dataesr/scientific-paragraphs-categorization huggingface dataset:
Amino acid sequence analyses and homology modeling Sequence analysis (GenBank 6WTT-A  [20  and multiple alignments were performed using the BLAST and CLUSTALW programs  [36]. The prediction of the protein secondary structure was performed using the DSSP program  [37] , while the editing of the alignment including the superimposition of secondary structures was conducted using the ESPript 3. 0 program  [38]. The automated comparative protein structure homology modeling server, Geno3D (https://geno3d-prabi. ibcp. fr) generated the 3D structure models of SARS-CoV-2 M pro using the published structure as template (PDB-code 6WTT)  [39]. PyMOL (http://www. pymol. org) and ViewerLite 5. 0 softwares (https:// www. 3dsbiovia. com) were used to visualize and analyze the generated model structures and to construct the graphical presentations and illustrative figures.
Docking methodology The three-dimensional x-ray crystal structure of SARS-CoV-2 M pro (pdb code: 6WTT) was retrieved in pdb format from Protein Data Bank with resolution 2. 15 Å  [39]. After that, the cocrystallized ligand of the SARS-CoV-2 M pro structure was extracted. Then, it was prepared in AutodockVina by removal of water and solvent molecules, addition of polar hydrogens, removal of the bound ligand and partial charge assignment and saved as. pdbqt format using AutodockVina to be included as a reference in the virtual screening. The grid box was defined by selecting the cocrystallized inhibitors to keep the center of each docked Apigenin analogues with same dimensions of binding box. Moreover, the grid box center was adjusted X = 4. 9, Y = 27. 64 and Z = -11. 206 with dimensions for SARS-CoV-2 M pro.
Its size was set to 60 × 50 x 50 Angstroms to cover the active site. The structure of Apigenin and Apigenin analogues were downloaded from the PubChem search (https://pubchem. ncbi. nlm. nih. gov/). AutodockVina program was performed between Apigenin analogues and SARS-CoV-2 M pro for molecular docking analysis such as binding types of interactions, binding energies, inhibition activities, ligand efficiency and distances. Molecular docking scores were set as AutoDock tools of the molecular graphics laboratory software package by keeping the analogue flexible  [40]. Boceprevir was used as control to compare the molecular docking results with the Apigenin analogues.
In-silico Osiris/Molinspiration and ADMET analysis Osiris and Molinspiration analyses are performed to describe 2D models and to indicate the type of pharmacophore site  [42, 43]. These analyses are employed to predict pharmacophore site and biological activity of the apigenin analogues and to determine the drug-likeness score. The acute toxicity in rodent models and chemical classification of the test compounds were predicted by GUSAR  [44]. It analyzes compounds based on the quantitative neighborhoods of atom descriptors and prediction of activity spectra for substance algorithm and correlates the obtained results with the SYMYX MDL toxicity database. Furthermore, it classifies them based on the Organization for economic co-operation and development (OECD) chemical classification manual.
Structural aspects The modeling of SARS-CoV-2 Main proteases from different variants and the characterization of the mutations structural impacts was investigated. M pro model (306 residues) was built by the aid of the automated homology modeling, Geno3D, web server using SARS-CoV-2 Main protease (PDB ID: 6WTT, chain A) as homolog. The M pro sequence exhibited high identity with that of the template (99. 76%) suggesting the high-quality models that could be obtained. The obtained model showed a perfect superimposition of the Cα with 6WTT regarding to the very low RMSD (root mean square deviation) value estimated at 0. 914 Å. The Main protease of SARS-CoV-2 was showed as composed of three domains (Figure  2 ). The domains I and II have an antiparallel β-barrel fold. The cleft between these domains generates the substrate-binding site.
General Unrestricted Structure-Activity Relationships (GUSAR) software  [44]  was used for quantitative in-silico toxicity prediction for Boceprevir, Apigenin and Apigenin analogues in rats with four types of administration (intraperitoneal, intravenous, oral and subcutaneous). As displayed in Table  2 , the different LD50 values suggests that availability of the tested inhibitors for metabolism by the liver is a major factor for its toxicity. The LD50 value of Apigenin 7-glucoside-4'-p-coumarate and Cosmosiin was higher than Boceprevir for intravenous (IV), oral, and subcutaneous (SC) routes of administration. Only the LD50 value of Cosmosiin was higher through intraperitoneal (IP) route. By the OECD chemical classification system, only the Apigenin 7-glucoside-4'-transare class 4 when administered through Intraperitoneal compared to class 5 for the other compounds.
Discussion The virus mutates and/or changes its antigenicity causing loss of the vaccination efficiency. Many experimental and computational efforts done to identify a genetic stable target for anti-SARS-CoV drug design were provided. In this work, the genetic stability of Main proteases of SARS-CoV -2 from different variants was confirmed. In addition, the superimposition of the crystal structures of M pro with and without ligands has RMSD ranging from 0. 26 to 0. 38 Å, indicated that the binding pocket is pre-shaped  [54]. Thus, M pro is an attractive target for anti-SARS-CoV drug design. The inhibitors of this enzyme can be found by structure-based design  [35] , by enzymatic assay of existing inhibitors of other virus main protease such as HIV or HVC protease inhibitors  [55]  or by screening of chemical database using docking approaches.
6, -7. 2 and -7. 5 kcal/mol, respectively  [11]. Apigenin 7-glucoside-4'-p-coumarate occupies the S1′, S2, and S4 subsites while Boceprevir filled the S1, S1′, and S2 subsites of SARS-CoV-2 M pro active site. Generally, small size inhibitors may bind only at S1 and S2 subsites  [11, 58]. Boceprevir with its medium size may bind to S1′, S1, and S2 subsites  [59, 60] , while some large inhibitors may bind to S1', S1, S2, and may extend through S3 subsites as for example alphaketoamide (PDB: 6Y2F)  [61, 62]. These compounds have dissimilar structures and sizes, yet they bind and inhibit the activity of SARS-CoV-2 M pro. All hydrogen bonds and hydrophobic interactions are shown in Table  1. The common residues in active site of M pro SARS-CoV-2 stabilizing all analogues best poses are His 41, Ser144, Gly143, Thr25, Thr26, Glu166, Asn142 and especially the catalytic Cys145.
In the present case, we observed that the IC 50 to curb the free radicals was significantly reduced (p < 0. 05) in the OUNJ extracts as compared to the SXNJ ones (Fig. 10e ). Moreover, the extracts were also evaluated for their in vivo anti-oxidant potential. Scopolamine was used to increase the oxidative stress level in experimental animals and it was found that enhanced stress significantly altered various biochemical parameters such as glutathione, catalase, SOD and nitrite in the negative control group (p < 0. 01) (Fig. 10a, 10b, 10c, 10d ). It has been observed that changes in the concentration of glutathione, catalase and nitrite in the OUNJ group were significantly different as compared to the negative control group (p < 0. 01) as well as the SXNJ group animals (p < 0. 05). Data indicated that SXNJ extracts altered the selected biochemical parameters, as well (p < 0.
Introduction Potentially inappropriate prescriptions (PIPs) of antidiabetic drugs (ADs) (PIPADs) to patients with type 2 diabetes mellitus (T2DM) have been reported in some studies. The detection of PIPs in electronic databases requires the development of explicit definitions. This approach is widely used in geriatrics but has not been extended to PIPADs in diabetes mellitus. The objective of the present literature review was to identify all explicit definitions of PIPADs in patients with T2DM.
Search strategy A method for developing explicit definitions of PIPADs in patients with T2DM has been published previously  [23]. The present systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  [24, 25]. The protocol was registered in the PROSPERO database (reference: CRD42021250028). Methodological assistance was provided by three librarians at the University of Lille (Lille, France). At each stage, the results were validated by four reviewers (A. V. , M. L. , J-B. B. , and, S. G. ). We systematically searched publications in the Medline (via PubMed), Embase, Web of Science and, Scopus databases. Studies were limited to those published in English and French between January 2010 and June 2021. Publications before 2010 were not included because drug strategies for the management of diabetes have changed particularly rapidly over the last ten years.
Data extraction Full manuscripts were obtained for all titles and abstracts that met the inclusion criteria and were then coded with NVivo software (version 12, QSR International Pty Ltd. Australia 2020). Two reviewers (E. G. and P. Q. ) independently examined the full text and extracted any mentions of explicit definitions. A third reviewer (M. L. ) was called upon to resolve any differences of opinion and helped to form a consensus. At this stage, the following records were excluded: studies that did not include T2DM, studies of animals or cultured cells, pharmacological studies, studies about mechanisms of action, and definitions that considered patients with T2DM but did not consider ADs.
Limitations Firstly, the search was limited to the Medline via PubMed, Embase, Web of Science and, Scopus database. A second limitation is the possible omission of certain keywords and MeSH terms, some PIPADs definitions might not therefore have been found. Thirdly, the review period started on January 2010 and ended in June 2021, i. e. more than six months before submission of the manuscript. However, we checked that an update did have not an impact on the present results. Fourthly, the most frequently found definitions of PIPADs were based on older patients or patients with renal insufficiency, whereas T2DM is not limited to these patient profiles. Fifthly, we focused solely on drug therapy in patients with T2DM, regarding insulin therapy, it is less easy to define explicitly the risks of inappropriate prescriptions.
This research was funded by  PreciDIAB , which is jointly supported by the  French National Agency for Research  ( ANR-18-IBHU-0001 ), by the  European Union  ( FEDER -agreement NP0025517 ), by the  Hauts-de-France Regional Council  (agreement  20001891/NP0025517 ) and by the  European Metropolis of Lille  (MEL, agreement 2019_ESR_11).
Introduction Apart from its traditional use in the post-authorization phase for safety assessment, risk management, and life cycle benefit-risk evaluation  (Flynn et al. , 2022) , the interest in Real-World Evidence (RWE) in the pre-authorization phase of medicines development is increasing exponentially  (Li et al. , 2021; Leufkens et al. , 2022; Purpura et al. , 2022). In this context, regulatory agencies should adapt their strategy (Heads of Medicines Agencies and European Medicines Agency, 2019a). RWE is defined as "the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of Real-World Data (RWD)"; the latter includes "big data", i. e. , "extremely large datasets which may be complex, multi-dimensional, unstructured and heterogeneous, which are accumulating rapidly and which may be analyzed computationally to reveal patterns, trends, and associations" (Heads of Medicines Agencies and European Medicines  Agency, 2019b).
Static and dynamic features of AlphaFold2 modeling The first version, AlphaFold1, involved a deep-learning and convolution-based approach in which a set of separately PDB-trained modules were used to produce guides, which, combined to physics-based criteria like energy potentials, resulted in predicted models  26. This approach was improved in the following version, AlphaFold2, by the use of graphs manipulated by attention mechanisms in which coupled sub-networks generated endto-end models mostly based on geometrical pattern recognitions. Local physics could optionally be applied as a final AMBER refinement step for energy minimization, particularly to optimize side chain geometry. A particularity of the AlphaFold2_advanced notebook (AF2A) is to give easy access to the several alternate structural models automatically generated from a unique set of amino acid sequences used as input.
Prediction of CYP102A1 dimer structure and alternate conformations The potential of AF2A for ab initio prediction of CYP102A1 structure and of its alternate conformations was evaluated. The polypeptide chain of CYP102A1 is 1,049 amino acid residues long, and is a natural fusion between a N-terminal P450 domain and a Cterminal reductase domain that is related to diflavin reductase protein family  9. Current technical limitations of AF2A implementation did not allow us to easily model more than a total of ~1,500 amino acid residues in a single run. CYP102A1 dimer accounts for 2,098 amino acids, making single step modelling technically difficult. However, modelling by parts of this dimer followed by the global assembly of these parts remained possible and was evaluated. The full-length monomer of CYP102A1 can be easily modeled in a single block and the resulting prediction was already released in the AlphaFold Protein Structure Database (APD at https://alphafold.
Determining mechanisms leading to dimer formation and design of synthetic dimeric monooxygenase Mechanisms leading to the formation of the unusual CYP102A1 dimeric structure were questioned with the aim to design synthetic dimer structure of other P450 enzymes mimicking the high specific activity of CYP102A1. Dimerization of the P450 domain and dimerization of the reductase domain are two events that likely occurred independently during the evolution. To find traces of these events, the NCBI database was searched by PBLAST for sequence similarities with the reductase domain of CYP102A1 excluding its P450 domain from the query to avoid biasing the search. Surprisingly, the large number of sequence hits featuring more that 50% sequence identity with the CYP102A1 reductase domain were all bacterial natural fusions with a P450.
Methods Softwares. AF2A modeling was performed using the Aphafold2_advanced Python notebook 34 that was run on Google Collaboratory cloud computing facilities using GPU and its large (48 Go) dedicated memory. Google Colab Notebooks were run using default parameters except for the selection of the pTMscore ranking procedure and the num_models parameter that was adjusted between 5 and 20 depending on cases. Resulting models were visualized and analyzed using ChimeraX and Pymol softwares. Matchmaker and ISOLDE plugins in ChimeraX were used respectively for RMSD minimization and for model docking into cryo-EM density maps. AliView algorithm was used for sequence manipulations and visualization and the MIA-Muscle plugin for sequence alignments. TCoffee from Expresso web server was used for structure-based sequence alignments and the Prodigy (PROtein binDIng enerGY prediction) webserver was used to calculate the binding free energies (G) of interacting domains.
ConsRank and CoCoMaps web applications were used respectively to rank models of complexes and to generate interaction maps of contacting residues. Selection of models among alternate AF2A predictions. Each predicted structure (from 5 to 20) was examined. Structures in which domains were separated or overlapping were considered to sign the absence of any predicted complex. Overlaps, when present, did not affect individual domain folds and were never observed with domains belonging to the same polypeptide chain. When formation of a defined complex was predicted, the use of the AF2A recommended pTMscore was not systematically consistent with calculated binding affinities using the Prodigy software. In that case, the retained structure was based only on this last energetic criterion (i. e. the best affinity).
What this study adds • To the best of our knowledge, this is the first study to concurrently compare the similarity among complete datasets from DIRs in terms of inclusion of drug pairs, recommendations for clinical management, severity and evidence of DDIs. • Considerable variation was identified in all types of information for DDIs, which has important clinical implications for patient safety and requires efforts towards harmonisation and standardisation. ANSM, being considered as the official source of information relevant to DDIs for French clinicians. Micromedex is a leading clinical information resource, listed as one of the statutorily named compendia in the Medicaid program and is widely used in the United States. 20, 21 The BNF and Thesaurus are publicly available online, while Micromedex can only be accessed via subscription.
| Data extraction Automated web data collection (web scraping) was executed for BNF and Micromedex in Python 3. 6  22  with terms of use that permit data collection. Thesaurus is a portable document format (PDF) file that is curated and updated annually. An R package (IMthesaurusANSM  23  ) enabled the automatic data extraction from the original document (version September 2019). The types of extracted information from each DIR are summarised in Table  1. We mapped DDIs from Thesaurus at the drug class level (e. g. , beta blockers) to their constituent individual drug ingredients using a mapping table available on the ANSM website. We also excluded DDIs from Micromedex containing drug combinations (e. g. , hydroxyamphetamine/tropicamide), as those simply collated DDIs from the combination's individual ingredients; hence, only single ingredient drug interactions were considered.
| Clinical management recommendations We Basic pre-processing involved text conversion to lowercase, drug name blinding (i. e. , replacement of all drug names with a common string), and sentence tokenisation using the Natural Language Toolkit (NLTK) in Python 3. 6. 27 e following advice categories were initially considered: (n = 4507), we developed a bespoke text classification process in Python using a methodology that has been widely implemented in similar tasks and provided the desired functionality while keeping the level of complexity low (Figure  S2  in the Supporting Information). First, we annotated a subset of randomly selected unique sentences (n = 200) by considering the above-mentioned categories. Then, each labelled sentence was tokenised into its constituent tokens (i. e. , words) and stemming (i.
| Impact on clinical decision support Examples of missed medicines and DDIs from the different DIRs can be found in Table  5. Some ingredients were surprisingly missing from the DDI section of some of the DIRs, such as levetiracetam from Thesaurus and rupatadine from Micromedex. A few ingredients were not licensed in the respective countries (e. g. , enalaprilat, an intravenous formulation of enalapril, is not available in the UK or France) or had been discontinued (e. g. , ketorolac in France or maprotiline in the UK) at the time of data collection. Micromedex, however, contained drugs that were discontinued or not approved in the US. The difference in size might also be partly related to the nature of the different DIRs. Micromedex is a commercial knowledge base, while BNF and Thesaurus are maintained by professional and regulatory bodies, respectively.
This is a complex interaction, which leads to decreased plasma concentrations of the active However, there are limitations to this study. First, no updates have been taken into consideration since the date of data retrieval (i. e. , offline data). Therefore, the results and conclusions of this study provide an overview of their similarity and consistency at that specific point in time, although no major updates usually occur. Second, Thesaurus contained a few DDIs originally reported at the drug class level, which were associated with multiple severity ratings. Hence, some standardised DDIs at the ingredient level were assigned more than one severity rating. In the BNF, there was a limited number of DDIs having multiple severity levels depending on the described clinical outcome. In both cases, the highest severity rating was considered for further analysis.
ACKNOWLEDGEMENTS We would like to thank  Ms Angela Hall  from the  Library  and  Knowledge Service  at the  Liverpool University Hospitals NHS Foundation Trust  for facilitating access to Micromedex. The authors would like to acknowledge  Antoni Wisniewski  and  Isobel Anderson  for providing feedback on this research. E. K. is funded for her PhD through joint funding from the  Engineering and Physical Sciences Research Council (EPSRC)  (Project Reference:  EP/R51231X/1 ) and  AstraZeneca.
1 Extracted information from the drug information resources DIR Extracted fields Categories BNF (accessed • Drug name (a) Active June 2018) • Interactant name pharmaceutical • Description ingredients (APIs) • Severity (where (e. g. , atropine); present) (b) Drug classes (e. g. , • Evidence (where combined hormonal present) contraceptives); (c) Herbs and supplements (e. g. , peppermint oil); (d) Foods and beverages (e. g. , grapefruit juice). Thesaurus - • Drug name (a) Drug ingredient; September • Interactant name (b) Drug classes. 2019 update • Mechanism (if (accessed July available) 2020) • Severity index • Additional information (specification for drug class, etc. ) • Clinical information (i. e. , manifestation, management) Micromedex • Drug name (a) Drug ingredients; In-depth • Interactant name (b) Combination drugs; answers • Interaction effect (c) Food; database • Summary (d) Tobacco; (detailed • Severity (e) Lab tests.
The recent progress of the designed Cur nanoformulations to treat various inflammatory diseases, such as arthritis, neurological diseases, respiratory diseases, UC, and psoriasis, is also discussed, thus providing new options and insights to address the limitations of Cur in clinical applications. The safety and therapeutic efficacy of Cur nanoformulations are important factors being considered in clinical trials. Related studies have shown that short-term intravenous administration of liposomal Cur at 120 mg/m 2 was safe (NCT01403545)  [116]. In another randomized double-blind placebo-controlled clinical trial, 1% nanocurcumin gel combined with 0. 1% triamcinolone acetonide was shown to be an effective treatment strategy to improve the efficacy of oral lichen planus compared with triamcinolone acetonide alone (IRCT20190523043678N1)  [117].
Y. pseudotuberculosis isolates and taxonomic assignment Yersinia isolates, together with some clinical and demographic data, are regularly sent to the French YNRL for enteric yersiniosis by medical laboratories for complete characterization. Isolation and taxonomic assignments are performed as described by Savin et al. (14)  based on a 500-gene cgMLST scheme designed to identify all the species of the Yersinia genus, as well as the lineage. A total of 359 isolates of Yptb received at the YNRL between 1991 and 2020 for complete characterization were genotypically assigned. In addition, genomic data of 9 isolates of Yptb lineage 16 with a clinical origin  (1969 to 1990)  were extracted from our database for comparison of their genetic relatedness.