dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow Cytometry Analysis of Immune Cells Tumors and spleens were harvested at 9 days after the first injection of αPD-1 in mice bearing MCA-205 tumors and at 11 days after the first injection of αPD-1 in mice bearing E0771 tumors. TILs and splenocyte suspensions were prepared as previously published  (10). Two million tumor cells or splenocytes were preincubated with purified anti-mouse CD16/CD32 (clone 93; eBioscience) for 30 minutes at 4°C before membrane staining with anti-mouse antibodies listed in Supplementary Table  S4. For intracellular staining, the FOXP3 staining kit (eBioscience) was used. Dead cells were excluded using the Live/Dead Fixable Aqua Dead Cell Stain Kit (Life Technologies). Samples were acquired on a BD Fortessa 16-color cytometer (BD), and analyses were performed with FlowJo software (BD).
CC Trial in Non-Cancer Patients As part of a CC clinical trial study (NCT04058392), two participants living with HIV with undetectable viremia were invited to take CC capsules (total 1 g, Natural Traditions) per day for four weeks in addition to their antiretroviral therapy. Feces were collected by each participant before and after four weeks of CC intake, and were kept at 4°C for a maximum of 16 hours before freezing at -80°C. The CC study was approved by Health Canada, the Natural and Non-prescription Health Products Directorate, and the research ethics board of the McGill University Health Centre (study number 2020-5903). Each participant provided written informed consent before entering the study. The study is conducted in accordance with the Declaration of Helsinki.
Acknowledgments We thank  Liliane Meunier  and  Véronique Barrès  of the molecular pathology core facility of the  CRCHUM  for performing the microscopy slide scanning. We thank the  CRCHUM animal facility ,  Montreal Clinical Research Institute  (IRCM) animal facility, and  Aurélie Cleret-Buhot  of the experimental imaging core facility of the  CRCHUM  for performing the image analysis and immunofluorescence images for figures. In addition, we thank  Thibeault Varin  of  IBIS (Institut de Biologie Intégrative et des Systèmes, Université Laval ) for his help with 16S rRNA sequencing. This study was financially supported by  Institut du Cancer de Montréal  (to  M. Messaoudene  and  B. Routy );  Canadian Institute for Health Research  (to  B. Routy  and  B. Castagner );  Prefontaine Family  and  Fondation du CHUM  (to B.
Data Availability Statement The raw read data generated from the RNA-seq, metagenomic sequencing, and 16S rRNA sequencing performed in this study are deposited under BioProject ID's PRJNA783624 (RNA-seq and metagenomic sequencing) and PRJNA706824 (16S rRNA sequencing). The newly constructed 5. 0M nonredundant mouse gut microbial gene catalog and associated Metagenomic Species Pan-genomes reconstructed thereof are available through dataverse INRAE-MGP (https://doi. org/10. 15454/L11MXM).
Static SANS and data fitting SANS experiments were performed on the D22 spectrometer at the Institut Laue Langevin, Grenoble. Static measurements used 'banjo' Hellma cells (1 mm path length) with first detector and collimator distance of 5. 6 m, second detector distance at 1. 4 m and neutron wavelength l = 6 Å. The measured wavevector q range was 0. 01 to 0. 8 Å À1 and the transmission of the samples was relatively high (40. 8) with low background values of B0. 2 cm À1. The sample rack was kept at room temperature, 25 1C. The data were reduced with GRASP software and data fitting (of radially-and sector-averaged spectra) was performed with SasView 5. 0. 2, employing the random head-tail sheet with Caillestructure factor model and the fitted parameters compared for the different NaCl concentrations.
Serpentine channel microfluidic setup A commercially available serpentine, B3 m long, tubular microfluidic device etched in high optical and neutron transmittance crown glass  35, 41, 42   ), where Q is volumetric flow rate and R eq is the equivalent radius of an ellipsoid calculated as 2A/P with A is the cross-sectional area, 0. 086 mm 2 , and P the perimeter, 1. 08 mm). Oscillatory rheology data for the 2% NaCl reference system  44  indicate a G 0 and G 00 crossover at B0. 45 s À1 corresponding to characteristic timescales of B2 s. The flow conditions examined can thus be considered 'strong', and well within the shear regime expected for MLV formation. 32 Measurements were recorded for every channel section with optical microscopy and every 7th channel section for SANS. The position of the fluid on the microfluidic chip was calculated inlet length + serpentine channel section number Â 24 mm (i.
Microscopy Following 60 min infusion of the sample onto the chip, the flow was stopped and microscopy images were then acquired with Olympus BX41 reflection microscope equipped with crosspolarised filters (U-AN-2 analyser and U-PO3 polariser). Image capture was done with a Basler acA2000-165um camera controlled with PylonViewer using 10Â/0. 25 Ach and 50Â/0. 50 MPlanFLN Olympus objectives under 10 000 ms and 200 000 ms exposure time respectively. Data were analysed with ImageJ by measuring the average light intensity and FFT for images acquired along the serpentine channel.
36 n order to resolve the nanoscale arrangement of the L a structure, we employed microfluidic-SANS and a narrow, rectangular, beam slit (1. 4 cm Â 200 mm) precisely aligned along the horizontal channel sections, as shown in Fig. 3b , illuminating a 0. 7 mL sample volume. Given the low illuminated volume (0. 7 mL), each SANS spectrum was recorded for 20 min to acquire adequate statistics. The centre of every 7th channel section, along the y direction, was sampled, corresponding to 17 channel positions along the serpentine channel, as illustrated in Fig. 5b. The scattering data at the start of the microchannel are generally anisotropic, as expected for aligned L a sheets, for all NaCl concentrations. Sector analysis of I(q) reveals greater alignment along the normal (xz) direction to flow, than along the flow direction, illustrated in Fig.
Acknowledgements We thank the  Institut Laue Langevin (Grenoble)  for beamtime; data available on doi. ill. fr/10. 5291/ILL-DATA. 9-10-1651(static SANS) and doi. ill. fr/10. 5291/ILL-DATA. 9-10-1706(flow SANS). We thank  EPSRC  and  Imperial College London  for a PhD studentship for LD. JTC acknowledges the  Royal Academy of Engineering (RAEng, UK)  and  Procter & Gamble  for funding a Research chair.
e heart was stabilized 15 minutes before the start of recording. e parameters of the cardiac activity were recorded continuously by a computer system dedicated to the acquisition with the IOX 2. 9. 5. 43 software (EMKA Technologies, France). After 15 minutes of stabilization in standard perfusion condition (Krebs-Henseleit), H. bonariensis extract was continuously perfused in increasing doses (1. 56; 3. 125; 6. 25; 12. 5; 25; 50 mg/ml) every 3 minutes. After each range, the heart was flushed with Krebs-Henseleit to measure reversibility.
Neutron data analysis Water calibration measurement was made to correct for the efficiency of the detector. The background measurement was made by measuring the empty chamber, which was subtracted from experimental data to improve the signal/noise. The ILL in-house software package LAMP was used to reduce and normalise the data. [35]  The 2D data was integrated in the vertical direction to a 1D curve and plotted as scattering intensity against scattering angle (2h). The scattering angle is converted to the scattering vector q by: q ¼ 4psinh k ð1Þ where, k is the neutron wavelength, h is the Bragg angle. The repeat distance (d) of the lamellar phase for the model membrane is calculated by: d ¼ 2ph q h ð2Þ Here, h is the diffraction order. During the neutron diffraction measurements, the sample was rotated in steps of 0.
Acknowledgements This work has received support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Grant Agreement 665593  awarded to the  Science and Technology Facilities Council. We would like to acknowledge the award of the neutron beam time on D16 at  Institute Laue-Langevin , and the technical and scientific support. [77] ILL raw data doi: 10. 5291/ILL-DATA. 9-13-875. Thanks for the studentship support from the  University of Manchester and China Scholarship Council  ( UoM-CSC joint PhD programme ) to M. L. We also thank  Dr. Shengjiang Yang  ( South China University of Technology ) for his precious suggestion about simulation data analysis, and  Dr. Gavin Stenning  ( ISIS ) for the help with membrane characterization. An allocation time from the  Shared Computation Facility (SCF) at the University of Manchester  is gratefully acknowledged.
Immunodetection was performed using Clarity TM Western ECL Substrate (Bio-Rad), and This journal is © The Royal Society of Chemistry 20xx Please do not adjust margins Please do not adjust margins images were acquired with a ChemiDoc TM XRS + Imager (Bio-Rad). The relative signal intensity of the bands obtained was quantified using the Image Lab software (Bio-Rad). Data were statistically analyzed as described below. In this experiment, three independent biological replicates were performed.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No 764920  MSCA-EJD PhotoBioCat). C. C. Pacheco ,  P. Oliveira  and  S. B. Pereira  acknowledge  FCT -Fundação para a Ciência e a Tecnologia, I. P. /Ministério da Ciência, Tecnologia e Ensino Superior  for the Assistant Researcher contract  CEECIND/00259/2017  (C. C. P. ), the  FCT investigator grant   IF/00256/2015  (P. O. ), and Junior Research contract  DL57/2016  (S. B. P).
These variations are likely due to the steric hindrance of the phenylacetylene group. In contrast, it improved the antiproliferative activity of the remaining 6 derivatives (14a, 14b, 15a, 20a, 20b, 25). Since each molecule was tested against two different cell lines, we explored whether the antiproliferative activity of our derivatives was significantly different between them. Data visualization of all MTT assay results that were carried out (Figure  4 ) did not reveal obvious differences in IC 50 distribution between cell lines. Statistical analysis led us to the conclusion that antiproliferative activities did not significantly differ between SK-N-SH and IMR-32, with p-value = 0. 09. This result suggests that our compounds are probably not substrates of the efflux pump P-gp responsible for drug resistance.
Molecular Docking To predict the molecular mechanism involved in the antiproliferative activity of these molecules, molecular docking of best compounds 11a and 11b from the TDAE series, and best compounds 14a and 25 from the Sonogashira series, was performed on both crystallographic structures of human Topoisomerase IIβ (3QX3) and human Tissue Transglutaminase (4PYG) obtained from the Protein Data Bank. We selected these drug representatives because they have the lowest IC 50 of the TDAE and Sonogashira series, respectively. Molecular docking was also performed on 3QX3 for compound XK-469, and on 4PYG for compound GK13 as references on each protein, respectively. Since no crystallized structure of topoisomerase complexed with XK-469 is available, the 3QX3 entry was chosen for Topoisomerase IIβ because of its complexation with a well-known topoisomerase II inhibitor, etoposide  [31].
Similarly, since no crystallized structure of human tissue transglutaminase combined with GK13 was found, the 4PYG entry was chosen for this protein because of its complexation with guanosine triphosphate (GTP) since this enzyme notably possesses a GTPase enzymatic  [32]. To perform molecular docking, affinities between compounds and protein targets were calculated by a "blind-docking approach" without "a priori" binding site information, for all our docking simulations  [33, 34]. We evaluated the binding modes of these compounds to each protein and calculated the binding affinities using the open-source program AutoDock Vina  [35, 36]  which uses a scoring function relying on the Broyden-Fletcher-Goldfarb-Shanno algorithm for the local optimization. The quality of protein-ligand interactions was visually examined from the resulting conformation binding mode of the lowest level of Gibbs free energy of binding (∆G).
In addition, simulations of logD at a physiological pH of 7. 4 returned identical values to logP, as presented in Table  3. These parameters were calculated with Simulations Plus software ADMET Predictor ®. During the absorption phase, this modeling revealed that compounds from the TDAE series result in a predicted bioavailability (F) ranging from 82. 71% for compounds 13 to 32. 69% for compounds 4. The hit compounds 11 returned a bioavailability value of 35. 48% from this simulation. For aromatic substituted epoxide derivatives from the Sonogashira series, F values were ranging from 12. 56% for compounds 14 to 1. 535 for compounds 21. In this series, only the non-aromatic substituted epoxide derivatives displayed high bioavailability with 90. 05% for compounds 24 and 89. 24% for compound 25.
The system was piloted by OpenLAB CDS ChemStation Edition C. 01. 07 computer software. The chromatographic separation was achieved using a Zorbax Eclipse Plus C18 column 50 × 2. 1 mm, 1. 8 µm Agilent (Santa Clara, CA, United States) protected by a Zorbax Eclipse Plus C18 (5 × 2. 1 mm, 1. 8 µm) guard column. Water acidified with 0. 1% of formic acid (v/v) as Solvent A and Acetonitrile acidified with 0. 1% of formic acid (v/v) as Solvent B was used for the gradient elution at 0. 5 mL. min -1. The gradient program was: 5% B (from 0 to 2. 0 min), 5% to 100% B (from 2. 0 min to 10. 0 min), 100% B (from 10. 0 min to 13. 0 min with post time of 2. 0 min. UV detection wavelength set at 254 nm and injection volume of 1. 0 µL.
After 72h of drug treatment, the medium of each well was replaced by 150 µL of fresh medium containing MTT at 0. 5 mg/mL, and cells were incubated at 37 • C for 4h. Then, the MTT solution was removed and 150 µL/well of DMSO was used to dilute the formazan crystals formed by the mitochondrial reductase of surviving cells. Finally, absorbance was measured at both 550 and 600 nm with a POLARstar Omega BMG LABTECH (Champigny s/Marne, France) plate reader. At least three independent experiments (in quadruplicate) were performed, and data were expressed as mean ± SD.
Data Analysis The analysis of the data obtained with the POLARstar Omega BMG LABTECH plate reader was carried out with RStudio  [30]  using packages from the tidyverse  [38]  for data visualization  [39] [40] [41]. The unit of raw data obtained by plate reading is absorbance (UA), which was converted to the percentage of living cells by normalizing each absorbance well by the mean absorbance of control wells (Equation (1)). Living cells i (%) = x i (UA) x control (UA) × 100 (1) Outliers were identified by univariate analysis estimating location and scale. Thus, two z-scores were calculated to estimate data dispersion from mean (Equation (  2 )) and median (Equation (  3 )) allowing us to remove outliers with a 1. 96 cut-off for 95% data accuracy  [42]. z i = x i -x s (2) w i = x i -median(x i ) MAD (3) IC 50 was determined a by 4-parameter logistic regression fitting model using R package 'drm'  [43] , as shown in equation (Equation (  4 ))  [44] , where Y is the response, a is the lower asymptote, d is the upper asymptote, X is the concentration, c is the EC50, b is the slope factor of the curve.
Data Analysis Statistical analyses were performed using R programming. p-values were calculated to quantify statistical significance, with the criterion set at p < 0. 05. The IC 50 obtained for each experiment was statistically analyzed first by the nonparametric Wilcoxon Mann-Whitney test to compare the IC 50 distribution between the cell lines, between cis isomers and trans isomers, and between TDAE and Sonogashira series. Secondly, we performed a nonparametric Kruskal-Wallis one-way analysis of variance and finally a nonparametric post-hoc Dunn's test for pairwise comparison to assess whether the IC 50 of each isomer from each series differs significantly with varying epoxide substituents. All these tests were performed in RStudio using the native "R Stat Package" and "rstatix" package  [45]  for Dunn's test.
Molecular Docking Crystal structures of human Topoisomerase IIβ in complex with DNA (PDB code: 3QX3)  [31]  and human Tissue Transglutaminase (PDB code: 4PYG)  [32]  were used as targets for docking simulations. The formula of the docked compounds was designed, and energy was minimized using the program MarvinSketch ChemAxon Ltd. (Basel, Switzerland) to evaluate possible binding modes. Proteins and ligands were prepared using ADFR and Python scripts provided with AutoDock-Vina. Hydrogens were not added to the proteins, but Gasteiger charges were added as the default set-up. We considered our target proteins as rigid objects while all ligands were handled as flexible. All simulations were performed with the AutoDock-Vina program  [35, 36]  on a macOS terminal using the Vina forcefield. Center inputs for setting up search space were determined with ChimeraX  [46, 47]  measuring the center of mass of each protein.
The input sizes for the definition of search space were set up to a 27,000 Ångström 3 cube, and exhaustiveness to 64. The other parameters were adopted as the program's default values. Analysis of the results was performed by ranking the different ligand poses accordingly to their binding energy. We considered the molecule adopting the lowest energetic conformation as a promising compound. Visual analysis of the lowest energy solutions for each compound allowed us to identify the protein binding site. All the figures were drawn using the program ChimeraX.
Pharmacokinetics Modeling with Simulation Plus Software Suite The drug database for pharmacokinetic modeling was set up with MedChem Stu-dio™ 4. 0 from the Simulation-Plus software suite. Drug likeliness parameters were determined with ADMET Predictor ® 10. 3. Pharmacokinetic parameters were determined with GastroPlus ® 9. 8. 2. From GastroPlus ® , a compartmental model was repeated for each drug as an administration to a 70 kg fasted human with normal gut physiology in a 100 mg immediate-release tablet dosage form.
Acknowledgments: We express our thanks to  Vincent Remusat  for 1 H-NMR and 13 C-NMR spectra recording,  Raphael Bergès  for his advice on cell culture,  Manuela Carenzi , and  Luis Montero  for their advice on R programming and statistics, and  Fathi Mabrouki  and  Elnur Garayev  for UHPLC acquisitions.
com). The sequence is available from GenBank (Accession no. : OK148444). The plastid genome of D. montanum is a circular molecule of 154,185 bp in length, comprising a large single-copy (LSC) region and a small single-copy region (SSC) of 84,746 and 16,320 bp, respectively, separated by two inverted repeat regions (IR) of 26,559 bp. We annotated 113 distinct genes, including 77 protein-coding genes, 4 ribosomal RNA genes (all located in the IRs) and 32 distinct tRNA genes. The genome contained 92 unique genes, 18 genes duplicated in the IRs, two (trnE-UUC, trnM-CAU) triplicated genes in the LSC and one partially duplicated gene split between LSC and IRs (rps12). Among annotated genes, 8 contained one intron  (atpF, ndhA, ndhB, petB, petD, rpl2, rpl16, rpoC1 ) and 3 contained two introns (clpP1, pafI, rps12).
Funding This study was funded by the  interreg-POCTEFA program  [ FLORALAB  Project  EFA294/19 ] and is set within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  [ ANR-10-LBX-41 ] as well as within the framework "  Ecole Universitaire de Recherche (EUR) "  TULIP-GS  [ ANR-18-EURE-0019 ].
Data availability statement The consensus genome sequence is openly available in GenBank of NCBI at [https://www. ncbi. nlm. nih. gov] (https://www. ncbi. nlm. nih. gov/) under the accession no. OK148444. The genomic data that support the findings of this study are available from the European Nucleotide Archive at [https://www. ebi. ac. uk/ena/] (https://www. ebi. ac. uk/ena/) The associated numbers are Study: PRJEB46773, Sample: ERS7180281, and Runs: ERR6447361 (Illumina) and ERR6447534/ERR6447760 (ONT), respectively.
Introduction Since dysfunctions of protein kinases are involved in the pathogenesis of several diseases, including solid or hematologic cancers and cardiovascular, autoimmune, and inflammatory diseases, protein kinase inhibitors (PKI) have triggered a large number of research programs within academia and pharmaceutical companies worldwide, leading to a regular approval of these drugs by the regulatory authorities. A list of PKIs currently in clinical trials is curated in a freely accessible database (http://www. icoa. fr/pkidb, accessed on 29 June 2022)  [1]. Nowadays, most of the marketed PKIs are used for the treatment of various solid or hematologic cancers or directed toward inflammatory diseases. These drugs are essentially administered via the oral route, and are victims of presystemic pharmacokinetic drug-drug interactions (DDI), since most of them are substrates of intestinal and/or hepatic cytochrome P-450 (CYP) enzymes (mostly CYP3A4) and membrane transporters  [2] [3] [4] [5].
The aim of the current study was to apply such methodology to oral targeted anticancer drugs that are narrow therapeutic drugs with potentially complex DDI, to better understand the respective role of metabolism and transporters in their DDI. Data characterizing the absorption time (k a and t max ) from a panel of 54 drugs were retrieved from DDI studies (n = 101) in published papers and/or from FDA publicly accessible files. Indeed, given that most inhibitors and inducers used in clinical DDI studies are not specific to CYP450 and transporters (or can simultaneously act on both these systems), the conclusions of some of these studies can be challenged.
Data Curation Given than MAT (or k a ) was usually not available, t max as well as terminal half-life (t 1/2 ) were retrieved when available either from data in tables or after noncompartmental pharmacokinetic analysis from digitization concentration-time profiles. AUC (area under the curve) ratios between DDIs and control periods were calculated from AUC zero-toinfinity for single-dose studies, and from AUC within the dosing interval when studies were performed at steady state. All parameters were reported as ratios from the DDI phase to control phase. Renal and feces elimination (%, total drug and metabolite(s)), plasma protein binding (%), and blood-to-plasma ratio were also retrieved. The data allowing for the estimation of the MAT within DDI studies were obtained either from the published literature extracted from Pubmed and/or from freely accessible Food and Drug Administration (FDA) files (section: Clinical Pharmacology and Biopharmaceutics review from multidisciplinary review and from Labeling file) up to 29 June 2021.
Physicochemical and Biopharmaceutical Properties The main physicochemical properties were estimated from ADMETlab 2. 0 (https:// admetmesh. scbdd. com/; accessed on 1 September 2021). The parameters are Log D (pH 7. 4), Log P, topological polar surface area (TPSA in %), Log S (mol/L), and hydrogen bonding (hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA)). The percentage of polar surface area was calculated from TPSA and from polar surface area (PSA in Å 2 ) given by Dragon 6 software (Talete, Milano, Italy)  [10]. pH-dependence solubility, solubility at neutral pH, and Biopharmaceutics Classification System (BCS) rating were obtained from FDA-submitted files, and classified according to the United States Pharmacopeia (USP) (i. e. , very soluble, freely soluble, soluble, sparingly soluble, slightly soluble, very slightly soluble, and practically insoluble).