dataesr/scientific-paragraphs-categorization huggingface dataset:
HPLC-ESI-MS Analysis Silymarin compounds were quantified using a LC-MS analysis that was performed on a Water 2695 Alliance (Waters-Micromass, Manchester, UK) coupled with a single quadrupole mass spectrometer ZQ (Waters-Micromass, Manchester, UK). LC-ESI-MS data were collected in the positive and negative modes. Data acquisition and processing were performed with MassLynx 4. 0 software (Waters-Micromass, Manchester, UK). The separation was performed at 35 • C on a core-shell column (Kinetex 5 µm XB-C18, 100 Å, LC Column 150 × 4. 6 mm, C18 with iso-butyl side chains, and with TMS endcapping, core-shell silica, Phenomenex Le Pecq France). The mobile phase was composed with a mixture of methanol (solvent A) and HPLC grade water (solvent B), both being acidified with 0. 05% formic acid. A linear gradient was applied for the mobile phase variation, ranging from a 5:95 (v/v) to 100:0 (v/v) mixture of solvents A and B, respectively, with a flow rate of 1.
Conformational and dynamics behavior of CYP2C9 WT and A477T The A477T mutation present in CYP2C9. 30 variant is located in the Substrate Recognition Site 6 (SRS6) (Supplementary S1 Fig). The comparison of the two crystal structures of losartan-bound CYP2C9 WT and A477T mutant did not show important differences between the two complexes except a rotation of the side chain of Q214 forming thus a hydrogen bond (HB) with the site chain of T477 in the mutant protein (PDB IDs 5XXI and 5X23  [13] ). Three losartan molecules were found to bind equally in the crystal structures of CYP2C9 WT and A477T mutant, two substrate molecules were present in the binding pocket and one of them was located in a peripheral site of the protein. One of the co-crystalized losartan molecule was bound far from the heme but nearby Q214 and the mutation residue 477 and involved in tight aromatic contacts with F476.
For the WT apo state, the most representative centroid structure corresponding to 11. 3% of the total 250 ns MD was very similar to the initial apo crystal structure of CYP2C9 PDB ID 1OG2  [25] ), (RMSD of the Cα atoms of 1. 01 Å) with a conserved β turn 4 conformation and unchanged F100 and F114 side-chain orientations. The other centroids showed different side-chain orientations for F100 and F114 belonging to the BC loop, a protein segment known to be a part of previously identified substrate access channels (2a, 2b, 2c, 2ac, and 2e)  [26, 27]  (see below for channel dynamics). The 15 centroid structures of the A477T apo mutant showed different binding-site conformations for F476 belonging to the ß turn 4. F476 has already been pointed out to be a key residue involved in the channel access to the catalytic pocket  [25, 27].
Therefore, significant interaction modifications occur in the mutant protein compared to the WT one corresponding to a movement of the β turn 4 in regard to the helix F of about 5 Å (see Fig  3B ). This shift may alter the well-known channel access including F476 (or so-called warfarin-binding subpocket found in the co-crystalized CYP2C9 -warfarin structure, PDB ID 1OG5  [25] ) and may destabilize the CYP2C9-substrate interactions in this zone. Hydrophobic interactions between F476 and other hydrophobic residues of the CYP2C9 binding pocket were monitored within a distance of 5 Å during the MD simulations (see  Fig 4 and S5 Fig). We observed that in the presence of the A477T mutation, F476 increased its contacts with L361 and F476 remained in stable contacts with L366 in the cases of ligand-bound states.
This observation is in accordance with the large movements of diclofenac observed during the MD simulations suggesting a dynamic behavior of the channels. Similar results have been reported in a previous study  [27]  for CYP2C9 bound to flurbiprofen, a NSAID similar to diclofenac. On the contrary, the large losartan molecule showing a very stable position in the binding pocket during the MD simulations triggered a very different behavior regarding the opening and closing of the channels. In fact, only three channels (2c, S1 and S2) were open during the MD simulations of losartanbound CYP2C9 in accordance with the two structures of the WT and A477T mutant co-crystalized with losartan, PDB ID 5XXI and 5X23  [13] , respectively. Interestingly, channel 2c was open in the losartan-bound structures, but closed in all other studied CYP2C9 states.
In order to analyze other possible positions of the losartan in the CYP2C9 active site we compared the docking poses of losartan with the recently reported co-crystalized structures, PDB ID 5XXI (WT-losartan bound) and 5X23 (A477T mutant-losartan bound). The most similar positions were found when the losartan was docked into the active site of the centroid number 4 of CYP2C9 WT apo representing 5. 76% of the entire MD simulations for WT apo (RMSD = 5. 41 Å) and of the centroid number 0 of A477T mutant-losartan bound representing 6. 59% of the entire MD simulations for the A477T apo mutant-losartan bound (RMSD = 4. 36 Å). These docking poses suggest that losartan can also adopt positions similar to the co-crystalized ones in CYP2C9 WT and CYP2C9 A477T mutant-losartan bound. Although RMSD are not very low, the losartan is similarly oriented in the docked and experimental structures (see S9 Fig).
System preparation Six crystallographic structures of CYP2C9 were taken from the Protein Data Bank (PDB) and analysed: the native sequence flurbiprofen-bound one (PDB ID 1R9O)  [28] , losartan-bound one (PDB ID 5XXI  [13] ), losartan-bound CYP2C9. 30 (containing the mutation A477T) (PDB ID 5X23  [13] ), and three structures of CYP2C9 containing the mutations K206E, I215V, C216Y, S220P, P221A, I222L, I223L: the apo CYP2C9 one (PDB ID 1OG2  [25] ), the warfarinbound one (PDB ID 1OG5  [25]  and the inhibitor 2QJ-bound one (PDB ID 4NZ2  [29]. We decided to construct our initial model of CYP2C9 based on the only one available apo crystal structure of CYP2C9 (PDB ID 1OG2) in order to getaway possible effects of bound ligands on the initial enzyme structure. We restored the 7 original CYP2C9 residues mutated in the crystallized enzyme.
Atomic charges derived from the electrostatic potential were calculated using the ChelpG scheme. All calculations were performed with the Gaussian 09 software (version C. 01, Gaussian, Inc. , Wallingford CT, 2010). Atomic parameters of losartan obtained with quantum mechanical calculations are given in S2 and S3 Tables. The force field for the heme Cpd I was taken from  [37]. The NAMD2. 6 program  [38]  was used for minimization and MD simulations. All systems containing water and ion molecules were firstly minimized with 5000 steps of conjugate gradient to remove poor contacts and relax the system. The systems were then submitted to a 1 ns MD simulation in order to equilibrate water and ion molecules located in the vicinity of the protein while the protein atoms were kept fixed. In a second stage, all constraints were removed and 5 MD production runs, each of 50 ns, were executed for each of the six CYP2C9 systems.
Substrate docking Five drugs known to be CYP2C9 substrates were docked into the crystal CYP2C9 structures PDB IDs: 1OG5 and 4NZ2 with restored native residues, 1R9O, 5XXI, 5X23, as well as into the initial models of the WT or the mutant A477T and into 240 representative conformations selected after the MD simulations for the six studied systems. The heme was kept in the intermediate Cpd I state. Drug protonation states were calculated at pH 7. 4 with the major microspecies option of the ChemAxon package (ChemAxon www. chemaxon. com). Interestingly, ChemAxon predicted two different pKa values for losartan, 4. 29 or 8. 0, starting from two different smile structures downloaded from BindingDB and DrugBank, respectively. We analyzed possible protonation of the losartan molecules co-crystallized with CYP2C9 in the PDB structures ID 5X23 and 5XXI and decided to consider the protonated state of the losartan, which corresponds better to the losartan environment in the binding sites.
Gasteiger atom charges were added using the AutoDockTools package. Molecular dockings were carried out with the Autodock 4. 2 program  [32]. A grid spacing of 0. 375 Å was used. The search space was centered on the protein binding sites with a box size of 24x25x30 Å. Ten independent docking runs were performed using the Lamarckian genetic algorithm  [45]  with an initial population of 150 random individuals and a maximum number of 25,000,000 energy evaluations. During docking, ligand molecules were flexible while the protein was kept rigid.
In the direct method, the LC% and EE% were calculated by dissolving a weighted amount of dry nanoparticles in acetonitrile and analyzing the caffeine absorbance in the resulting solution. This method allowed the amount of polymer to be estimated gravimetrically in which caffeine was contained, without relying on the assumption that all PCL was precipitated in the nanoparticle form (as assumed in the indirect method). To verify the quantification procedure, the caffeine mass balance was checked by summing up the amount in the supernatant and the one in the dry nanoparticles. In the case of recovered caffeine being lower than 97%, the data were considered unreliable and discarded. A list of the formulations for which loading capacity and encapsulation efficiency have been evaluated is given in Table  1.
X-ray Photoelectron Spectroscopy The nanoparticle surface composition was analyzed by XPS Versa Probe 5000, PHI Electronic (Chanhassen, MN, USA). The system is equipped with dual charging neutralization guns. They consist of an electron gun combined with an argon ion gun, at low energy, for minimizing the charging effect during data acquisition. The same argon gun was employed during the depth profile analyses, which was conducted in the etching mode, at higher energy, achieving an etching rate of 5. 9 nm/min. This etching procedure was performed simultaneously to the scanning analyses, providing a real time chemical information of the first external layers of the nanoparticles' structures.
The caffeine concentration was determined by UV spectrophotometry. Caffeine cumulative release data were collected and fitted by 5 models, i. e. , (i) Zero-Order; (ii) First-Order; (iii) Higuchi; (iv) Hixon-Crowell; and (v) Baker-Lonsdale model  [48, 49]. The regressions were carried out by interpolating the data until the asymptote in the cumulative release curve was reached. The contribution of free caffeine to the released curves was calculated by subtracting the free caffeine curve from the NP curves before regression. The model with the highest correlation coefficient, R 2 , was considered to be the best fit for the release kinetics. The zero-order model describes a system with a drug release rate independent of its concentration (Equation (  3 )). The data were plotted versus time. Q -Q 0 = K 0 t (3) where Q and Q 0 are the quantities of released caffeine at time t and t 0 , respectively, and K 0 (time -1 ), the zero-order release constant.
Acknowledgments: The contribution of  Giulia Delpiano  in collecting DLS, LC and EE data and preliminary work by  Ivan Bevilacqua  on influence of the caffeine solvent is gratefully acknowledged. DM acknowledges  Fiora Artusio ,  Marco Bazzano  and  Luca Lavagna  for the fruitful scientific discussions. DM gratefully acknowledges  Ermenegildo Zegna  for supporting his mobility by means of the action "EZ founder scholarship" and  European Union  for the SMDTex fellowship.
Cytokines were measured using Legendplex Mouse Inflammation Panel (Biolegend) or individual ELISA kit (R&D Mouse DuoSet IL-6; Mabtech IFN-γ, IL-17a ELISA kits; Ebioscience TNF-α ELISA kit). Liver histology and hepatic triglycerides measurement. A slice of left lobe of the liver was fixed in 4% PFA for 48 h and then transferred to ethanol, fixed in paraffin, trimmed, processed, sectioned into slices approximately 3 μm thick, mounted on a glass slide and stained with hematoxylin and eosin (H&E). Hepatic lipids were evaluated by quantifying the % lipid area using the software ImageJ  29. In vivo intestinal permeability and plasma sCD14. In vivo assay of intestinal barrier function was performed using fluorescein-conjugated dextran (FITCdextran, 3-5kDA) method, as previously described  30. Briefly, on the day of sacrifice, FITC-dextran (0.
16s rRNA gene sequencing. 16s rRNA gene sequencing of fecal DNA samples (collected at week 9 of CD or HFD) was performed as previously described  31. Briefly, the V3-V4 region was amplified and sequencing was done using an Illumina MiSeq platform (Illumina). Sequencing data were analyzed using the quantitative insights into microbial ecology (QIIME 1. 9. 1) software package. Sequences were clustered into operational taxonomic unites (OTU) at a 97% identity threshold using a closed-reference picking approach with UCLUST against the Greengenes reference database (version 13. 5) and phylogenetic tree was built using FastTree. Rarefaction was performed (13,000 sequences per sample) and used to compare abundance of OTUs across samples. Alpha-diversity was estimated using both richness and evenness indexes (Chao1, Shannon or number of observed species).
Beta-diversity was measured Bray Curtis distance matrix and was used to build principal coordinates analysis (PCoA) plots. Linear discriminant analysis (LDA) effect size (LEfSe) algorithm was used to identify taxa that are specific to diet and/or treatment. Sequencing data are deposited in European Nucleotide Archive (ENA) under the accession number PRJEB25364. Mouse gene expression and microarray analysis. Total RNA was isolated from the caecum of ASF-colonized mice using RNeasy Mini Kit, according to manufacturer's instructions. The RNA integrity was verified in Bioanalyzer 2100 with RNA 6000 Nano chips (Agilent Technologies). Only samples that have RNA integrity >9 were used for the study. Mouse transcriptomics was performed using SurePrint G3 Mouse GE 8x60K Microarray (Design ID: 028005, Agilent Technologies), according to manufacturer's instructions.
Microarray data are deposited in GEO under the accession number GSE111451 [https://www-ncbinlm-nih-gov. gate2. inist. fr/geo/query/acc. cgi?acc=GSE111451]. Microarray data processing and analysis was performed as previously described  28. Briefly, Agilent Feature Extraction Software v10. 7. 3. 1 was used to convert scanned signal into tab-delimited text files. p-value for each probe in each array was computed to test whether the scanned signal is significantly higher than background signal. Probes were filtered according to two criteria: only probes with p-value lower than 0. 05 were considered and present in at least 60% of arrays in at last one biological groups. Signals were then log2 transformed, normalized according to quantiles method, corrected for batch effect using ComBat method  32  with covariate and Agilent's probes ID were mapped to EntrezGeneID for further analysis.
Total bacterial RNA were purified using RiboZero. Library was sequenced using NextSeq 500 on the single lane 75 bp paired-end mode. Metatranscriptomics data processing and analysis was performed by first trimming unmapped reads and then were aligned using Bowtie2 aligner  34  to genome sequences retrieved form NCBI RefSeq database except for L. rhamnosus CNCM I-3690, which was mapped using a genome generated from Danone Nutricia. Mapped reads was quality filtered and only reads with MAPQ ≥ 5 was used. Reads were counted against transcript features database (General Feature Format (GFF) files from NCBI RefSeq database except for L. rhamnosus CNCM I-3690 which was predicted using Prokka v1. 12b) using the mode "union". Count data was further filtered using the following criteria: genes with 0 read across all samples and genes with <1 count per million were excluded.
For visualization, count data was transformed using Variance Modeling at the Observation Level (voom method)  35  and then normalized by quantile method. Differential analysis was performed using the exact tests for differences between two groups of negative binomial counts programmed in the R package "edgeR"  36. All the significant signatures (up-regulated or down-regulated genes) were annotated for enriched pathways using the package GOstats  37. Pathway definition for bacteria was downloaded from PATRIC database. Pathway lists were cleaned to exclude host pathways that were predicted from the same enzyme commission numbers (EC) assigned to both bacteria and host genes. As for host microarray, the method PLAGE was used to compare the pathway activity level between groups. p-values were adjusted using Benjamini-Hochberg's method to correct for multiple-testing problem.
Significant threshold is fixed at alpha = 0. 05 (type 1 error). All metatranscriptomics were deposited on GEO repository under the accession number GSE112387 [https://www-ncbi-nlm-nih-gov. gate2. inist. fr/geo/query/acc. cgi?acc=GSE112387]. Statistical analysis. In each experiment, multiple mice were analyze as biological replicates. No statistical methods were used to predetermine sample size. Sample size was estimated according to previous experience using the models described. No samples, mice or data points were excluded from the reported analysis. Animals were randomly assigned to each experimental groups. All analyses were performed unblinded except the histological analyses. Dot plots with a linear scale show the arithmetic mean. Bar graphs are expressed as mean ± standard error of mean (SEM).
Acknowledgements We thank the members of the  INRA-Jouy-en-Josas animal facility  and the ANAXEM germ-free platform for their assistance with the animal experiments;  Dr. Elena Verdu  for the ASF samples;  Dr. Jean-Marc Chatel  for his assistance during the experimental design process with ciclosporine experiments;  Julie Riviere  and  Abdel  for their assistance with the histological work,  Marjorie Lebarbenchon  for technical help. Our work has benefited from the facilities and expertize of the  @BRIDGE Histological and Microarray Platform of UMR 1313 GABI, High-throughput Sequencing Platform of I2BC, Biochemistry Platform of UMR 1149 Inflammation Research Center , and  IRS-UN Mass Spectrometry Platform of CRNH. J. M. N. holds a fellowship from  Canadian Association of Gastroenterology and Canadian Institute of Health Research.
Methods and findings A search in PubMed, Embase, ClinicalTrials. gov, Google Scholar, conference proceedings, and the WorldWide Antimalarial Resistance Network (WWARN) pharmacology database identified 31 relevant clinical studies published between 1 January 1990 and 31 December 2012, with 4,546 patients in whom lumefantrine concentrations were measured. Under the auspices of WWARN, relevant individual concentration-time data, clinical covariates, and outcome data from 4,122 patients were made available and pooled for the meta-analysis. The developed lumefantrine population pharmacokinetic model was used for dose optimisation through in silico simulations. Venous plasma lumefantrine concentrations 7 days after starting standard AL treatment were 24. 2% and 13. 4% lower in children weighing <15 kg and 15-25 kg, respectively, and 20.
A meta-analysis could potentially overcome this by pooling individual patient level data from several different studies and characterising both pharmacological properties and the influence of differences in study design, study size, comparator therapies, dose regimens, and inconsistent co-administration with fat. Lumefantrine exposure at day 7 has been evaluated in a meta-analysis previously, but not using a dynamic modelling approach to characterise and quantify pharmacological properties, the influence of covariates, and the relationship between drug concentrations and study outcome  [16]. The aim of this study was to assemble a large and therefore sufficiently powered pooled dataset of patients to examine the pharmacokinetic properties of lumefantrine. This approach enabled critical re-evaluation of the current twice-daily 3-day dosing regimen of AL, particularly in children and in pregnant women in their second and third trimesters.
Model building Pharmacokinetic data, clinical covariates, and efficacy data from patients treated with AL were used for this individual patient data meta-analysis. A search was conducted in PubMed, Embase, ClinicalTrials. gov, Google Scholar, conference proceedings, and the WorldWide Antimalarial Resistance Network (WWARN) pharmacology publication database to identify relevant antimalarial clinical studies published between 1 January 1990 and 31 December 2012  [16]  in which pharmacokinetic parameters as well as clinical covariates in patients treated with AL were recorded. The search strategy used key terms "lumefantrine pharmacokinetics" or "lumefantrine concentration" and "clinical study". Under the auspices of WWARN, investigators were invited to participate in this individual patient data meta-analysis.
Individual study protocols were available for all trials included, either from the publication or as a metafile submitted with the raw data. Individual patient data from eligible studies were shared, standardised, and collated using a methodology described in the WWARN clinical and pharmacology data management and statistical analysis plans  [17, 18]  and previously published research  [16]. Concentration-time data and clinical covariates from patients contributing 2 or more venous plasma samples were used to build the pharmacokinetic models. Patients contributing only 1 pharmacokinetic sample could not be used for model development as between-patient variability could not be dissected from residual variability. Lumefantrine concentration data in different sampling matrices (i. e. , venous blood and capillary plasma and blood) and lumefantrine concentration data after administration of different formulations (i.
e. , crushed tablets and dispersible tablets) were not used for the formal pharmacokinetic model development due to the sparse sampling schedules (i. e. , <2 samples per patient for crushed tablets, dispersible tablets, venous blood, and capillary blood) and to avoid introducing additional sources of variability (i. e. , capillary plasma samples). Consequently, lumefantrine concentration-time data in venous blood, capillary blood, and capillary plasma as well as lumefantrine concentration-time data from dispersible tablets and crushed tablets were evaluated using a post hoc correction factor at residual variability level with all other pharmacokinetic parameters fixed. Patients contributing <2 venous plasma samples were used for external validation of the developed population pharmacokinetic models.
Both a lumefantrine population pharmacokinetic model and a separate lumefantrine/desbutyl-lumefantrine population pharmacokinetic drug-metabolite model were developed. The natural logarithms of the concentration data were modelled in NONMEM v. 7. 3 (ICON Development Solutions, Ellicott City, MD) on a Windows XP operating system (Microsoft, Seattle, WA) with a G95 Fortran compiler (Free Software Foundation, Boston, MA). The developed lumefantrine population pharmacokinetic model was used to generate individual post hoc pharmacokinetic parameter estimates. The resulting lumefantrine concentration-time profiles were subsequently used to link lumefantrine plasma concentrations and clinical study outcome (i. e. , cure defined as absence of PCR-corrected recrudescent infection during follow-up) using a time-to-event approach (pharmacokinetic-pharmacodynamic timeto-event model).
Ethical approval All data included in this analysis were obtained in accordance with the laws and ethical approvals applicable in the countries in which the studies were conducted, and were from clinical studies in which blood samples were obtained with the knowledge and consent of the individuals to which they relate. Data were fully anonymised either before or during the process of uploading to the WWARN pharmacology database. Ethical approval to conduct individual participant data pooled analyses was granted to WWARN by the Oxford Tropical Research Ethics Committee (OxTREC).