dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-Crystal X-ray Diffraction Analyses These analyses of 1b-1e, 1g, and 1i were performed on a Bruker Kappa Apex II CCD diffractometer using ϕ,ω-scans of narrow (0. 5 • ) frames with Mo Kα radiation (λ = 0. 71073 Å) and a graphite monochromator. The structures were solved by direct methods in the SHELX-97 software  [63]  and were refined by the full-matrix least-squares method against all F2 in anisotropic approximation using SHELXL-2014/7  [64]. Absorption corrections were applied via the empirical multiscan method by means of the SADABS software  [65]. The hydrogen atoms' positions, except for H atoms in NH and OH groups, were calculated with the riding model. Nitrogen-bound and oxygen-bound H atoms were located on a difference Fourier map and refined freely. The resultant crystal structures were analyzed for short contacts between nonbonded atoms using PLATON  [66, 67]  and MERCURY  [68].
General crystallographic data for these compounds are summarized in Table  2. CCDC 2016713-2016718 contain the supplementary crystallographic data for benzimidazoles 1b-e, 1g, and 1i, respectively. These data can be obtained free of charge via http://www. ccdc. cam. ac. uk/cgi-bin/catreq. cgi or from the Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; Fax: (+44)-1223-336-033 or by e-mail: deposit@ccdc. cam. ac. uk.
Acknowledgments The authors are very grateful to " Ministère de l'Enseignement Supérieur et de la Recherche Scientifique de l'Algérie " as well as University of Ferhat Abbas Sétif 1 for the funding and the support of the project. Our thanks go to SALEM Pharmaceutical Laboratories headed by  Dr. A. Maiza  for the supply of the different chemicals used in this study. Pr. M. Boutahala  is gratefully acknowledged for the provision of the equipments used in the analyses as well as the laboratory facilities.
Acknowledgements This work was generously supported by the  Austrian Science Funds (FWF) : Project No. P30237  (financed by the  Austrian National Foundation for Research, Technology and Development and Research Department of the State of Upper Austria). The used NMR spectrometers were acquired in collaboration with the  University of South Bohemia (CZ)  with financial support from the  European Union  through the  EFRE INTERREG IV ETC-AT-CZ program  (project  M00146 , " RERI-uasb "). We are grateful to  Markus Himmelsbach  ( JKU Linz ) for support with HRMS analysis and to  Kirill Faust  ( JKU Linz ) for support with X-ray analysis. This work has also been partially supported by  INSA Rouen of Normandie, Rouen University of Normandie , the  Centre National de la Recherche Scientifique (CNRS) ,  EFRD , and  Labex SynOrg  ( ANR-11-LABX-0029 ), and by  Region Normandie (CRUNCh network).
More precisely, 25 μL of IB or PEG solution was pre-incubated with 10 μLo f HA and 10 μL of inhibitor before adding 5 μLo f enzyme. All reactions were carried out in duplicate. The reaction mixtures were analyzed on a Beckman P/ACE MDQ (AB Sciex, USA) CE apparatus equipped with a photodiode array detector. The control of CE was performed using the Beckman 32 karat software (Beckman Coulter, USA). Uncoated fused silica capillaries purchased from Polymicro Technologies (Phoenix, AZ, USA) were used. The total length of the capillary was 57 cm (47 cm to the detector) with 50-μm inner diameter. The detection wavelength was set at 200 nm (bandwidth 10 nm). New capillaries were conditioned by performing the following rinsing cycles: 1 M NaOH (10 min), water (10 min), and BGE (10 min). At the start of each working day, the capillary was rinsed with 1 M NaOH (5 min), water (5 min), and BGE (5 min), followed by applying + 15 kV (10 min)  [44].
Methods We conducted a retrospective and comparative cohort study of all admitted patients aged 65 years old and older with a prescription of oral or intravenous PPI at the internal medicine department of a university hospital. These drugs were prescribed by patients' general practitioners (GP) or specialists. Patients who were admitted from December 2016 to May 2017 (before the implementation of our experiment) were enrolled in the control group and patients admitted from July to August 2017 were enrolled in the PPI awareness experiment group. The patient demographics (age, gender) and information about their PPI treatment (brand name, dosage, therapeutic indication, duration of treatment) were collected in the electronic health record (Millenium™, Cerner, Kansas City, USA). Any missing data were collected during the medication reconciliation which consisted of a pharmaceutical interview of the hospitalized patient and a telephone call to his community pharmacy.
Full dose was considered as 20 mg of omeprazole or rabeprazole, 40 mg of esomeprazole or pantoprazole, and 30 mg of lansoprazole. The criteria for appropriate use of PPIs were established according to the French national consensus guidelines. [17]  The observed adverse events were attributed to PPIs when no other etiology has been highlighted by the medical team and when the adverse event regressed after treatment discontinuation. An intervention of the pharmacovigilance center was requested for the most uncertain cases in order to evaluate the cause of the adverse event. Records of prescription modifications by hospital physicians during patient hospitalization were collected in the database. A pharmaceutical intervention is defined as a review of medicines and discussion about potentially inappropriate prescriptions with the hospital physician (including residents).
In addition to this presentation, a pocket sized guideline on the correct use of PPI was distributed. A patient handout relating to the appropriate use of PPIs was also distributed. Some non-appropriate PPI prescriptions were also highlighted and discussed during this multidisciplinary team meeting. Informed consent was obtained from all individual participants included in the study. This study was approved by the Ethics Committee of our hospital. Results were expressed as mean ± standard error of mean (SEM). Chi-square tests were performed using GraphPad™ Prism version 5 (Graphpad Software, San Diego, CA, USA) to compare the groups. The level of significance was p <0. 05.
Quinacrine binds to GSTA1 (GSTα protein) and inhibits its activity of promoting binding of reduced glutathione to active compounds Various GST class family member proteins were screened computationally for the binding activity with quinacrine using software iGEMDOCK. GSTA1 gene which encodes for GSTα protein was found to be binding with QC among all GST proteins. GSTA1-GSH bound complex structure file (PDB accession no. 1PKW) was selected for further docking study. Modeled complex of GSTA1-Ethacrynic acid (EAA) was used as reference for affinity, nature and location of binding of quinacrine (QUN) with GSTA1. Best docking poses were selected for analysis and the iGEMDOCK binding energy values obtained were -89. 3 (kcal/mol) for QUN and -53. 5 (kcal/mol) for EAA. Further, the detailed analysis of the complexes revealed that quinacrine binds mainly to G-site  [30]  with residues Tyr9 and Gln54 involved in hydrogen bonding, residues Arg15, Glu104, Asp101(chain B) involved in π-cation/anion interaction, and residues Leu107, Ala216, Phe10, Gln67 involved in hydrophobic interaction.
RNA isolation and RT-PCR analysis Total RNA was isolated directly from 25 cm 2 cell culture flask. Briefly cells were grown and exposed to different concentrations of quinacrine in 25 cm 2 cell culture flasks. The total RNA was isolated using Ribozol reagent and 1 µg of total RNA was reverse transcribed to cDNA using the cDNA synthesis kit as per the manufacturer's instructions. This first strand was then further used for amplification of selected targets using specific primers (Supplementary Table  1 ) by RT-PCR method. 18S rRNA was used as internal control and the band densitometry analysis was performed using NIH Image J software.
Molecular docking study of GSTA1 Computational docking of the quinacrine and Ethacrynic acid onto GSTA1 was performed using iGEMDOCK docking program  [48]. Stable docking option with population size N = 300, generations (80) and no. of solution (10) was selected. Repeated studies were performed with same compound and settings to avoid false positive and negative results. Structure files of Glutathione (GSH) in complex with GSTA1 (PDB accession no. 1PKW), GSTA1 in complex with ligand Chlorambucil (PDB accession no. 4HJ2) were downloaded from RCSB protein databank  [49]. Structure of compound Ethacrynic acid (EAA) was taken from bound complex with GSTP (PDB accession no. 2GSS). Residues in proximity of 6-8 Å radius of the ligand (EAA) were selected and structurally equivalent residues in the structure file 1PKW used as binding site for docking study.
ACKNOWLEDGMENTS AND FUNDING AS thanks  DBT, Govt. of India  for financial assistance in the form of a pilot project grant [ 6242-P59/RGCB/PMD/DBT/ANSR/2015 ] for this study. MK acknowledges  UGC JRF  [No. 3374/ (NET. DEC 2014 )] for fellowship support. BC thanks  DST-FIST for infrastructure research grant  No. SR/FIST/LSI-536/2012  and  Department of IT, BT  and  S&T and Government of Karnataka. SN acknowledges  DST INSPIRE SRF  [ IF140949/2015 ] for fellowship support. This study was funded by  Department of Biotechnology, Govt. of India  grant no. 6242-P59/RGCB/PMD/DBT/ANSR/2015.
2006; Holsapple et al. 2006; Meek et al. 2014). Thus, the ultimate approach comprises a WoE evaluation that weighs the human relevance of pathways and allows for quantitative analysis with the highest level of sensitivity and specificity achievable. To identify the various MOAs underlying the nongenotoxic carcinogenic potential of agrochemicals, we conducted an extensive analysis of data on 411 unique agrochemical active substances that have been evaluated for carcinogenicity by the United States Environmental Protection Agency (US EPA) and European Chemicals Agency (ECHA; CL inventory). This manuscript describes the results of this analysis while identifying knowledge gaps and exploring a way toward a novel mechanism-based approach.
Data collection For the identification of MOAs involved in the nongenotoxic carcinogenicity of agrochemicals, we compiled a list of agrochemicals based on the agrochemicals that have been evaluated for carcinogenicity by the US EPA (US EPA Annual Cancer Report 2016) and on the EU pesticides database (Carc classes 1 A, 1B and 2; (http://ec. europa. eu/food/plant/pesticides/eu-pesticides-database/public/?event=activesubstance. selection&language=EN). In total, this list comprises 411 unique agrochemicals. For all substances, toxicity assessment reports and documents were collected from the databases of the European Chemicals Agency (ECHA), the European Food Safety Authority (EFSA), the National Institute of Environmental Health Sciences (NIEHS), the United States Environmental Protection Agency (US EPA), the International Agency for Research on Cancer (IARC), the Joint FAO/WHO Meeting on Pesticide Residues (JMPR), and the Agency for Toxic Substances and Disease Registry (ATSDR).
Exclusion and inclusion criteria In general, only studies that were considered to be of sufficient quality according to criteria laid down by the respective agencies were included. In case only summaries of assessment reports were to be found or solely assessment reports from one organization, data were deemed too little for analysis. An exemption to this rule was made when only reports of one organization (e. g. EFSA, US EPA) could be found but the report provided qualitatively acceptable and detailed information. In this case, the substance was included for analysis. Substances labeled too old generally presented with reports pre-1990 and often overlapped with cases with too little information. In case younger reports were available (>2000) but the assessment was solely based on carcinogenicity studies (pre-1990, a substance was still assigned to the category "too old/too little information".
Substances showing a treatment-related increase in the incidence of benign or malignant tumors in a long-term carcinogenicity study without in vivo evidence of genotoxicity were labeled as 'nongenotoxic carcinogens' (NGTXC). These substances are listed in Supplementary Table  1. Subsequently, for every substance labeled 'nongenotoxic carcinogen', data were collected on the observed tumor type(s), sex and species of the test animal as well as available details on dose and (proposed) carcinogenic MOA. To harmonize tumor data, all included tumors were categorized according to standardized pathological nomenclature (INHAND guidelines) by a board-certified pathologist with ample experience in carcinogenicity studies.
Identification of MOAs for NGTXC Where available, details on the carcinogenic MOA of substances were collected from the combined assessment reports. This was complemented with existing knowledge on MOAs as reported in the scientific literature or the AOP database (AOP wiki; www. aopwiki. org). Thus, a list of MOAs underlying the reported tumors was compiled. Tumors for which no underlying MOA could be deduced from the reports and/or literature were labeled 'unknown'.
Results In total, data were collected on 411 unique substances. From this initial list, 59 substances were excluded based on the availability of too little or too old information. From the remaining 352 substances, 25 substances were excluded for analysis based on evidence of a genotoxic MOA underlying the observed carcinogenicity, and for 157 substances no treatment-related changes in tumor formation were observed; these were therefore designated as noncarcinogenic and also excluded for analysis. The remaining 170 substances were categorized as nongenotoxic carcinogen (Figure  1 ; Supplementary Table  S1 ) and were subjected to further analysis. For the 170 substances considered as nongenotoxic carcinogen, 340 unique occasions of treatment-related tumor formation were registered. This clearly shows that on average more than one treatment-related tumor type occurred per substance.
2010; Klaunig 2018 ). In our dataset, it was only explicitly reported as underlying mechanism for 7 out of 340 cases of tumor formation, either related to formation of reactive metabolites or disturbance of mitochondrial function. This type of oxidative stress-related tumor formation was reported in liver (hepatocellular adenoma/carcinoma or hemangioma/hemangiosarcoma), spleen (hemangioma/hemangiosarcoma), and the lymphoid system (lymphoid tumor), related to three substances, mostly in mice, either in females alone (three cases) or in both sexes (three cases). In one case, oxidative stress-related tumors were observed in mice (both sexes) and rat (female). One case of follicular cell adenoma/carcinoma, observed in both male mice and male rats, was reported to relate to oxidative stress as a result of metabolism of the iodine-containing substance.
Thyroid peroxidase inhibition as endocrine mediated model Another MOA could be discriminated in the database underlying thyroid follicular cell tumors that are not related to induction of liver enzymes. This MOA (observed and described for four substances) is related to specific substance-mediated inhibition of the enzyme thyroid peroxidase resulting in reduced production of T4, leading to decreased serum T4 levels which in turn activates the HPT axis leading to increased TSH production  (Motonaga et al. 2016 ; Figure  5(B) ). In all four cases, these thyroid-specific tumors were observed both in rats and in mice.
Discussion The present manuscript comprises an analysis of an unbiased selection of agrochemicals for which carcinogenicity study reports are publicly available. This selection includes among others herbicides, insecticides, acaricides, and additives to pesticide formulas listed in the US-and EU-databases. About one-third of these substances could be categorized as nongenotoxic carcinogens with an average of approximately two tumor types per substance. In the analysis, only tumors that show a treatment-related tumor response have been included. Despite the wide variety of tumors in various organs distilled from the carcinogenicity studies, a prevalence of MOAs involved in carcinogenesis could be seen. Scrutiny of the unknowns will probably result in additional MOAs or more elaborate networks.
g. hormonal and proliferation studies) from sub-chronic data. Databases such as the Toxcast/Tox21 database can provide important data on substance-induced effects in this respect, but will need careful interpretation for various reasons. High-throughput screening approaches such as ToxCast mainly inform on MIEs, while also information on KEs may be needed to allow for a more comprehensive understanding of the MOAs involved. Furthermore, meaningful interpretation of in vitro data and incorporation of in vitro techniques in a MOA-driven approach strongly depends on the possibilities for relevant in vitro to in vivo extrapolation and a detailed understanding of important caveats of in vitro models such as lack of biological barriers, organ-organ interactions, and relevant metabolism. Based on the biological role of the key aspects tested in human tumor formation, as well as their threshold of activation, human relevance can be defined.
This will then be grounded on the simple assumption that early mechanistic endpoints will be human relevant unless clearly dismissed from a qualitative point of view. Moreover, an alternative approach should combine a high negative and a high positive predictive value. For pharmaceuticals, an approach focusing on NPV appears adequate. However, in contrast to pharmaceuticals, agrochemicals provide a class of chemicals that is relatively data poor when it comes to data on pharmacological or nonpesticidal MOAs. Therefore, any MOA-driven approach in this respect should include evidence for both the activation as well as the absence of activation of relevant MOAs. In order to make this approach work, it is important to identify MOAs for the tumor types with a currently unknown MOA. Subsequently, data on KEs in the respective MOAs has to be collected to identify which KE are suitable for inclusion in the new MOA-driven approach.
Acknowledgments The authors gratefully acknowledge  Dr. Andrea Terron ,  European Food Safety Authority, Parma, Italy , and  Dr. Stephanie Melching-Kolmuss ,  BASF, Limburgerhof, Germany , for being sparring partners throughout the project. Also, the authors would like to thank  Dr. Shalenie den Braver-Sewradj ,  National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands , for critical proofreading of the manuscript. The authors note with appreciation the value of four sets of comments and critiques provided by reviewers selected by the Editor and anonymous to the authors.
Over a wide range of time points and concentrations, the intensities and kinetics of multiple fluorescent dye signals can record intracellular activities in parallel  [18]. Following image acquisition, HCA software can perform object segmentation of the subcellular structures from which the intensities originate allowing quantitative morphometric analysis  [16]. Parameters can therefore calculate changes in cell number (CN), nuclear area (NA), nuclear intensity (NI), mitochondrial membrane potential (MMP), plasma membrane permeability (PMP), and intracellular calcium (IC). This yields information in the number, shape, and size of individual cells, assessment of their nuclei and other membrane organelles, as well as mitochondrial and plasma membrane barrier function  [17, 19, 20]. A HCA model human hepatocytes demonstrated high sensitivity for detecting potential human toxicity as part of an analysis of 250 marketed drugs compared to conventional cytotoxicity assays  [21].
However, such manual and single-signal assays are more labour intensive, time consuming, and costly, as well as being less precise than automated assays. No single cell parameter will definitively detect adverse effects and accurately predict toxicity potential in vivo. HCA can therefore provide subtle mechanism of action information for surfactant-based PEs since there is usually a close association of mucosal perturbation with increases in drug permeability  [27]. Previous HCA studies by our group revealed that when Caco-2 cells on 96 well plates were exposed to 8. 5 mM C 10 for 60 min, NI, IC, NA, PMP, MMP, and CN were all increased  [28]. Higher concentrations of C 10 and longer exposure times caused cell death as determined by the MTT assay, so these data yielded insights into the mechanism of action of C 10.
That study also provided rank-order data on the parameters for the sodium salts of a series of MCFAs with C 10 not necessarily being the most effective, but having more favourable parameters and better solubility than higher chain length MCFAs. SNAC has never been assessed by HCA in any cell type. It can therefore allow conclusions to be made on mechanistic differences between effects of SNAC and C 10 on Caco-2 cells when compared head-to-head. To explore paracellular permeability changed, the first aim of this study was to compare the effects of SNAC and C 10 on intestinal permeability of fluorescein isothiocyanatedextran 4,000 Da (FD4), transepithelial electrical resistance (TEER), and TJ localization in Caco-2 monolayers. Second, we compared cellular changes on Caco-2 cells exposed to C 10 and SNAC head-to-head by performing conventional cytotoxicity assays and HCA to provide insight into their epithelial mechanism of action.
Cells were counterstained with Hoescht (1 μM) to visualize the nuclear DNA. The slides were washed with PBS and mounted in Dako fluorescence mounting media. Images were examined using a Zeiss Axiovert 200 M inverted fluorescence microscope (Carl Zeiss, Germany) with Zeiss AxioVision Rel. 4. 8 software for controlling the image recording, microscope stage and image merge. Images were processed using ImageJ®.
Statistical analysis Statistical analysis was carried out using Prism-5® software (GraphPad, San Diego, USA). Statistical significance was measured by one-way ANOVA with Dunnett post-hoc tests for in vitro calculations of P app and in vitro conventional toxicity assays. For HCA study, variations in PE concentration induced changes were normalized to the untreated control and each treatment time was analysed by one-way ANOVA with Dunnett's post-test to compare treatment groups to the untreated controls. Results are presented as the mean ± standard error of the mean (SEM). A significant difference was considered if p < 0. 05. IC 50 values were extrapolated from the concentration-response graph for conventional assays by plotting triplicate data points over a concentration range and calculating values using regression analysis.
Summary of conventional cytotoxicity assay data The decline in the cell viability indicated by the MTS assay in response to both agents at very high concentrations is likely caused by an interference with mitochondrial respiration. Over 2 h, results indicated an overall lower in vitro cytotoxicity profile for SNAC compared to C 10 in Caco-2 cells using these five end-point assays. When comparing the IC 50 of C 10 and SNAC across the assays (Table  1 ), a trend was observed for both agents of initial MMP perturbation at low concentrations, followed by an increased nuclear, lysosomal, and plasma membrane permeability at high concentrations. Mitochondrial metabolic activity as measured by MTS was associated with the highest IC 50 for each PE. Common features across most assays were cytotoxicity at 20 mM C 10 and 40 mM SNAC upon acute exposure, confirming that the capacity of these agents to increase FD4 flux across monolayers at such concentrations was due to toxicity and not to TJ opening.