dataesr/scientific-paragraphs-categorization huggingface dataset:
Cytocompatibility study The safety of PLGA particles as drug delivery systems is very important for medical applications. However, PLGA nanoparticles are well known to be biodegradable and biocompatible in vitro and in vivo compared to other nanoparticles of a similar size range such as zinc oxide, ferrous oxide and fumed silica which exhibit toxic effects when applied in the field of nanomedicine  [69, 70]. An in vitro cytotoxicity study was conducted to assess the cell viability following exposure to the unloaded PLGA nanoparticles, NIH-3T3, HS68 and hATSC cells were cultured in the presence of unloaded PLGA nanoparticles. MTS assay showed excellent cell viability even at high nanoparticles concentration (20 mg/ml) (Figure  5 ). Furthermore, the cells viability in NP suspension was comparable to that in stabilizer solution (Lutrol ® F68) at a concentration corresponding to the amount present in the nanoparticle suspension (data not shown).
Acknowledgment Acknowledgments are made to the SCIAM (" Service Commun d'Imagerie et d'Analyse Microscopique ") for transmission electron microscope images. We thank the  "Région Pays de la Loire"  ( Bioregos 2 project ),  ANR ("Agence Nationale pour la Recherche" , grant ( ANR-09-PIRI-0004-01 ), the  French and Syrian education ministers  for the financial support.
Selected regions of the merozoite surface proteins 1 and 2 (MSP1 and MSP2) and the glutamate rich protein (GLURP) were amplified using nested PCR as described previously  [16]. Separate reactions were performed for each pair of primary and nested primers. A no-template control was used in all reactions. Genomic DNA from cloned laboratory strains was used as a positive control for each allele. PCR products were characterized based on sequence and size polymorphisms identified by gel electrophoresis. Size polymorphisms were analyzed by capillary electrophoresis on polyacrylamide gels using an ABI 3130XL sequencer (Applied Biosystems, Carlsbad, CA) and compared using Genemapper 4. 0 software (Applied Biosystems). Genotyping patterns were compared using GelCompar II software (Applied Maths, Sint-Martens-Latem, Belgium).
Statistical analysis Statistical analysis was performed using SAS version 8. 2 (SAS Institute, Cary, NC). All statistical tests were performed at a 5% significance level and were two tailed except for non-inferiority tests. Continuous variables were compared using Student's t-test for normal distributions or a non-parametric Wilcoxon test for distributions that were not normal. Categorical variables were compared using the chi-square test or Fisher's exact test. The intent-to-treat population was defined as all patients who received at least one dose of study medication and who did not have two rejections or vomiting; the per-protocol population comprised all ITT population children who completed the study without major protocol violation and the safety population comprised all patients who received at least one dose of study treatment.
Parasite densities were normalized using logarithmic transformation. Risks of treatment failure were estimated using the Kaplan-Meier product limit formula. Data were censored for patients who did not complete follow-up or were reinfected with non-falciparum species. For the PCR-adjusted ACPR, new Plasmodium falciparum infections detected on the basis of genotyping were also censored. The primary objective, non-inferiority of ASAQ vs. AL in the day-28 PCRcorrected ACPR rate during the first malaria episode, was assessed in the perprotocol and intent-to-treat populations. In addition, in the secondary analyses, PCR-corrected rates for subsequent episodes were compared in the intent-to-treat population. ASAQ was considered non-inferior to AL if the lower limit of the onesided 95% confidence interval for the difference in rates was greater than 25%.
Splenomegaly Splenomegaly was detected on day 0 of the first episode in 8. 6% (17/198) of children in the ASAQ group and 11. 5% (23/201) of those in the AL group (p50. 525). All cases were grade 1 or 2. For subsequent episodes, splenomegaly was present in at most 1. 2% of the children in each group, and no differences were detected between the ASAQ and AL groups (data not shown). dehydration, or convulsion (data not shown). Most TEAEs occurred during the first three episodes (Figure  8 ). Also, no TEAEs considered treatment-related led to study discontinuation. AESIs were reported in 14 children in the ASAQ group (6. 7%) and 14 (6. 8%) in the AL group (table  5 ). The most common AESIs were abnormal liver function tests (5. 6% of children overall) and neutropenia or agranulocytosis (1. 5% overall).
Acknowledgments We wish to thank  Dr. Phillip Leventhal  ( 4Clinics, Paris, France ) for his guidance in writing the manuscript. Financial support for this study was provided by  Sanofi Access  to Medicines. We acknowledge the study monitors  Brigitte Charron ,  Stephanie Durant  and  Tina Olympio , the clinical study team of  Christopher Eruaga ,  Buyi Benzamin ,  Ambayo William ,  Grace Musimenta ,  Katoko Agnes  and  Anna Adeke  as well as parents, and children who participated in the study.
Experimental Section General. Experimental procedure for the synthesis of compounds 2, 2', 3, 4, 12, 14, 15, 20, 27-29 and LY503430 with copies of their 1 H and  13  C NMR spectra were previously reported. 4  1 H NMR spectra were recorded on a Bruker AVANCE 400 at 400 MHz and data are reported as follows: chemical shift in ppm from tetramethylsilane as internal standard, multiplicity (s = singulet, d = doublet, t = triplet, q = quartet, m = multiplet or overlap of non-equivalent resonances, integration). 13 C NMR spectra were recorded on a Bruker AVANCE 400 at 100 MHz and data are reported as follows: chemical shift in ppm from tetramethylsilane with the solvent as internal standard (CDCl3, δ 77. 0 ppm), multiplicity with respect to proton (deduced from DEPT experiments, s = quaternary C, d = CH, t = CH2, q = CH3).
The promiscuity of a collection of enzymes consisting of 31 wild-type and synthetic variants of CYP1A enzymes was evaluated using a series of 14 steroids and 2 steroid-like chemicals, namely, nootkatone, a terpenoid, and mifepristone, a drug. For each enzymesubstrate couple, the initial steady-state velocity of metabolite formation was determined at a substrate saturating concentration. For that, a high-throughput approach was designed involving automatized incubations in 96-well microplate with sixteen 6-point kinetics per microplate and data acquisition using LC/MS system accepting 96-well microplate for injections. The resulting dataset was used for multivariate statistics aimed at sorting out the correlations existing between tested enzyme variants and ability to metabolize steroid substrates.
Interestingly, the X-ray structural information now available for CYP1A P450 proteins  [10, 11]  does not evidence any clear features explaining how major differences of activities among CYP1A enzymes could be related to local structural differences. Statistical approaches thus constitute an alternative but they require sufficiently large datasets of experiments thus relying on high-throughput methods. The steady-state rates were determined by monitoring metabolites produced from a given steroid substrate by LC/MS following different incubation times in 96-well microplate using yeast-expressed recombinant mammalian CYP1A enzyme. Due to the large number of enzyme-substrate couples, each giving frequently several metabolites, only measurements at saturating substrate concentrations were performed.
Statistics. A normalization procedure based on the variance in the dataset was used as previously described  [9]. CYP1A activities for the different steroidal substrates were analyzed using principal component analysis (PCA) and multidimensional scaling (MDS). PCA visualizes systematic patterns or trends of variation in large data set. The different trends of variation hidden in the initial multidimensional space are evidenced since the new orthogonal axes of the projected space (the two first principal components) are derived from the directions of larger variability  [17]. MDS is a nonlinear projection of the distances separating each object from the others in the original multidimensional space into a 2-or 3-dimensional diagram designated as the MDS configuration plot  [18]. MDS enables one to easily visualize and evaluate distances between two objects considering at the same time the influence of all other objects.
An indicator to evaluate the quality of a MDS analysis is calculation of a diagnostic index known as Kruskal's stress which varies from 0 (a perfect fitting) up to 1 (no fitting). It measures the closeness of the distance mapping in the 2D MDS plot compared to the "real" distances in the original space. The multivariate statistics and dendrogram construction were performed by using Addinsoft XLSTAT software. Datasets and correlation matrices used throughout this work are available upon email request from urban@insa-toulouse. fr.
Human CYP1A Structures. The atomic coordinates were taken from the Protein Data Bank, RCSB, Rutgers University (entry: 4i8v for human CYP1A1 at 2. 6 Å resolution and 2hi4 for human CYP1A2 at 1. 95 Å resolution)  [10, 11]. Protein visualizations were performed by using PyMol (Delano Scientific LLC, San Carlos, CA, USA).
Data Normalization for Analysis. Since kinetic analyses were not carried out with purified enzymes and because P450 contents in microsomal fractions were not systematically determined due to limited sensitivity of spectral quantification with certain microsomal fractions, actual catalyst contents from a yeast microsomal preparation to the other were not systematically assessed. Two parallel normalization treatments were thus carried out on the raw activity data. The first treatment of dataset consisted in unit scaling of activities; it is aimed to easily visualize the full dataset by normalizing the activities of all metabolites in a way that each activity value ranges from 0 to 1. This makes a direct comparison of the dataset possible even if the raw unprocessed values widely differ, as is the case here.
To do so, for each metabolite, the highest signal observed is arbitrarily set at 1. All other activities determined for this metabolite with all other enzymes are expressed as a ratio of their values to the highest activity observed. This makes all activity values for each metabolite to range from 0 to 1 after this data processing. Such a processing was already used for activity data visualization purposes by others  [20]. The second treatment of dataset consisted in a normalization based on the variance in the dataset as described previously  [9]. This variance normalization  [21]  eliminates the arbitrary factor linked to differences in expression levels of enzyme variants. A possible drawback could be that global substrate selectivity features of the whole enzyme collec-tion are not corrected and could introduce some arbitrary weighting of data.
Two dimensionality reduction statistical approaches: principal component analysis (PCA) and multidimensional scaling (MDS), were performed on the variancenormalized dataset to obtain projections of the data in twodimensional diagrams. The two statistical approaches, PCA and MDS, are complementary and somewhat slightly affected by unavoidable data bias. PCA is a linear approach fairly sensitive to weighting and by the way the "distances" between activities are calculated (the metrics). MDS is in contrast a nonlinear projection, frequently less sensitive to these factors which, in contrast to PCA, do not necessarily yield a unique solution.
Structural Features Important for a Steroid to Be a CYP1A Substrate. Correlations existing between substrate structural elements and the activities were first investigated using a subset of the dataset for the sake of clarity. This subset includes only the wild-type CYP1A enzymes and the activities corresponding to the principal metabolite produced for each steroidal substrate. The analysis was limited to wild-type enzymes since the process of chimera formation by sequence shuffling may alter the typical profile of the protein structural elements that fit the substrate compared to the original profile in the wild-type enzyme. Similarly, the analysis was limited to the main activities for each steroidal substrate because the main product being produced at a high rate implies an optimal fitting (in the general acceptance of this term) of the substrate molecule within the catalytic cavity of the enzyme.
CYP1A Structural Elements Influencing Steroid Metabolism. When statistical analysis is no longer applied to the dataset columns (i. e. , activities) but to rows (i. e. , enzymes), it is possible to classify by MDS analysis the different wildtype and chimeric enzyme structures for their specificity toward the steroidal substrates assayed (Figure  5(a) ). Human CYP1A2 enzyme exhibits a fairly narrow substrate specificity for steroids compared to the other tested wild-type CYP1A, even compared to its rabbit CYP1A2 ortholog. Consequently, the duplicated assays of this enzyme appear relatively isolated from its rabbit counterpart on the left border of the plot. Moreover, rabbit CYP1A2 presents a J helix which is of the 1A1-type in its sequence, contrary to what is seen for human CYP1A2 (see Table  1 ), and this helix has been proposed to be involved in the interaction with NADPH-P450 reductase  [22, 23].
lower cost generic ACEIs  (Vončina et al. , 2011). The findings were also compared with the influence of prescribing restrictions for the ARBs in the Republic of Srpska  (Markovic-Pekovic et al. , 2012). Only administrative databases were used in the various studies to provide accurate data on reimbursed utilization and expenditure  (Godman et al. , 2011a). As a result, only data on ambulatory care utilization and expenditure data was recorded. This typically includes out-patient but not in-patient data. In all cases, utilization rates were computed using Defined Daily Doses (DDDs) in line with international guidance  (WHO, 2003). The latest DDDs were also used, again in line with recommendations  (WHO, 2003; Godman et al. , 2011a). We did not use Prescribed Daily Doses (PDDs) as there was no access to patient data in the majority of countries studied.
, , 2011a,b;,b; Vončina et al. , 2011). No attempt was made to analyze the appropriateness of prescribing due to the lack of access to patient databases. In addition, the main emphasis of the various studies was on prescribing efficiency once a decision had been made by the physician to prescribe either a PPI, renin-angiotensin inhibitor drug or a statin. As a result, care should not be compromised through encouraging the greater use of generics at appropriate doses  (Norman et al. , 2008; Usher-Smith et al. , 2008; Moon et al. , 2010; Godman et al. , 2013a; Martin et al. , 2014).
Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. This activity is usually performed within dedicated databases (national, European, international. ), in which the ADRs declared for patients are usually coded with a specific controlled terminology MedDRA (Medical Dictionary for Drug Regulatory Activities). Traditionally, the detection of adverse drug reactions is performed with data mining algorithms, while more recently the groupings of close ADR terms are also being exploited. The Standardized MedDRA Queries (SMQs) have become a standard in pharmacovigilance. They are created manually by international boards of experts with the objective to group together the MedDRA terms related to a given safety topic.
Introduction The development of new drugs has allowed the treatment of many diseases that were previously considered incurable and with potential fatal outcomes for patients. However, this major therapeutic advance is limited by the toxicity of some drugs that may also be dangerous for patients. To minimize the risks associated with drug use, it is necessary to detect as early as possible the adverse drug reactions (ADRs) that may have been unnoticed during clinical trials. This is the role of regulatory authorities and of pharmacovigilance units within pharmaceutical laboratories and hospitals. The main source of knowledge for pharmacovigilance is based on the reporting of the ADRs by health professionals and patients. These case reports are recorded in pharmacovigilance databases. To facilitate the analysis of those data, ADRs are coded using a controlled vocabulary, usually MedDRA (Medical Dictionary for Drug Regulatory Activities)  [1].
This resource is created through the projection of Med-DRA on the terminology SNOMED CT (Systematized Nomenclature of Medicine -Clinical Terms)  [11]. The projection is performed on the basis of the UMLS (Unified Medical Language System)  [12] , in which several terminologies are already merged and aligned, including MedDRA and SNOMED CT. The ontoEIM resource has been exploited to build groupings through the hierarchical subsumption  [10, 13]. Precision observed is high while the recall is extremely low, which is due to the fact that the SMQs contain terms from different SOCs. In other experiments, the ontoEIM resource has been used in combination with the semantic distance algorithms and applied to a subset of the MedDRA terms  [14]. The same approach has been applied to a subset of WHO-ART (WHO Adverse Reaction Terminology) terms  [15].
Methods The method is organized in four main steps (Figure  1 ): (1) computing the semantic distance and similarity between the MedDRA terms using ontoEIM, (2) computing the semantic relations from a flat list of the MedDRA terms with the terminology structuring methods, (3) clustering the MedDRA terms, (4) and evaluating the obtained clusters against the SMQs. For the implementation, we exploit Perl and R (http://www. r-project. org) languages, and several Natural Language Processing (NLP) tools.
Evaluation of the generated results We first evaluate the correctness of the generated semantic relations, which is done manually by a computer scientist. We then perform quantitative and qualitative evaluation of the generated clusters. The quantitative evaluation is performed thanks to their comparison with the SMQs. A cluster is associated to the SMQ with which it has the maximal F-measure. For the setting of the thresholds of the semantic distance and similarity algorithms and their evaluation, we perform a ten-fold cross-validation: the data are partitioned into ten subsets, one subset is used for the setting up the methods while the remaining nine subsets are used for the evaluation. This process is done ten times with a different training subset each time. Three classical measures are then computed: precision P (percentage of the relevant terms clustered divided by the total number of the clustered terms), recall R (percentage of the relevant terms clustered divided by the number of terms in the corresponding SMQ) and Fmeasure F (the harmonic mean of P and R).
We plan to perform an exhaustive analysis of the nature of semantic relations which can be observed within the SMQs, which will allow to propose other methods and to reduce the current false negatives within the clusters. An alternative method will consist into the exploitation of corpora for the detection of other semantic relations among the terms. In addition, we intend to carry out a more detailed evaluation of the generated clusters. This addresses particularly the impact of the generated clusters on the exploring of the pharmacovigilance databases (such as the FDA database) and on the signal detection tasks. The very first results of this type of evaluation (not presented in the article) are very promising because they lead to an improvement of the signal detection by comparison with the results obtained with the SMQs.
Acknowledgments The presented research was conducted as part of the  PROTECT consortium. It has received support from the  Innovative Medicine Initiative Joint Undertaking  (www. imi. europa. eu). The authors also acknowledge the support of the  French Agence Nationale de la Recherche (ANR)  and the  DGA , under the  TecSan  grant  ANR-11-TECS-012. The authors are also thankful to the reviewers for their patience and remarks which improved the quality of this work.
A cross-reaction study with the nal von minden methamphetamine Drug-Screen ® test (nal von minden GmbH, Regensburg, Germany), confirmed the poor response of cathinones with these screening tests. Looking at these data obtained with marketed amphetamine/methamphetamine assays, even if cross-reactivity is chemical structure-dependent, a positive result is not expected in a user's urine, except perhaps in the context of massive intoxication. By contrast, it was recently shown that MDPV cross-reacts with the phencyclidine (PCP) immunoassay used in hospitals. 3 Recently, in commercial enzyme-linked immunosorbent assays, eight cathinone derivatives among 30 designer drugs that were evaluated demonstrated cross-reactivity at concentrations at low as 0. 15 mg/L when tested against the Randox Mephedrone/Methcathinone ELISA kit (RANDOX Toxicology, Crumlin, UK) recently developed for forensic specific cathinones screening in urine and blood specimens.
56 In a fatal case related to the consumption of methylone, steatosis of the heart muscle, congenital heart disease, and bronchial asthma could have been the predisposing factors for sudden cardiac death that occurred in the presence of methylone. 57 Among 62 mephedrone young victims collected in the UK National Programme on Substance Abuse Deaths database, mephedrone alone was identified at postmortem on eight occasions. 58 ehavioral effects include insomnia, confusion, agitation, panic attack, anxiety, severe paranoia, hallucinations, psychosis, suicidal ideation, self-mutilation, and aggressive, violent, and self-destructive behavior. 59 White matter abnormalities with greatest severity of damage underlying executive motor areas have been observed in patients with a distinctive extrapyramidal syndrome due to intravenous ephedrone abuse.
All information collected will be useful for professional continuing education of the clinician pertaining to new trends of substituted cathinone abuse. Meanwhile, substituted cathinones represent only part of synthetic derivatives available on the online market. An ever-increasing diversity of new products not under control are manufactured to supply an ever-increasing and diversified demand for psychoactive substances. In conclusion, it seems that: 1) emergency departments will continue to encounter patients suffering from complications caused by synthetic stimulants; 2) clinicians should remain vigilant of the risk of increasing morbidity and mortality associated with these products; and 3) as key health care practitioners, physicians and pharmacists must continue to feed databases of case reports and help promote awareness of this problem in their communities.
1% (w/w), the rest being GIB 1. 4% (w/w), GER 6. 7% (w/w), and most likely potassium sulfate and yellow substances which were all eliminated further on by gel filtration (Table  1 ). Enhancement of the purity level of GRA was achieved by the final gel filtration step. Seven runs of gel filtration afforded 3. 10 g of purified GRA as a white amorphous solid after freeze-drying. The purity of GRA assessed by HPLC resulted to be 99% (area peak based) and 95% on weight basis. Table  1. Summary of data for the purification of glucoraphanin (GRA) and the production of R-sulforaphane starting from 150 g of Tuscan black kale (Brassica oleracea (L. ) ssp acephala (DC) var. Sabellica L. cv. 0D74) defatted seed meal. Purified GRA 3. 10 g R-Sulforaphane 1. 09 g a Glucosinolate content (g) reported as the mean of eight determinations (n = 8).