dataesr/scientific-paragraphs-categorization huggingface dataset:
In spite of massive world-wide research into COVID-19, there is still much we do not know and understand. The depiction of available data along the pathway can identify specific knowledge gaps and direct further research. Furthermore, controversial and contradictory findings can be compared and put in relation to known underlying mechanisms for further clarification. Another benefit of the framework comes from the fact that AOP-supported descriptions of various organ manifestations may result in AOP networks that reveal, not only the interrelation between various outcomes, but also major pathway interconnections and hub KEs that can serve as diagnostic markers or targets for therapeutic interventions. Furthermore, AOP networks help elucidate the role of co-exposure and the ways comorbidities and other modulating factors influence the disease outcome.
WHAT CAN COVID-19 RESEARCH DO FOR AOPs? The support that AOPs provide for COVID-19 research reaches back to the development of the AOP framework itself, as referred to on several occasions above. This synergistic feedback loop is emphasized by the key principles of the framework: AOPs are stressor-agnostic, they are modular, and they are living documents. Thus, the information and understanding gained from unraveling the mechanisms (MIEs, KEs, KERs, and AOs associated with COVID-19) can be assumed to reach back into and inform other fields (e. g. , toxicology and chemical safety assessment). In fact, such cross-feed of information between fields, such as cancer research and disease models development, has repeatedly been proven to be useful. For example, big data analytics within toxicology frequently applies data sets originally derived from other fields to develop and test mechanism-driven predictive models  (35) (36) (37) (38).
Once structured and organized to be associated with the modules of AOPs, this type of data becomes broadly informative across disciplines  (28). In addition, the application of the AOP framework to map a disease of high societal relevance will provide valuable experience for the evolution of the framework itself. This effort can be expected to inform potential needs for adaptations of the framework to improve the strategies for its maintenance and sustainability. As stated by Carusi and colleagues, "although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive longterm planning and strategies"  (26).
CONCLUSIONS Overall, the development of AOPs and AOP networks for COVID-19 can be expected to support a set of at least nine research needs, as identified here, including (i) means for improved knowledge exchange between experts from different disciplines, from virology to clinical research, (ii) increased synthesis of prior knowledge from other fields, such as toxicology and medicine, (iii) increased understanding of the variable courses of the disease, (iv) means for identifying knowledge gaps, (v) means to compare and contrast data and knowledge to better understand controversies and uncertainties, (vi) increased understanding of the interrelation of the various outcomes, (vii) detection of new biomarkers (which have direct evidence of causality and essentiality for the outcome, in line with the principles of the AOP framework), (viii) understanding of the meaning of co-exposure and potential risk factors, and (ix) increased mechanistic understanding of the disease to support human-relevant data generation.
In addition, thanks to today's state-of-the-art technological advances with COVID-19 there has been a never-before-seen tsunami of data focused on one particular human disease. Application of the AOP framework to the systematic organization of this unprecedented amount and breadth of data can be expected to effectively support the evolution and improvement of the framework itself. Thus, in the time of the COVID-19 pandemic, the AOP framework, and in particular the European Commission CIAO project, has the potential to provide a concrete example of how a structured approach based on inter-and multidisciplinary synergy among scientists and health professionals could facilitate data exchange, identify knowledge gaps, redirect funding, and increase success in the understanding of the disease. Such an effort enables resilience, improves emergency preparedness, and empowers quick action to tackle future global health emergencies.
Acknowledgements We thank  Liliane Rayer  and  Florence Tessier  (library of botany of the  MNHN ) for their help finding historical and recent bibliography,  Jacques Florence  and  Florence Tessier  for their help translating some old Latin and Greek texts,  Jacques Florence  for his help using the ICBN. Dario De Franceschi  (MNHN),  Bernard Riera  (CNRS) and  Julien Bachelier  ( Freie Universität Berlin ) for proofreading the manuscript. Financial support for  Felipe Espinosa 's PhD was provided by the  Colombian government agency Colciencias  and by the  ISYEB (Institut de Systématique, Évolution Biodiversité, Paris). The MNHN gives access to the collections in the framework of the  RECOLNAT national Research Infrastructure.
sativa extracts, but isolating active fractions or identifying active compounds from these extracts would allow for the attribution and rationalization of the antidiabetic behavior of N. sativa. That is why this study aims to investigate the inhibitory effect of N. sativa extracts and fractions against the activities of intestinal α-glucosidase and pancreatic α-amylase in vitro, and to explain the inhibitory effect of these fractions against these enzymes by identifying their active compounds responsible for this effect, determining their modes of inhibition, and evaluating their toxicity. The identification and characterization of the compounds were carried out by GC-MS and HPLC-DAD in comparison with local database abs standards. Inhibitory actions of potential antidiabetic compounds were further characterized by the determination of their IC 50 values and inhibition modes both by analyzing enzyme kinetic parameters and by molecular modeling.
sativa extracts, but isolating active fractions or identifying active compounds from these extracts would allow for the attribution and rationalization of the antidiabetic behavior of N. sativa. That is why this study aims to investigate the inhibitory effect of N. sativa extracts and fractions against the activities of intestinal α-glucosidase and pancreatic α-amylase in vitro, and to explain the inhibitory effect of these fractions against these enzymes by identifying their active compounds responsible for this effect, determining their modes of inhibition, and evaluating their toxicity. The identification and characterization of the compounds were carried out by GC-MS and HPLC-DAD in comparison with local database abs standards. Inhibitory actions of potential antidiabetic compounds were further characterized by the determination of their IC50 values and inhibition modes both by analyzing enzyme kinetic parameters and by molecular modeling.
Inhibition Mechanism Analysis 2. 5. 1. IC 50 Determination To calculate the IC 50 value, various concentrations (from 10 to 100 µM for apigenin, (-)-catechin and gallic acid, and from 1 to 10 µM for acarbose) were used. The assays were conducted as described above. The concentration of each inhibitor causing the 50% activity inhibition was calculated from % activity vs inhibitor concentration curves using software ED50 plus v1. 0 (Mexico, Mexico). The experiments were repeated three times. Fractional inhibitory concentrations (FICs) at the IC 50 level were calculated for both potential inhibitors, as follows: FIC = IC 50 drugs in combination with acarbose/IC 50 potential inhibitor alone.
Molecular Docking Analysis Docking data for the binding of apigenin and gallic acid with pancreatic α-amylase, (-)-catechin, and gallic acid with α-glucosidase were performed with PyRx virtual screening tool software, which includes Autodock 4 and Autodock Vina (The Scripps Research Institute, La Jolla, CA, USA) and Pymol v2. 1. 1 (Schrodinger, New York, NY, USA) to predict the conformation of these molecule ligands within the appropriate target binding site of pancreatic α-amylase (PDB: 2QMK) and α-glucosidase (PDB: 5NN5). The software Discovery Studio 2020 (Dassault Systemes, Vélizy-Villacoublay, France) was used to determine the type of interaction and visualization in 2D, and UCSF Chimera 1. 14 (San Francisco, CA, USA) was used for the 3D representation of molecules and interaction residues.
The docking protocol employed was described by Proença et al. [10]  for α-glucosidase and Proença et al. [11]  for α-amylase, but using human pancreatic α-amylase (PDB: 2QMK) and α-glucosidase (PDB: 5NN5). The 3D structure of each ligand was retrieved from PubChem (available online: https://pubchem. ncbi. nlm. nih. gov/ (accessed on 25 March 2021)). Initial virtual screen with the whole enzyme was conducted with the following sizes of the grid box: 81 Å × 82 Å × 85 Å (for α-glucosidase) and 60 Å × 78 Å × 63 Å (for α-amylase) in the x, y, and z dimensions, respectively, to identify the most favorable binding site predicted by the program based on the lowest docking energy and maximum docking number. The docking was then refined using this site with a grid box of 25 Å (square). The present study was conducted by using Swiss albinos mice from the local animal husbandry department of the Faculty of Science, Mohammed First University (Oujda, Morocco).
At the end of the incubation period, the liquid in the tubes was removed by aspiration and the radioactivity ( 125 I-cortisol) was measured using a gamma counter (Packard Cobra II auto-gamma counter; Packard Instrument Co Inc, Meriden, Connecticut). The assays were performed using cortisol radioimmunoassay CT test kits (RADIM, Freiburg im Breisgau, Germany) including calibrator samples ranging from 10 to 800 ng/mL. The calibration equation was computed using Prism (GraphPad Software, La Jolla, California) by plotting the log of cortisol concentrations (ng/mL) vs the logit B/Bo. The best curve was determined by the polynomial second-order equation. The limit of quantification of the cortisol assay for the urine samples was set at 10 ng/mL. 6β-OH-cortisol concentrations in urine were determined by using a 2-step, quantitative competitive enzyme immunoassay technique.
The assay was performed using 6β-hydroxycortisol kits from Stabiligen (Villers-lès-Nancy, France) including calibrator samples ranging from 50 to 1000 pg/mL. The calibration equation was computed using SoftMax Pro software (Molecular Devices, Sunnyvale, California) by plotting the log of 6β-OH-cortisol concentrations versus the A/Ao (when A was standard or sample 6β-OH-cortisol absorbance and Ao was the standard 0 absorbance). The best line was determined by using the 4-parameter logistic model. The limit of quantification of the 6β-OH-cortisol assay for the urine samples was set at 50 pg/mL.
Statistical Analysis In both studies, the safety population comprised all subjects who received at least 1 dose of the study drug and in the PK analysis, all subjects who were exposed to GLPG1205 and who had available and evaluable data were included. The PD analysis population in study 1 comprised the safety population excluding all major protocol violations and the GLPG1205 10-mg dose (as no PD effect was expected at this dose, based on preclinical pharmacological data); due to the dose adaptation (GLPG1205 200 to 150 mg) in cohort E (see Results section for further details), only day 1 data from this cohort were included in the formal PD analysis. The number of subjects included in each study was expected to give a reasonable precision around the estimates derived for PK and PD evaluations.
After multiple administrations (day 14), mean inhibition was observed before dosing on day 14 (24 hours after dosing on day 13) for GLPG1205 50 mg once daily (78. 6%) and 100 mg once daily (83. 5%) versus 4. 6% for placebo. Inhibition of ligand binding to the GPR84 receptor was sustained over time; at 24 hours after dosing (day 15), 77. 9% and 71. 6% mean inhibition was observed for the GLPG1205 50-mg and 100-mg once-daily doses, respectively, versus 21. 1% for placebo. Inhibition of ligand binding to GPR84 was analyzed at day 14 for the 200/150-mg once-daily dosing regimen; however, due to the protocol violation of lowering the dose from day 8 onward and the small number of subjects remaining on day 14, the data are not presented.
Administration of GLPG1205 50 mg once daily in all age groups, with or without a loading dose of 250 mg, did not reveal any safety concerns. A separate study has demonstrated that there is no food effect on GLPG1205 exposure (data on file at Galapagos). 17  After a single administration of GLPG1205, GPR84 receptor occupancy measured as inhibition of ligand binding to GPR84 was observed for the 30to 800-mg doses compared with placebo with a concentration-dependent effect. After 13 days of oncedaily GLPG1205 administration, ligand binding was already strongly inhibited before dosing for all doses. Based on the results from both the SAD and MAD parts of study 1, it can be concluded that GLPG1205 caused an extensive and sustained reduction in GPR84 receptor occupancy, suggesting that the 50-mg dose of GLPG1205 may potentially be considered as one of the therapeutic doses in further clinical development.
Acknowledgments The authors thank the following:  Lien Gheyle , principal investigator ( SGS, Belgium );  Annegret Van der Aa , former head of Clinical Operations (formerly of  Galapagos, Belgium );  Morgane van der Eecken , clinical trial coordinator ( SGS, Belgium ); and  Steve de Vos , therapeutic area leader ( Galapagos, Belgium ), for their role in both the first-in-human and effect of aging studies. The authors also thank  Marie-Hélène Gouy , clinical pharmacology lead (formerly of Galapagos, France), and  Lisa Allamassey , statistician (formerly of  Galapagos, Belgium ), for their role in the first-in-human and effect of aging studies, respectively. Additional thanks are conveyed to  Roland Blanqué ,  Isabelle Parent , and  Corinne Saccomani  of the  Translational Sciences Laboratory at Galapagos, France , for their assistance with developing the binding assay.
stuDy population We analyzed two different cohorts of patients. The first cohort was included to perform a descriptive analysis of the metabolomic profile of patients with decompensated cirrhosis with and without ACLF to determine whether patients with ACLF show a specific metabolomic signature. The second cohort was an intervention cohort aimed at assessing the effects of simvastatin plus rifaximin, a pathophysiological-based treatment, on the metabolomic profile of patients with decompensated cirrhosis. The first cohort included 42 patients with cirrhosis randomly selected from a prospectively collected database of consecutive patients admitted to the Liver Unit of the Hospital Clinic of Barcelona for acute decompensation of cirrhosis. Demographic, clinical, and analytical data were collected prospectively at admission and during a 3-month follow-up period.
untargeted metabolomics by ultrahigh performance liquid Chromatographytandem mass spectroscopy Analyses were performed by Metabolon, Inc. (Morrisville, NC) using ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC/ MS-MS) (Durham, NC). Metabolic extracts were obtained by dry and reconstitution procedures in different solvents. All methods used a Waters ACQUITY UPLC and a Thermo Scientific Q-Exactive highresolution/accurate MS interfaced with a heated electrospray ionization (HESI-II) source. Four methodological procedures were used to analyze different compounds depending on their hydrophilic/hydrophobic and acid/alkali properties. Raw data were extracted, peak identified, and processed by Metabolon. Peaks were quantified using area under the receiver operator characteristic curve (AUROC) analysis.
statistiCal anD Data analysis Metabolome values were transformed to log2, adding 0. 01 to avoid indetermination. A total of 940 metabolites were identified in the plasma samples of patients from the descriptive cohort, and 1,041 metabolites were detected in the plasma samples of patients included in the intervention cohort. Metabolites corresponding to drug compounds or derived metabolites were excluded from the analysis; therefore, the final analysis included 837 metabolites in the descriptive cohort and 985 metabolites in the intervention cohort. A systematic analysis of the annotated metabolites was performed by manually querying different databases, including the Human Metabolome Database (HMDB) (http://www. hmdb. ca) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.
genome. jp/kegg). Principal component analysis (PCA) is a dimensionality-reduction method that transforms a set of variables into a reduced set of noncorrelated variables. PCA was performed with the metabolomics data of the descriptive and the intervention cohort using the open-source statistical computing environment R (http://www. r-proje ct. org/) and the prcomp package. To select metabolites that differed between patients with ACLF versus decompensated cirrhosis and patients treated with simvastatin and rifaximin versus placebo, we applied the unpaired Student t test for each pairwise comparison between groups followed by false discovery rate control using the Benjamini-Hochberg approach. (23) athway analysis was conducted using MetPA (through https://www. metab oanal yst. ca/),  (24)  which combines several advanced pathway enrichment analyses along with the analysis of pathway topological characteristics across more than 800 metabolic pathways.
However, based on existing literature we can speculate which of the drugs may be responsible for the different findings. Finally, the intervention trial was aimed at evaluating the safety of two doses of simvastatin, and the follow-up period is short; therefore, the number of clinical outcomes developed during follow-up is low. In this regard, the analysis of the effects of changes on the metabolomic profile on clinical outcomes is limited and should be taken with caution. A large multicenter international study with longer treatment duration and evaluation of clinical outcomes is underway (LIVERHOPE Efficacy Trial, clinical trials. gov NCT03780673). In conclusion, our results show that treatment with simvastatin and rifaximin modulates the metabolomic profile of patients with decompensated cirrhosis and decreases and deactivates a number of metabolites and metabolomic pathways associated with cirrhosis progression and development of ACLF.
Acknowledgment: We acknowledge the support of  Nicki van Berckel  in the overall management of the  LIVERHOPE  project. We also acknowledge the support of  Roser Poblet  and  Beatriz Márquez  in administrative issues related to the current work and manuscript handling. We are very thankful to the physicians and nurses of the different liver units participating in the study. Finally, we thank the patients and their families for their participation in the  LIVERHOPE  project.
Introduction. -Evidence regarding a possible association between psoriatic manifestations and use of calcium channel blockers (CCBs) is sparse. Currently, psoriatic manifestations are not listed in the summary of product characteristics (SmPC) of CCBs. In this context, we aimed to investigate the association between psoriasis and CCB exposure. Methods. -We reviewed spontaneous reports recorded in the French national Pharmacovigilance Database (FPVD) between 1985 and 2019. The association between CCB exposure and risk of psoriasis was assessed using the case/non-case method. We also analyzed literature data. Results. -Ninety-four reports of psoriatic manifestations after CCB exposure were recorded in the FPVD. Both induction and exacerbation cases were observed. Time to onset was less than 2 years in 64% of reports and outcome was favorable in 71% of reports after CCB discontinuation.
g. verapamil, diltiazem, bepridil)  [10]. CCBs are associated with a wide spectrum of cutaneous reactions  [11]. The first reports of psoriatic manifestations with CCBs date back to late 1980s/early 1990s  [12] [13] [14]. Two decades later, data from epidemiological studies regarding psoriasis risk with CCB use remain conflicting  [15] [16] [17]. Currently, psoriatic manifestations are not listed in the summary of product characteristics (SmPC) of CCBs, and more information may be warranted in order to judge whether it is necessary to include a mention of psoriasis in the SmPC. In this context, we aimed to investigate the association between CCB exposure and risk of psoriasis, by analyzing data from the French national pharmacovigilance database (FPVD) and data from the literature.
Descriptive analysis We performed a retrospective, descriptive analysis. All spontaneous notifications of psoriasis with CCB exposure from the inception of the database in 1985 to 31 December 2019 were reviewed. For each CCB marketed in France (amlodipine, felodipine, isradipine, lacidipine, lercanidipine, manidipine, nicardipine, nifedipine, nimodipine, nitrendipine, verapamil, diltiazem and bepridil), a broad search in the FPVD was performed using a query with the following high level terms: "psoriatic conditions" and "psoriatic arthropathy" (MedDRA® version 20. 1). Only notifications in which CCBs were considered as "suspect" were included in the study. For each notification, we systematically recorded variables related to the patient (i. e. , age, gender, and medical history); related to the drug (international nonproprietary name and action taken against the ADR); and related to the ADR (i.
Case/non-case study We assessed the association between psoriasis and CCB exposure using a case/non-case approach, which evaluates the disproportionality of combinations of a drug with a particular ADR in pharmacovigilance databases. Cases are all reports of the ADR of interest (in this study, psoriasis). Non-cases are all other ADRs reported in the database during the same period of time (in this study, cutaneous ADRs except psoriasis). This method allows comparison of drug exposure (in this case, to CCBs) among cases and non-cases by calculation of the reporting odds ratio (ROR) and its 95% confidence interval (CI). This is a validated method of safety signal detection  [20] [21]. For each CCB, the ROR was calculated using the following formula: (a/c)/(b/d), where (a) is number of psoriasis reports with the suspected CCB, (b) is the number of cutaneous ADRs except psoriasis with the suspected CCB, (c) is the number of psoriasis reports with all other suspected drugs, and (d) is the number of cutaneous ADRs except psoriasis with all other suspected drugs.
The 95% CI was calculated using the Woolf method  [22]. To validate our results, we chose lithium as a positive control  [23]. BBs, ACEIs and ARBs were taken into account as a potential confounders, and the stratification method was applied. For each stratum (e. g. BB use stratum and BB nonuse stratum), the ROR and 95% CI were calculated, and homogeneity between the stratified RORs was tested using the Breslow-Day test  [24]. If RORs were not significantly different, they were pooled to calculate the adjusted Mantel-Haenszel ROR; significance of the adjusted ROR was assessed using the Cochran-Mantel-Haenszel test  [25]. Statistical analysis was performed using SAS 9. 4 Software (SAS Institute Inc. , Cary, NC, USA).
Review of the literature reports We reviewed published articles dealing with psoriasis risk with CCB use from the following databases: Medline®, Cochrane®, Scopus®, ScienceDirect®, and Google Scholar®. For each CCB (amlodipine, felodipine, isradipine, lacidipine, lercanidipine, manidipine, nicardipine, nifedipine, nimodipine, nitrendipine, verapamil, diltiazem and bepridil), the search was conducted as follows: (CCB name) AND ['psoriasis' OR 'pustular psoriasis' OR 'psoriatic erythroderma' OR 'psoriatic arthritis' OR 'psoriasiform dermatitis'].