dataesr/scientific-paragraphs-categorization huggingface dataset:
As expected, the membrane thicknesses obtained by SAXS for samples in D 2 O and H 2 O buffer do not differ significantly (especially for low x glycyrrhizin ) and d m,MKP decreases slightly with increasing x glycyrrhizin. Values determined by SAXS are ≈3-4 Å higher compared to the ones obtained from SANS. This seems reasonable because different membrane contrasts do not lead to exactly the same membrane thickness values. Pencer et al. [57]  also determined the membrane thickness of DOPG by the MKP method from SANS data and reported a value of 31. 29 ± 0. 05 Å. This value is similar to the one obtained in our study with a value of 30. 5 ± 0. 3 Å. Moreover, in the study of Pencer et al. , it is stated that the thickness obtained from the MKP method corresponds to the thickness of the hydrocarbon region  [57]  and therefore appears smaller than the total membrane thickness.
Model-Dependent Fitting of SAS Data In this section, the scattering curves obtained from SANS and SAXS are evaluated by model-dependent fitting to derive the SUV size, membrane thickness and membrane contrast parameters. To describe a SUV shape with a head-to-tail contrast over the lipid membrane, the core multishell (CMS) model with three shells implemented in the program SASView  [59]  was used. This model was already successfully used to model the SUV shape in mixtures of the phospholipid DMPC and the saponin aescin  [58, 70, 71]. Parameters obtainable from this model are the core radius of the SUV R c , the corresponding polydispersity σ R c and the thicknesses of the lipids head d head and tail part d tail. The latter parameter can also be accompanied by a polydispersity σ d tail. The total membrane thickness amounts to d M,CMS = 2•d head + d tail.
Both values (R c and σ R c ) were afterwards fixed for the approximation of the SAXS data. Values obtained for R c and σ R c are listed in Table  2. Thereby, a decrease in R c from 207 Å to 156 Å with increasing x glycyrrhizin was observed, while σ R c increases from 37 to 64 %. Here, we want to mention that the high value of σ R c can have a significant influence on R c. Especially for samples with x glycyrrhizin of 0 and 1 mol%, the CMS fits follow the SAXS data nicely at low q, which indicates that determination of R c and σ R c from SANS data yields reliable results (compare Figure  4 ). = 32 %). In this case, a minimum in the SAXS curves quite clearly defines both parameters although a scattering plateau is not reached. With increasing x glycyrrhizin and also increasing polydispersity σ R c the size decrease for samples in H 2 O buffer is more pronounced, which might be due to the usage of the H 2 O.
SANS SAXS XSLD tail R c R c , σ R c R c , σ d tail d tail , σ The membrane contrast is sharper in the SAXS data and therefore the membrane SLD parameters were derived from this data after R c and σ R c were fixed based on the results from SANS. For fitting the membrane part in the SANS data, equivalent NSLD values were calculated from the results for the XSLD values. Thereby, the molecular formula of a mixture of the DOPG tail part and the respective glycyrrhizin amount was taken into account. All NSLD and XSLD values for the hydrophilic and hydrophobic membrane parts are shown in Table  S4. As SLD and membrane size parameters are directly correlated, the thickness of the hydrophobic membrane part d tail and therewith the complete membrane thickness d M,CMS is determined during the SLD optimization process.
Values for d tail obtained from SANS and SAXS data for samples prepared in D 2 O/H 2 O buffer are listed in Table  S5  and the corresponding values for d M,CMS are plotted in Figure  6. For SANS, this parameter yielded only reasonable results after having fixed the NSLD values on the basis of the SAXS results. In the case of SAXS, a polydispersity of d tail (σ d tail ) has to be considered to successfully represent the scattering data and especially the vanishing minimum at q ≈ 0. 04 Å -1. These values are additionally listed in Table  S5. As expected also from comparison with the MKP results, the thickness values derived from SAXS data for samples in D 2 O and H 2 O buffer are similar. In comparison to SAXS, the membrane thickness derived from SANS is about 4-5 Å lower. Pan et al. [53]  determined a size of 27.
5 Å for the hydrophobic part of the DOPG membrane. This value was derived from simultaneous fitting of SANS and SAXS data and lies between the values determined by us with both methods for pure DOPG vesicles (d tail,SANS,D 2 O,DOPG = 26. 3 ± 0. 1 Å and d tail,SAXS,D 2 O,DOPG = 31. 3 ± 0. 1 Å). Simultaneous determination of d tail was not performed in this work, because the results for SANS and SAXS from model-independent fitting showed a significant offset and having 'fixed' d tail from the more sensitive method SAXS leads to non-reliable results for the correlated NSLD values. The reason for this offset most probably is the different contrast seen by the different kinds of radiation used. At present SASView does not allow to account for this. However, we believe that simultaneous fitting of SAXS and SANS data will not lead to better results which would justify additional programming effort.
Conclusions and Outlook The influence of adding glycyrrhizin to long-time stable vesicles composed of the negatively charged phospholipid DOPG was investigated. Samples were prepared in a D 2 O-/H 2 O-based buffer solution with a pH value of 7. 4, so that the lipid as well as the saponin should be deprotonated at their acidic functions. Stable SUVs were formed even at a glycyrrhizin content of up to 50 mol%. This was shown by cryo-TEM and small-angle scattering methods. The complementary usage of SANS and SAXS for samples prepared in D 2 O buffer showed the necessity of using both methods in the present case to derive profound structural parameters. Whereas from SANS data alone the influence of glycyrrhizin addition was only hardly resolvable on the membrane scale, SAXS data clearly indicated an incorporation of glycyrrhizin into the hydrophobic membrane part.
Acknowledgments: The authors thank the  ILL  for granting the beamtime at the D22 beamline that was available as part of proposal 9-11-1824. Moreover, the authors thank  Marco Adamo  for his support during the beamtime. This work benefited from the use of the SasView application, originally developed under  NSF  award  DMR-0520547. SasView contains code developed with funding from the  European Union 's  Horizon 2020 research and innovation program  under the  SINE2020  project, grant agreement No  654000.
Background Sipuleucel-T, marketed as Provenge®, is an autologous cellular immunological agent  1  indicated in the treatment of metastatic castration-resistant prostate cancer (mCRPC). 2  Cardiovascular (CV) toxicities with sipuleucel-T are uncommon (0. 3%). 3  We report the first case of sipuleucel-T-induced inflammatory cardiomyopathy and describe the potential mechanisms. Through review of an international pharmacovigilance database, VigiBase, we also identified increased reporting of sipuleucel-T-associated CV toxicities.
Methods We searched for adverse drug reactions (ADRs) reported in VigiBase (NCT03530215), the World Health Organization database containing individual case safety reports from more than 130 countries since 1967. 5 VigiBase is managed by the Uppsala Monitoring Centre (Uppsala, Sweden). The use of confidential, electronically processed patient data was approved by the French National Commission for Data Protection and Liberties (Commission Nationale de l'Informatique et des Libertés; reference number 1922081). Using the MedDRA term 'cardiac disorders' (System Organ Class level), we searched for CV adverse events associated with sipuleucel-T versus all other drugs in the database. Disproportionality analysis was calculated by the information component (IC), a Bayesian disproportionality indicator, which has been previously described.
mL -1 ) for matrix interference. The mean IC50 value obtained for CTX1B was 0. 26 ± 0. 07 ng mL -1 (n = 3) with a mean CV of 2. 8%. In the Serranidae family, most of the specimens of S. powelli (n = 14/15) tested negative, except for one specimen showing composite binding affinity, such as one specimen of E. tuamotuensis (Table  2 ). Unknown = no information about the fishing site location. b Each value represents the mean ± SD of three aliquots per sample tested once. c ND = no affinity detected. d CTX-like content was calculated according to Equation (3) (Section 3. 4. 1). e LOD: the limit of detection for fish samples was estimated at 0. 07 ± 0. 02 µ g CTX1B eq. kg -1 of fish flesh. (f) CTX1B. Data represent the mean ± SD of three subsamples (5 g each) per specimen tested each in one experiment (n = 3) and CTX1B tested in three independent experiments (n = 3), each concentration run in duplicates.
Overall, the nontoxic (one specimen and one pooled sample) and toxic (three specimens) statuses were confirmed by LC-MS/MS for 6/10 samples showing a 60% agreement between the three biological and the chemical methods. Of note, such discrepancies were also observed between CBA-N2a and LC-MS/MS data in fish from the Selvagens Islands, suggesting the presence of metabolic products, which need to be further elucidated, as well  [69]. In this region, C-CTX1 was the most frequently encountered Caribbean CTX analog, although the toxin profile of the zebra bream Diplodus cervinus was free of it  [69]. Similarly, our results show that CTX1B was not the main contributor to the toxicity of the E. illustris specimen from the Gambier Islands. Our results also suggest that the other deep-water fish species analyzed in this study may contain metabolized Pacific CTX analogs that differ from the ones typically metabolized by lagoon coral reef fish.
e. , #7 to #16, #20), each concentration tested in duplicate. The dose-response curves were established by plotting net count per minute (cpm) values vs. pure toxins or fish concentrations tested based on the four parameters logistic regression model (4PL) using GraphPad Prism software version 9. 0. 2. (GraphPad, San Diego, CA, USA). From these dose-response curves, the effective concentration, which causes 50% inhibition (IC 50 ), was determined. For the pure CTX1B from the ILM, the mean IC 50 value was 0. 27 ± 0. 01 ng mL -1 (n = 3). The limit of detection (LOD) and quantification (LOQ) for fish samples were estimated to 0. 07 ± 0. 02 and 0. 13 ± 0. 01 µg CTX1B eq. kg -1 of flesh, respectively, according to the following formula: LOD = (IC 80 of CTX1B/MCE) (1) LOQ = (IC 50 of CTX1B/MCE) (2) The toxin content of fish extracts was estimated using the following equation and expressed in µg CTX1B eq.
The C18 cartridges were then washed with 3 mL MeOH/H 2 O 75/25, and then CTXs were eluted with 2 × 3 mL of MeOH/H 2 O 90/10. The two eluting fractions (2 × 3 mL) were combined and evaporated under N 2 at 40 • C. The purified extract was reconstituted with 500 µL of MeOH prior to LC-MS/MS analysis at a concentration ranging from 80 to 160 g flesh eq. mL -1. LC-MS/MS analyses were performed using a UHPLC system (UFLC Nexera, SHIMADZU, Japan) coupled to a hybrid triple quadrupole-linear ion-trap API4000 QTRAP mass spectrometer (SCIEX, CA, USA) equipped with a TurboV ® electrospray ionization source (ESI). The instrument control, data processing, and analysis were conducted using Analyst software 1. 6. 3 (Sciex, CA, USA). The quantitative targeted analysis of CTXs was investigated according to the method previously described  [103].
Statistical Analyses No statistical comparison could be made with MBA or LC-MS/MS data, as only one value per specimen was obtained. Conversely, the repeatability of CTX contents obtained with rRBA (n = 3) and CBA-N2a (n = 4) could be evaluated based on the coefficient of variation (CV) calculated following the formula ((SD/mean) × 100%). To determine whether rRBA values (n = 3) differed significantly with CBA-N2a values (n = 4), the Student's t-test (multiple unpaired) was performed using GraphPad Prism software version 9. 0. 2 (GraphPad, San Diego, CA, USA).
In the specific case of the pentafluorosulfanyl moiety, physicochemical data are scarce  [2] [3] [4] [5] [6]  and experimental measurements of the acidity (pK a )  70  and lipophilicity (log P)  71, 72  imparted by the pentafluorosulfanyl moiety would be useful data for medicinal chemistry programs. We thus turned our attention to a subset of indoles substituted at C-2 by H (20), F (22), CF 3 (23), or SF 5 (5a/8), and the results are summarized in Figure  1B. Values of pK a were measured in acetonitrile by spectrophotometric titration. 55 he pK a of indole 20 (32. 57)  70  decreases dramatically by 5. 4 units upon fluorine and fluorine-containing substitution at C2, resulting in pK a 27. 20 for 22. Swapping C2-F for a C2-CF 3 substituent only slightly impacted the pK a by 0. 44 units (pK a of 23: 26.
In silico assessment is typically done in the first place to estimate the mutagenic potential of a compound against databases. Indole is considered to be nonmutagenic, and therefore, there is no mutagenicity concern emerging from the indole moiety. However, uncovered fragment -SF 5 was detected in the used systems (Derek, Sarah Nexus, and Case Ultra). 55 Hence, due to incomplete coverage, it was recommended to perform further tests to evaluate potential mutagenic activity of the SF 5 moiety. We thus performed the Ames test which is a classical biological test to determine the mutagenic potential of a chemical compound. 55 Since cancers are often linked to damage to DNA, this rapid, reliable, and inexpensive test is used to estimate the carcinogenic and genetic activity at the nucleotide level, based on different histidine-requiring bacterial strains of Salmonella typhimurium carrying mutations in the genes in the absence and presence of a liver-metabolizing system.
81, 82 Over the years, a large database has been accumulated with this assay, confirming its ability to detect genetically active compounds of most chemical classes with around 80-90% sensitivity and specificity. The 2-SF 5 -indoles 5a and 8 have been tested, and no mutagenic evidence was observed over the different bacterial strains tested, which means that they can potentially be used for further development in drug discovery. 55.
org/ 0000-0002-1732-9641Complete  contact information is available at: https://pubs. acs. org/10. 1021/acsorginorgau. 1c00010 Notes The authors declare no competing financial interest. ■ ACKNOWLEDGMENTS This work was supported by the  French National Research Agency  (Grant No. ANR-PRC-17-CE07-0008 ),  Université de Haute-Alsace ,  Université de Strasbourg ,  CNRS ,  Région Grand-Est, and Foundation for Frontier Research in Chemistry (ICFRC). The authors thank  Pascale Hoehn  (safety lab, DSC) and  Susanne Glowienke  and  Erika Udovic  (preclinical safety, Ames test, in silico) from  Novartis. Work at Tartu was supported by the  Estonian Research Council  grant ( PRG690 ), by  EU  through the  European Regional Development Fund  under project  TK141  " Advanced materials and high-technology devices for energy recuperation systems " ( 2014-2020.
Materials and Methods All information about AR protein mutations that lead to AIS and their crystal structures were retrieved with the UniProt code P10275 (www. uniprot. org accessed on 16 January 2020). Crystal structures of AR (PDBID: 1R4I, 2AM9, 2PIQ, 5JJM, 4OEZ) in a high resolution were chosen from the RCSB protein database and represented the variety of conformations of the LBD  [18]. Co-crystalized protein-ligand and protein-protein complexes (PDBID: 2PIQ, 5JJM, respectively) were used as the starting point for simulation of monomer and dimer. Both monomer and dimer structures were solvated with water model transferable intermolecular potential 3P (TIP3P) and a solute-box distance of 1. 2 nm. All steps of MD simulations and analysis were performed using the Gromacs (2018. 1, available at https://manual.
gromacs. org/documentation/2018. 1/index. html accessed on 03 February 2020) software tool and the CHARMM36 force field  [19, 20]. A constant system temperature during MD simulation experiments was maintained with a Nosé-Hoover thermostat. The simulations were performed at 310 K and 1 bar pressure, achieved with Parrinello-Rahman barostat. A cut-off radius of 14 Å was applied for both the Coulomb (electrostatic) and the Lennard-Jones (VdW) interactions using the particle-mesh Ewald Life 2021, 11, 659 3 of 12 (PME) method for calculation in the Verlet cut-off scheme  [21]. DHT molecule was extracted from the crystal structure (PDBID: 2PIQ) and processed with a Swissparam. ch server to generate a topology compatible with the Gromacs environment  [22]. To simulate a phosphorylated state of serine, we selected a topology of dianionic phosphoserine, which was substituted with PyMol 1.
5 software (The PyMOL Molecular Graphics System, pymol. org accessed on 15 February 2020)  [23]. We set the length of MD for monomers to 100 ns, and later extended it to 200 ns to confirm the accuracy of the obtained results. The dimer protein structures were simulated for a longer time, up to 500 ns, without extending the simulation. Radius of gyration (Rg), root mean square fluctuation (RMSF) and deviation (RMSD) analyses were carried out using Gromacs built-in tools. Based on the expected distribution of RMSD values in the first half of MD simulation, we analyzed a time-lapse corresponding to the second half, when the protein is already equilibrated and relaxed. Conformational consistency of the protein molecule was assessed with a built-in clustering module, which detected every leap of RMSD values, having a cut-off distance of 1Å for the two structures to be equivalent, and derived the conformational cluster's number, distribution, and size of each cluster.
As opposed to the standard surface generating software tools, which depict an external part of the protein, we applied an alternative analysis pack CaverAnalyst 2. 0 (available at https://www. caver. cz/ accessed on 28 April 2020) for the cavity volatility assessment and explanation of distal mutation effect on the AR inactivation process  [28]. The length of the tunnels and the radius were calculated for every snapshot of a trajectory, allowing the cavities inside the protein to be probed throughout the trajectory file. From these, a set of residues (Pro683, Leu708, Gln712, Arg753, and Phe765) were selected as a starting point for the tunnel search and volume calculations. To increase the accuracy of the search, we set approximation level to 15, minimum probe radius to 0. 8, shell radius to 3 Å, and shell depth to 4 Å.
Computational studies All molecular modelling was performed using the Schr€ odinger 2018 software suite (Schr€ odinger Release 2017-3, Schr€ odinger, LLC, New York, NY, 2017). The model of FAAH was constructed based on electron diffraction data (X-ray, PDB: 3PR0, 2. 2 Å), whereas the model of TRPV1 was constructed from a cryo-EM structure (PDB: 5IRZ, 3. 28 Å)  [29, 30]. Protein structures for docking simulations were prepared using Maestro's Protein Preparation Wizard. The corresponding N-arachidonoyl conjugates of 4-AP (i. e. AM404), and of the paracetamol analogues 5-amino-2-methoxyphenol (compound 2) and 5-aminoindazole (compound 3) were manually placed in the capsaicin-binding pocket of TRPV1, and FAAH models of bound N-arachidonoyl conjugates were constructed by superimposing each conjugate with anandamide covalently bound to FAAH.
The diencephalon was homogenized in 1 ml ice-cold 10 mM Tris buffer, to which had been added 1 mM EDTA, 300 mM ascorbic acid, 10 mM MAFP and 100 mM ibuprofen, the latter to prevent in vitro prostanoid formation and degradation. In order to determine prostanoid levels, aliquots (200 ml) of blood and homogenates of the diencephalon were precipitated with 1 ml icecold acetone, containing 300 mM ascorbic acid and 10 nM PGE 1 -d4 (Cayman Chemicals, Co. , Ann Arbor, USA) as internal standard, and analyzed by the LC-MS/MS technique (see below). For details on telemetric temperature recordings, see Refs. [36, 37]. In short, under anesthesia (1% isoflurane, Abbot Scandinavia, Solna, Sweden), a temperature-recording probe was implanted intraperitoneally (TA11TAF10; Data Sciences International, St. Paul, MN, USA) and postoperative analgesia was given with buprenorphine (Temgesic, 100 mg kg À1 in 0.
Calculations and statistical analysis GraphPad Prism 8 (GraphPad Software, La Jolla, CA) was used for statistical analysis and to perform curve fitting (non-linear regressions) and calculations of pEC 50 values (the negative log concentration that elicited half-maximal vasorelaxation). The level of statistical significance was set at P < 0. 05. Comparisons between two groups of data were performed using Student's t-test. Comparisons of more than two groups of data were performed using 1way ANOVA or repeated measures ANOVA followed by either Dunnett's, Tukey's or Sidak's multiple comparisons test, Welch's ANOVA test or 2-way ANOVA followed by Sidak's multiple comparisons test. Data are presented as the mean ± SEM; n indicates the number of animals examined.
In silico studies of the FAAH-mediated production of Narachidonoyl conjugates and their interactions with TRPV1 To understand the interaction of the N-arachidonoyl conjugates with both target proteins, FAAH and TRPV1, the corresponding Narachidonoyl conjugates of 4-AP, 2 and 3 were chosen as key compounds from the in vitro studies for modelling with FAAH (PDB: 3PR0, 2. 2 Å, X-ray) and TRPV1 (PDB: 5IRZ, 3. 28 Å, cryo-EM)  [29, 30]. The two proteins were prepared using the Protein Preparation Wizard Script within Maestro (Schr€ odinger Inc. ) before manual introduction of the N-arachidonoyl conjugates of 4-AP, 2 and 3. The interaction of AM404 with FAAH was first studied (Fig. 3A ). Upon FAAH-mediated conjugation of 4-AP, the tetrahedral intermediate of AM404 is covalently attached to Ser241 via its carbonyl carbon  [41] , whereas the amide hydrogen and nitrogen of AM404 are interacting with backbone Met191 and the hydroxyl group of Fig.
Acknowledgements This study was supported by  Medical Faculty of Lund University (Dnr. ALFSKANE-450751 ), AFA F€ ors€ akring (Dnr. 140376 ),  Hj€ arnfonden  ( FO2019-0316 ),  Stiftelsen Olle Engkvist Byggm€  astare (189e290), The  Swedish Research Council  (# 07879 ) and  The Royal Physiographic Society ,  Lund Sweden ,  Clermont Auvergne University, INSERM  and the  Agence Nationale de la Recherche  of the  French government through the program ''Investissements d'Avenir'' (I-Site CAP 20e25 ).
Aims: To develop a pharmacokinetic model of isoniazid (INH) concentration taking into account demographic factors and genetic variables [N-acetyltransferase 2 (NAT2) genotype], and to propose an initial INH dosage that could maximize the probability of achieving the desired INH concentration. Methods: A retrospective analysis was undertaken of INH concentration data collected from patients with tuberculosis in Tunisia. Results: In total, 118 patients were included in this study. The one-compartment model [volume of distribution (V), elimination rate (Ke)] was found to have good predictive performance. Multi-variate analysis showed that NAT2 affected both V and Ke significantly, but age, gender and weight did not. Internal validation of the final model showed correlation of 0. 95 between individual predicted INH concentration 3 h after drug intake (C3) and observed C3.
Patients and data collection A retrospective analysis was undertaken of INH concentration data collected from patients with tuberculosis in Tunisia. All patients diagnosed with pulmonary and/or extrapulmonary tuberculosis at the University Hospital of Monastir, Tunisia; who received a conventional four-drug tuberculosis regimen; and who underwent TDM of INH between January 2013 and December 2016 were included in this study. Tuberculosis treatment was based on a four-drug tuberculosis regimen (isoniazid, rifampicin, pyrazinamide and ethambutol), administered once daily at doses of 5,10, 25 and 15 mg/kg, respectively. Included patients were divided into two groups: the building group, used for development of the pharmacokinetic model; and the validation group, used for external validation of the final model.