reasons for its prescription. The algorithm cannot detect these situations because it only relies on official drug indications. Second, the Cohen's kappa coefficient value (0. 74) indicates a substantial agreement between observers, which could be explained by their common background and similar training. This value could have been improved by involving more human experts, but very few were available at the time of the study. Furthermore, the suggested diagnoses were confirmed only in 20% of cases. The remaining diagnoses could not be confirmed mainly because information was often implicit, a recurring issue in healthcare  [27] , particularly in the case of preventive treatments.
Our algorithm adds another level of information to the clinical data by using knowledge databases. It improves the reliability of the ICD-10 billing codes used to describe patients. We think that this could help ensuring accurate reimbursement of patient stays and improve knowledge discovery from studies based on EHR data. The next step in our project is to ask clinical coders to test the algorithm in the daily practice. We think that they could use the algorithm to improve their initial coding from scratch and we would like to fully implement it in the EHOP CDW. In the future, we want to automatically send to the coding software the list of stays with potentially missing comorbidities and the codes suggested by the algorithm. Thus, all clinical coders will benefit from the system for their daily practice.
Data availability for RNA-seq Total RNA was extracted from the splenic CD11c + DCs stimulated with mock or CSGG (100 g/ml) for 4 hours and purified with Ribospin II (GeneAll Biotechnology). RNA quantitation and quality control were performed using NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE). Library preparation was performed using the TruSeq Stranded mRNA Sample Preparation Kit (Illumina, San Diego, CA), and RNA-seq was performed using the NextSeq 500 Sequencing System.
Approach used in this review Scientific databases such as PubMed, Scopus and Web of Science were searched for the following terms: 'aluminum phosphide', 'phosphine', 'antidote' and 'protective role', from January 1980 up to May 2018. We reviewed the abstracts of relevant articles, looking particularly for those examining special antidotes against AlP. Duplicate articles or articles that did not match our subject, were excluded. Experimental (including in vitro and in vivo) and human studies were presented separately.
To advance the development of methods that could be applied for assessing toxicity of Med-NPs, we have suggested Hoechst and PI staining as an alternative to LDH release assay and Hoechst-TMRM as alternative to MTT. The detection of the staining can be accomplished through a variety of techniques including immunofluorescence microscopy, FACS or the HCS platform. Advanced technologies, such as HCS, have the advantage of providing reliable data in terms of statistics, and in parallel to its applications in assessing basic information on the toxicity of the Med-NPs, it could also indicate the interaction of the nanomaterial with the various cell compartments and contribute to the clarification of the mode of action of the drug candidate  [63, 90, 91]. HCS also has the potential to be used in assessing immuno-and genotoxicity  [80].
Acknowledgements The authors would like to thank  P Boisseau  for his contribution. They are very thankful to the  EU-NCL  and  US-NCL consortium. Financial & competing interests disclosure This research was partially carried out within the  EU-NCL project , a  European Union 's  Horizon 2020 Research Infrastructure  under grant agreement no. 654190. This work was also supported by the  European Commission's Joint Research Center within the Consumer Products Safety of the Directorate of Health , Consumers and Reference Materials through the  Joint Research Center Multiannual Work Program. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
CYP-Enzyme Assays Using membrane preparations, CYP-activities were assessed through determination of product formation by EthR O-deethylation (EROD; CYP1A2) (excitation 530 nm; emission 580 nm), coumarin 7-hydroxylation (CYP2A6) (excitation 330 nm; emission 460 nm) or O-debenzylation of DBF (CYP3A4) (excitation 485 nm; emission 535 nm)  [20, 22, 26]. Assays were performed in 96-well format with a multi-mode microtiter plate reader (SpectraMax ® i3x, Molecular Devices, USA) using SoftMax Pro 2. 0 Software. Experiments were conducted with 8 nM CYP1A2, 100 nM CYP2A6, and 25 nM CYP3A4 (final well concentrations). Reactions were performed in 100 mM potassium phosphate buffer (without NaCl) (pH 7. 6) supplemented with 3 mM MgCl 2 and an NADPH regenerating system (NADPH 200 µM, glucose 6-phosphate 500 µM and glucose 6-phosphate dehydrogenase 40 U•L -1 , final concentrations).
Stock solutions of EthR were prepared in DMSO, while coumarin and DBF were prepared in acetonitrile (ACN). Final solvents concentrations were maintained constant throughout the experiment (0. 2% (v/v) DMSO or 0. 1% (v/v) ACN). Product formation was followed for 10 min at 37 • C and rates were calculated by using a standard curve of the products. Reactions were performed in triplicate with substrate concentrations ranging up to 5 µM EthR (CYP1A2), 20 µM coumarin (CYP2A6) or 10 µM DBF (CYP3A4). Velocity data were plotted according to the Michaelis-Menten equation with high confidence (r 2 > 0. 95) using GraphPad Prism 5. 01 Software (La Jolla, CA, USA) and kinetic parameters (k cat and K M ) were derived  [20, 22, 47]. Variance in data was analyzed using one-way ANOVA with Bonferroni's multiple comparison tests, with 95% confidence interval-GraphPad Prism 5.
This work was in part financed by a joint project, funded by the  Portuguese Fundação para a Ciência e a Tecnologia  [Grant  FCT-ANR/ BEXBCM/0002/2013 , as well as Grant  UID/BIM/0009/2016  of the  Research Center for Toxicogenomics and Human Health (ToxOmics) ] and the  L'Agence Nationale de la Recherche  [Grant  ANR-13-ISV5-0001 ]. F. E. was supported with a post-doctoral fellowship grant of the  Portuguese Fundação para a Ciência e a Tecnologia  [Grant  SFRH/BPD/110633/2015 ]. The authors declare no conflict of interest.
Statistical Analysis Results are showed as mean ± standard deviation, based on 3 experiments. The MTT results are expressed as mean ± standard error of mean, based on 3 experiments. The statistical significance of the differences between samples was determined using GraphPad Prism 5 software with either one-way analysis of variance (ANOVA) (for encapsulation efficiency, drug loading, NP recovery, and kinetics of curcumin release data) or two-way ANOVA (for particle size, polydispersity index, zeta potential and cytocompatibility data) with Bonferroni post-test. Differences were considered significant at p < 0. 05.
Acknowledgments: The authors acknowledge support of CITHEFOR EA 3452 by the " Impact Biomolecules" project of the "Lorraine Université d'Excellence" (Investissements d'avenir-ANR ). The authors also thank  Lise Salsi  from the  Service Commun de Microscopies Electroniques et de Microanalyses X (SCMEM)  of the  Université de Lorraine  for the help and implication in this project. Felipe Oyarzun-Ampuero  acknowledge to Fondecyt project number  1161450.
Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety. Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus.
INTRODUCTION Patients use social media to seek information, to receive advice and support from other Internet users in order to better manage their own health care and improve their quality of life  (Lamas et al. , 2016). Patients and their family share information about drugs in social media; they report on the outcomes and the impact of the drugs on their health and day-life; they describe their attitudes toward the drugs, including adherence to the treatment, adverse events and sentiment  (Laranjo et al. , 2015). Consequently, social media data mining has been recognized by drug agencies as a potential approach to identify patient reporting of adverse drug reactions (ADR), and to analyze the attitudes and knowledge of general public and patients on medicines. The Food and Drug Administration (FDA) 1  in the United States and the European Medicines Agency (EMA) 2  are considering social media as a new data source to strengthen their surveillance activities.
, 2015). Dedicated tools may help experts to extract relevant information from the data available in different sources without spending time to explore manually the data  (Lardon et al. , 2015; Nikfarjam et al. , 2015; Sloane et al. , 2015). Nevertheless,  Sarker et al. showed that the most popular algorithms in the published studies were supervised classification techniques to detect posts containing ADR mentions, and lexicon-based approaches to extract named entities from texts  (Sarker et al. , 2015). We believe that such approach still requires enhancements to support data mining in pharmacovigilance, particularly in making the data generated by patients more prominent, explicit, and accessible to experts in pharmacovigilance. Moreover, data mining should not be limited to ADR detection but rather integrate all kinds of information reported in posts, like patients' attitudes toward the treatment, compliance, and misusage.
The main objectives of the ADR-PRISM project  (Bousquet et al. , 2017)  were to perform text mining and visualization tools to enhance our understanding of patient reported information in social media and to assess how it could be used for pharmacovigilance purpose. In the context of ADR-PRISM, we conducted an in-depth content analysis of the anonymized data publicly available on methylphenidate on five open French patient forums. This study is used in this article to present the methods that we developed to mine social media and to illustrate their results. The research done in the framework of ADR-PRISM has been supported by an Ethics Advisor Board. The Ethics Advisory Board was composed of scientists with different scientific backgrounds: Gaby Danan from Pharmacovigilance Consultancy; Alain-Jacques Valleron from INSERM UMR1169 and Paul-Olivier Gibert from Digital&Ethics, and provided independent advice on the project.
, 2012)  and MEDLINE abstract  (Avillach et al. , 2013). More challenges have appeared when considering using texts from social media due to the informal and colloquial expressions of internet users  (Liu and Chen, 2015). Topic modeling is another promising text mining approach, which aims to discover hidden semantic structures in a collection of texts. Topic models constitute a set of probabilistic models allowing to explore, understand and organize large groups of structured or unstructured data  (Blei et al. , 2003). This family of models is based on the hypothesis that documents in the corpus correspond to a distribution of several topics. No prior assumption is made about the nature of topics pervaded in the studied corpus. The outcome of such models is twofold: (i) the list of thematic in the corpus, (ii) the distribution of topics on documents, that enables the clustering of similar documents.
Corpus Messages were collected using the Detec't extractor, a scraper developed by Kappa Santé. We selected five popular and open French forums: www. atoute. org, www. doctissimo. fr, www. e-sante. fr, www. onmeda. fr (previously www. aufeminin. com) and sante-medecine. journaldesfemmes. com according to their popularity and their quality evaluated by the Net scoring tool  (Katsahian et al. , 2015). The extraction was based on a set of 403 drugs from the French Health Insurance database used as keywords to extract the corpus of messages. All data extracted was publicly available and anonymized. We identified all the messages containing these drug names and extracted the whole discussions containing these messages. To extract methylphenidate related data, we used the following French brand names of the drug: Ritaline R , Quasym R , Concerta R and Medikinet R.
Medical Thesauri Three thesauri were used to represent the medical terms: RacinePharma, the Anatomical Therapeutic Chemical (ATC) classification system for drugs 7  , and the Medical Dictionary of Regulatory Activities (MedDRA) for disorders 8. RacinePharma was used to identify drug names in the messages. This resource is updated on a monthly basis to follow the modifications in the French Public Database of Medications 9. Rationale for choosing RacinePharma is that it covers all medications available on the French market. All commercial drug names were mapped to their active substance, allowing for grouping drugs that have the same active substance. ATC provides a hierarchy of drugs with five levels: (1) anatomical main group, (2) therapeutic subgroup, (3) pharmacological subgroup, (4) chemical subgroup and (5) chemical substance.
Identifying ADRs in Posts This module comprises two steps: the first one consists in recognizing drug names and medical concepts in text using NER methods; the second one consists in identifying the semantic relation between these entities, i. e. , the ADR relation. The Smart Taxonomy Facilitator (STF) Skill Cartridge TM developed by Expert System was applied on the corpus for the NER task. It combines a rule-based approach and a dictionary-based approach. The latter includes two main technologies, (i) Fuzzy Term Matching, which takes into account possible variants of the terms present in the taxonomy, thus reducing the number of false negatives, (ii) Relevance Scoring, which applies a series of heuristics that assign a score to each extracted concept, thus eliminates the least relevant concepts in order to reduce false positives.
All these methods have been evaluated in a number of empirical studies as well as in several comparative simulation studies  (van Puijenbroek et al. , 2002; Roux et al. , 2005; Ahmed et al. , 2010)  as broadly comparable, and have been used by different regulatory agencies and drug safety monitoring systems. We applied all these four methods to our database and listed the signals detected with each method in Table  5. Our results showed that more signals were detected with the two frequentist methods. As the objective of this work was not to evaluate different signal detection methods, we limited ourselves to analyze the signals obtained for methylphenidate with PRR and then compared them with two other sources: (i) the adverse effects described in the Summary of Product Characteristics (SPC) of methylphenidate and (ii) the suspected ADRs reported in Vigibase 10  , which is the WHO's global database for ADRs filled with Individual Case Safety Reports (ICSRs) collected in over 110 countries and spans over more than 100,000 different medicinal products.
Topic Model Preliminary Data Processing for Topic Model Preliminary data processing was performed for cleaning and formatting the methylphenidate sub corpus so that the topic model can be applied on it and text categorization can be achieved. To this purpose, the corpus was transformed into a matrix where each line represents a message and each column represents a token (i. e. , a term present in the corpus). This matrix is called the document-term matrix (DTM). Data cleaning was performed as follows: 1. Preprocessing: All words in messages were turned into lowercases. French accents, punctuation and abusive spaces between words were removed. 2. Removal of stopwords: In addition to stopwords, the names of the drug in the messages (e. g. , Ritaline R or methylphenidate) were excluded since this information was already taken into account as message annotations.
As previously mentioned, the modeled topics are probability distributions over the words found in the corpus. To determine each topic, words were ranked from highest to lowest tf-idf value of their probability in this topic  (Blei and Lafferty, 2009). For each topic, the first 15 words were designated as the set of characteristic words and used to interpret its semantics. We applied the method described above to the formatted methylphenidate sub corpus, and focused on the topics related to ADR, misuse and abuse. The posts related to these topics of interest were analyzed thanks to descending hierarchical classification (DHC) on observed words. The analyses were performed using the STM (Structural Topic Model) package  (Roberts et al. , 2014)  with the R software. The DHC was performed using the software Iramuteq and the ALCESTE classification.
Overall Corpus Twenty one million messages have been extracted. The messages were all posted between the 1st January 2007 and the 31st January 2016. This data set was used as a basis for signal detection. Based on the automatic annotation process described above, we have identified 31,586 ADRs, concerning 1,426 distinct drug names (representing 1,055 unique ATC codes) and 1,775 distinct symptoms (representing 1,154 unique MedDRA PTs). The five most common chemical substances involved were paracetamol, clomifene, venlafaxine, plastic IUD with progestogen and levothyroxine sodium (Table  1 ), the five most common PTs involved were pain, weight increased, fatigue, nausea and acne (Table  2 ).
Sub-corpus of Messages Related to Methylphenidate The methylphenidate sub corpus contained all messages belonging to the Detec't database published between the 1st January 2007 and the 31st January 2016 containing at least one of the following French brand names: Ritaline R , Concerta R , Quasym R and Medikinet R. This corpus contained 3443 messages from five different sources, with 75% of messages coming from Doctissimo (Table  3 ). Ritaline R was the most frequent brand name in the sub-corpus (Table  4 ).
Methylphenidate was inefficient in seven patients. In some cases, the patient perceived the adverse effect as a beneficial effect, for example, one patient considered weight loss and decrease of appetite as a positive effect, and another patient said "it wakes me up and I'm more motivated, it has an antidepressant effect and I had euphoria. " Considering the signals detected using PRR, the overlap with other data sources was important: 66. 7% of the identified relationships (26 of 39) are detected as a signal, among which, 38. 5% (10 of 26) have been mentioned in the product SPC, and 88. 5% (23 of 26) have been alarmed in VigiBase. We obtained signals for neuro-psychiatric symptoms but also for a cardiac symptom (palpitations). The missing adverse effects (false negatives) could potentially enhance certain signals.
Pharmacovigilance and Methylphenidate In this study, text mining techniques were used to detect ADRs in social media with encouraging results. Starting with the overall corpus of messages containing drug names, the system was able to detect messages with potential ADRs. A manual review was however needed to exclude some false positives when the message was unclear. Moreover, our approach demonstrated its ability to detect phamacovigilance signals. 66. 7% of the relationships  involving methylphenidate extracted automatically from social media (26 out of 39) have been detected as a signal. Among these, 88. 5% have been alarmed in VigiBase. An example of ADRs associated with methylphenidate that was detected as a signal from social media but not in VigiBase is muscle spasms. These results suggest that messages in forum could be used as an additional data source of knowledge for drug safety.
Strengths Determining which co-occurrences of drugs and symptoms in messages are true ADRs is a challenging task because of the complexity of modeling the linguistic pattern of causality. However, our study demonstrates the feasibility of the extraction of information on drugs and related ADRs from Web forums. We implemented a lexicon-based method to extract drug names and medical entities from posts. The system was based on the Smart Taxonomy Facilitator (STF) Skill Cartridge TM developed by Expert System. The system exhibits good NER performance, with F-measures of 0. 94 and 0. 81 for recognition of drugs and symptoms respectively  (Chen et al. , 2017). These results are similar to those obtained by other authors on messages in English  (Lardon et al. , 2015). Regarding French language social media, Morlane-Hondere et al.
obtained a F-measure of 0. 95 for chemicals, 0. 86 for signs/symptoms and 0. 82 for diseases using classifiers based on Conditional Random Fields and Support Vector Machines, which is also equivalent to our results  (Morlane-Hondère et al. , 2016). We retrieved in social media several signals that were found associated with methylphenidate in traditional data sources. We did not detect new ADR signals related to methylphenidate from our corpus. We plan to conduct a more extensive and systematic comparison of the ADRs on other drugs Topic modeling approaches are rather new in medical domain, and most of the studies have focused on tweets rather than web forums. Other studies focusing on medical themes and forums messages like  (Yang et al. , 2015; Tapi Nzali et al. , 2017)  used the same LDA model.
Two pharmacovigilance experts of our consortium (ALL and CB) performed an internal analysis of our results and concluded that this approach was a useful method that enhances expert' s ability to explore and analyze huge sets of text data. Above all, topic modeling performs automated annotation of such large datasets with latent "topic" information. We plan to conduct further evaluation with more experts in the 16 http://www. ema. europa. eu/docs/en_GB/document_library/Report/2017/02/ WC500221615. pdf future. Moreover, we showed that, besides ADRs, social media could be used to identify unexpected misuse behaviors-like parents taking pills prescribed to their child-that are impossible to detect from other sources.