dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Dr. Frederick Bayer ,  National Museum of Natural History Smithsonian Institution , for the gorgonian identification, and the staff at the  Mass Spectrometry Center, University of California-Riverside , for providing HRMS. We also thank  Dr. William Fenical , from  Scripps Institution of Oceanography, University of California San Diego , for structural support during the collection expedition and plentiful scientific discussions. This work was supported by grants from the  United States National Science Foundation (NSF)  and the  Brazilian funding agency Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).
Save parameter data file. A JAVA program was developed to compute the Voronoi diagram and its dual Delaunay triangulation graph  19  on each selected ROI (Fig. 1 ), in order to determine several geometrical and topological parameters: cell density, cell area and perimeter, average distance of the polygon center to the nearest neighbor, structural entropy, and mean number of Delaunay neighbors per cell. The two-dimensional structural entropy was calculated as follows: 20 E Q -T A R G E T ; t e m p : i n t r a l i n k -; s e c 2 ; 3 2 6 ; 3 7 5 Entropy ¼ logðNÞ - X H logð1∕HÞ ; where H ¼ Ai∕ðROI areaÞ, Ai is the area of the i'th Voronoi polygon, and N is the total number of centroids or polygons in the ROI. Data in the figures are presented as mean AE one standard deviation. Analysis of variance followed by a post hoc analysis (Tukey's test) were performed for group comparison.
The sample loop was 20 µL (Rheodyne 7125 injector). Chromatographic data were collected and processed on a computer running Millennium 2010 software, version 3. 20. The analytical column (SunFire C18, 250×4. 6 mm, internal diameter 5 µm) was purchased from Waters (Tokyo, Japan). The mobile phase consisted of acetonitrile/water (50:50, v/v). The flow rate was 0. 6 mL⋅min -1. All separations were carried out at 30°C. Acetonitrile of analytical grade was obtained from Merck (Nogent-sur-Marne, France) and deionized water was obtained from Milli-Q system (Millipore, Molsheim, France).
One microliter of the derivatized extract was injected. Acquisition and integration of data were performed using the Xcalibur™ software (version 1. 4. 2, 2009, Thermo Electron Corporation). The instrument utilized electron impact ionization and was operated in the SIM mode. Ions with m/z 72 (MDN), m/z 277 (EDDP-TMS), m/z 78 (MDN-d9), and m/z 280 (EDDP-d3-TMS) were monitored. Preparation of working standard solutions. MDN and EDDP working standard solutions 100 and 10 μg/mL were prepared in methanol from MDN and EDDP available standard solutions 1 mg/mL, and then stored at -20°C. These solutions were used for the construction of calibration samples and quality control (QC) samples. Combined internal standards solution was prepared in methanol by dilution of the stock solution of MDN-d9 and EDDP-d3 to a final concentration of 10 μg/mL for both analytes.
Statistical analyses Statistical analyses were performed with the Prism version 5 for Mac software (GraphPad Software, San Diego California), using the Student's t test or Mann-Whitney U test, depending on the distribution of the data, to compare bacterial loads in organs and the log-rank test to compare mortality rates. Clustering analysis of the LN lesional patterns: histology sections were scored according to previously described criteria of histology lesions in the lymph node  [16]. Each sample was scored as « 0 » or « 1 » for each of the criteria related to tissue damage and inflammatory reaction, so that a « 0 / 1 » table gathering the lesional patterns of all samples was created. The table was computed by the BioNumerics software, version 6. 6 (Applied Maths, Kortrijk, Belgium), using the Minimum Spanning Tree approach to analyze and display the similarity of the patterns.
Hemolytic Effect of Different α-CD Derivatives Native α-CD showed relatively high hemolytic effect as its HC50 value is 16 mM. TRIMEA was much more hemolytic followed by the succinyl derivative. RAMEA caused hemolysis at similar concentration to the non-substituted native α-CD. The order of hemolytic effect in case of methylation is TRIMEA >> RAMEA > native α-CD. Among the non-ionic derivatives HPACD and AcACD were not hemolytic up to 100 mM concentration. In case of the ionic derivatives (phosphated, sulfated and carboxymethylated), high HC50 values (>100 mM) were determined, which indicates that there is no severe hemolytic effect under these circumstances (Figure  1A ,B and Table  1 ). All data were obtained from three to five independent biological replicates and in the same experiments, four parallel concentrations were measured.
Discussion In our study, the toxic effect of α-CD derivatives was investigated in vitro, on Caco-2 cells and in parallel on human erythrocytes by two different methods: viability tests (MTT and hemolysis) and impedance-based cytotoxicity assay (RT-CES). The IC50 and HC50 values were determined and correlated with the structures. Mainly, the MTT assay is based on the enzymatic conversion of MTT in the mitochondria, and detects early cytotoxicity compared to the hemolysis and RT-CES  [31]. Hemolysis studies were a simple and rapid investigation to classify the CDs according to their cytotoxicity  [15]. Both MTT and hemolysis tests are suitable for the morphological verification of the toxic concentrations of α-CD, but could not provide kinetic data  [32]. They are endpoint determinations, and higher concentrations of excipients may interfere with these detection methods  [33].
Multiple parameters under dynamic circumstances are needed for predicting human acute toxicity studies  [36]. Real-time cell electronic sensing assays provide reliable and dynamic cytotoxic parameters. The RT-CES method was the most sensitive technique in our study to determine the safest concentration of different α-CD derivatives. However, the cytotoxicity ranking obtained was the same as measured by MTT on the same cell line (Caco-2) and determined by the hemolysis test on human erythrocytes. Correlation was found among these tests based on different cell lines and different endpoints. The RT-CES method has been validated by Zhu et al. [37]. High cytotoxicity information content and more predictable kinetic data in human experiments were established. Methylated (TRIMEA, RAMEA) and succinylated derivative (SuACD) showed the highest cytotoxicity.
Statistical All data were obtained from three to five independent biological replicates and in the same experiments, four parallel concentrations (wells) were measured. Raw data of cell viability (difference of absorbance values at 570 nm against 690 nm), hemolysis (absorbance at 540 nm), and cell index were compared with one-way ANOVA (using Geisser-Greenhouse correction) followed by Tukey post-testing (because all data passed the D'Agostino and Pearson omnibus normality test). For this purpose, data related to five concentrations were selected. Statistical significance for the difference of means was assigned into one of five categories: p > 0. 05 (not significant), p < 0. 05 (*), p < 0. 01 (**), p < 0. 001 (***) or p < 0. 0001 (****). Data presented in this paper are expressed as mean ˘SD.
Thyx docking methodology The H. pylori ThyX protein structure (PDB code 3AH5  [33, 34] ) was processed by PYMOL software  [35]  to remove water molecules and the C:dUMP cofactor. Polar hydrogen atoms were added and atomic partial charges were assigned using the PYMOL VINA plugin  [36]. The residues A:Arg197, C:Arg109 and C:Tyr110 were chosen to be flexible during docking performed with the VINA software  [37]. A cubic search volume of 25 Â 25 Â 25A ˚centred on A:FAD N5 atom was defined and the lowest predicted energy conformation was kept for analysis.
Calculation and curve fitting Experiments were carried out in duplicate and each compound was tested in at least two different experiments. All curve fitting was carried out using Prism 2015 | Vol. 3 | Iss. 5 | e00161 Version 8. 0 software (GraphPad Software, Inc. , La Jolla, CA, USA). Dose-response curves for test compounds from calibration induction inductions assays were generated and the Ind max and Ind 50 values were determined by fitting the data to a three-parameter sigmoid (Hill) equation (as described by  Ripp et al. 2006 ). Ind max is the maximum fold induction of CYP activity induced by the compound and Ind 50 is the concentration at which 50% maximal induction was achieved. The Relative Induction Score (RIS) was determined using the following equation: RIS ¼ C max;u Â Ind max C max;u þ Ind 50 (1) The C max,u value for omeprazole was taken from Mostafavi et al.
Metabolite analysis by HPLC Supernatants were analyzed for unchanged drug and specific metabolites by LC/MS-MS: phenacetin and 4acetamidophenol for CYP1A2, tolbutamide and 4hydroxy-tolbutamide for CYP2C9, bupropion and hydroxyl-bupropion for CYP2B6, dextromethorphan, dextrorphan and dextrorphan glucuronide for CYP2D6, Midazolam, 1 0 -hydroxymidazolam and 1 0 -hydroxymidazolam glucuronide for CYP3A activity. The data were collected and processed using MassLynx 4. 1 Software from Waters-Micromass. The chromatograph (Acquity UPLC system I Class) was fitted with an Acquity UPLC BEH C18 column (2. 1 mm i. d. 9100 mm length, 1. 7 lm particle size), coupled to a Xevo TQS mass spectrometer (all from Waters, Milford, MA) and used in electrospray ion positive mode except for tolbutamide and its hydroxyl metabolite which were analyzed in ion negative mode.
Introduction Spontaneous reporting systems (SRSs) are pivotal for signal detection, especially for rare events with a high drug-attributable component, such as torsade de pointes (TdP). Use of different national SRSs is rarely attempted because of inherent difficulties, but should be considered on the assumption that rare events are diluted in international databases. Objective The aim was to describe TdP-related events associated with antipsychotics, H 1 -antihistamines and antiinfectives in three national SRSs (in Italy, Germany and France) and highlight potential signals of torsadogenicity through a combined literature evaluation. Methods A common search strategy was applied to extract TdP-related events: (1) TdP, (2) QT interval abnormalities, (3) ventricular fibrillation/tachycardia, and (4) sudden cardiac death.
National databases have been used very rarely to analyze the risk of drug-induced TdP in the post-marketing setting  [2] ; this may depend on the fact that a single national SRS is perceived to be insufficient to detect rare events, especially as compared with the large catchment area of international databases, such as the FDA Adverse Event Reporting System (FAERS). Nonetheless, national databases have the advantage of providing the actual local picture of the risk (which depends on the real drug use) and offer access to the patient's medical history (the so-called ''narratives''), thus potentially enhancing the performance of signal detection. In particular, multiple database analysis can be used to compare results across databases, while maintaining the diversity of reporting pattern within each single source, and avoid the theoretical dilution phenomenon that may occur when analyzing international SRSs  [3].
On these grounds, we analyzed three European national SRSs (i. e. , French, German and Italian databases), with the following aims:  (1)  to describe the distribution of TdPrelated events associated with antipsychotics, H 1 -antihistamines and anti-infectives among the different databases; and (2) to identify novel signals of torsadogenicity, by comparing published literature data, especially from international SRSs, namely FAERS.
Data Sources: Accessibility and Technical Issues A summary of national SRSs is provided below, with a description of the key technical issues faced to harmonize the process of data extraction. In all SRSs, drugs are codified through the Anatomical Therapeutic Chemical (ATC) Classification System codes, whereas adverse reports use Medical Dictionary for Regulatory Activities (MedDRA Ò ) terms. Within each database, the in-house de-duplication process was applied. 1. The French SRS (2000-2010), named the Base Nationale de Pharmacovigilance (BNPV), is a computerized information database for research purposes, and includes medically validated reports from 31 regional pharmacovigilance centers; no data are collected from the manufacturer. Data submitted to the BNPV are stored in 14 different tables that are linked to each other using the ''report identification number''.
The French Imputability Method is adopted for causality assessment  [4]. Reports related to vaccines are collected only in the French database and, therefore, were identified and excluded if vaccine(s) was the only reported agent. Likewise, reports on illicit drugs, heavy metals intoxications, homeopathic treatments, herbal medicines, medical devices, cosmetics, and accidental ingestion of substances that are not strictly considered as medication (e. g. , mouthwash) were excluded  [5].
Details on outcome definition and relevant codification have been fully provided elsewhere  [6]. In the Italian and French databases, a free-text search strategy was also performed by analyzing narratives through automatic search, based on a list of ''string'' and/or items agreed among authors (see the Electronic Supplementary Material, Table  S1 ). These potential additional cases were finally validated for inclusion by a clinical pharmacologist and/or pharmacologist with expertise in pharmacovigilance (i. e. , F. S. and S. A. for the French database; U. M. for the Italian database).
Descriptive Analysis Overall, 3505 cases for events of interest and ARITMO drug classes were retrieved from the three national SRSs, with Germany ranked first (n = 1468). Large inter-and intra-database differences were found (Table  1 ). The distribution of the events in France has remarkable peculiarities: the largest volume of reports is related to group 3A (i. e. , ventricular fibrillation), whereas only a few cases (if any at all) were recorded for groups 3B and 3C. Conversely, group 3C records the largest number of reports extracted in both the German and Italian SRSs. The contribution of narratives is appreciable, especially for group 1 ? 2, both in Italy (23 %) and France (15 %). Overall, France ranked first (n = 143) in terms of drugs with at least one case of interest across outcomes, followed by Italy (n = 130) and Germany (n = 110).
Antibacterials (J01) were more frequently recorded in groups 3 ? 4, and represented 61 % (491 out of 801) in the Italian SRS and 38 % (515 out of 1372) in the German SRS. Conversely, cases related to antipsychotics mostly emerged in the German SRS (where they ranked first, both for groups 1 ? 2 and 3 ? 4), and were recorded in 54 % of total cases (792 out of 1468). Antihistamines are also widely reported, especially in German and French SRSs (Table  2 ). The complete list of drugs with all data separated for each single outcome is provided as electronic supplementary material (see supplementary appendices S2, S3 and S4 for France, Germany and Italy, respectively).
Signal Detection Forty-four drugs received at least three cases in at least one national SRS (Table  3 ), 41 of which met criteria for SDRs in at least one single source. Among these, 31 SDRs were detected only from one single national database: 18, ten and three from the French, German and Italian SRSs, respectively. Only five SDRs were detected in all national data sources (amisulpride, aripiprazole, haloperidol, olanzapine, risperidone). Nine SDRs detected from national SRSs were characterized by at least three cases without concomitant proarrhythmic drugs (signal substantiation): quinine, tiapride.
Discussion Our study aimed at testing heterogeneity among national SRSs and verifying whether this feature actually translates into improvement of signal detection performance. To our knowledge, this is the first study comparing three national SRSs for signal detection of drug-induced TdP. Diversity across and within national SRSs emerged in terms of distribution of TdP-related events and associated drugs; surprisingly, nine SDRs captured by national SRSs were not detected by the most recent studies on spontaneous reporting carried out in FAERS. Our initial hypothesis, indeed, was that spontaneous reports in national SRSs (and submitted to FAERS) may be diluted in FAERS, and cannot be fully appreciated in international SRSs, thus, potentially compromising signal detection performance. This hypothesized dilution phenomenon may be theoretically ascribable to two major issues: (1) only serious reports from European countries are submitted to FAERS; (2) the background for comparison may be influenced by a number of biases, including masking effect, which was documented in FAERS and is hard to manage by simple unmasking protocols because of the large size and diversity of the database, characterized by complex interdependencies between drugs and events  [13].
Actually, it is interesting to observe that the number of cases obtained from a single national SRS largely exceeded those retrieved from studies performed on the entire FAERS (with the exception of levocetirizine). Therefore, the most plausible interpretation is that only a minority of European reports recorded in national databases are submitted to FAERS. In other words, only partial, but unmeasured, overlap is likely to exist between national SRSs and FAERS, an unexpected phenomenon considering the seriousness of TdP and related clinical events. Differences among national databases in case and drugs distribution (e. g. , ventricular fibrillation was most frequently reported in France; antipsychotics accounted for the majority of reports in Germany, whereas anti-infectives did in Italy) are likely to be multifactorial.
A driving factor is expected to be the actual pattern of drug use at the national level, which is further influenced by marketing penetration, pharmaceutical pressure on a given product(s), specific safety issues causing regulatory restrictions, number of drugs available on the market, marketing life, reimbursement issues and prevalence of the different diseases. In addition, further reasons may be the different time windows used across databases (only the Italian SRS strongly differed from the others and collects data since 1969), as well as peculiarities in drug selection, recording, mapping, detection and removal of duplicates, and methods used for causality assessment within each database. The type of reporter could also vary among SRSs. For example, in France, the majority of reports are submitted by hospital physicians and then validated by hospital practitioners specialized in pharmacovigilance.
Therefore, tachycardia not leading to a hospitalization (mainly corresponding to the group 3C) is not usually reported. Moreover, the diagnosis of the cardiac disorder is performed mainly in hospital and confirmed by a specialist consultation before notification is sent to the regional center. Finally, the free- text search strategy through narratives was not feasible for the German SRS. The high rate of reports retrieved through narratives in the French SRS is due to the presence of a detailed summary of the cases (with biological exams or electrocardiogram (ECG) results), which is routinely done by the pharmacovigilance experts in French regional centers. Taken together, all these aspects make each SRS a unique entity for research and may also explain why four out of five novel signals were identified only in one database.
Therefore, multiple national database analysis appears informative when studying the arrhythmogenic potential of drugs, in order to capture the entire spectrum of drug-induced torsadogenicity. Our study should be interpreted with caution, in the light of recognized limitations affecting SRSs  [14]. In particular, we acknowledge that the lack of complete patient-related risk factors and information on drug administration do not allow a full causality assessment of individual reports. In addition, although a common protocol was adopted, the aforementioned peculiarities affecting each SRS (e. g. , population coverage, completeness of data, duplicate detection, drug and adverse event coding, possibility to access patient's medical history) did not allow final data aggregation. Nonetheless, we believe that this issue represents an important strength of the research, which clearly emphasized the identity and unique capabilities of national databases when investigating the torsadogenic risk of drugs.
Moreover, we cannot assess and compare the performance in signal detection among national SRSs in terms of time to first detection of a signal and the so-called false discovery rate  [15]. The former was not the core of this work, and it is also believed to be a minor issue as we have analyzed drugs on the market for several years. The latter was not a pre-specified purpose of this study, it implies the identification of reference compounds/events a priori, and depends on a number of factors: the type of event under investigation  [16] , threshold used for signal detection  [17] , the adopted algorithms, which may perform differently among databases  [18] , the presence of co-prescriptions  [19] , the comparator used for disproportion calculation  [20] , and the nature and size of the database (e.
In addition, there are three specific aspects to be mentioned. (1)  The automatic multi-step data mining approach performed in FAERS, especially drug codification, de-duplication process and handling of missing data, may affect signal detection. There is the possibility that reports coming from national European SRSs were entered into FAERS without filling all key information (e. g. , age, sex) and were therefore removed from published FAERS analyses  [21]. (2) FAERS is not the only international SRS for comparison: Vigibase and Eudravigilance represent large international databases, but did not fulfill our criteria (i. e. , published literature evidence) for signal detection when the study was planned. However, as previously demonstrated, FAERS reports on TdP were mostly submitted by US and European high-income/upper middle-income countries, thus, supporting the notion that FAERS data are highly informative of the global pattern of torsadogenic events  [11].
Future studies should consider the use of other international sources, especially in the light of the current role of Eudravigilance, which recently started data collection also on medicinal products not approved through the centralized procedure. (3) Additional cases were extracted through freetext search. Nonetheless, we strongly believe that this was unlikely to result in false positives. The analysis of narratives represents an actual added value of our study because the manual validation of cases increases the sensitivity of the search without affecting the overall performance (manual codification of narratives is performed for all events recorded in the Italian database, thus, no major distortions are likely to occur). Although the contribution of narratives appears minor as compared with the standard approach (approximately 6 % in France and 10 % in Italy), the fact that few reports originated from free-text analysis strongly indicates the good quality of the overall codification process, which, both in Italy and France, involves a number of regional centers.