dataesr/scientific-paragraphs-categorization huggingface dataset:
Mice Skin Surface Fluorescence Imaging Samples were observed with an upright Macrofluo fluorescence microscope, EL6000 source (Leica Microsystems, Rueil-Malmaison, France), equipped with a Cool Snap HQ Camera (Roper Scientific, Ottobrunn, Germany), and Metamorph (Molecular Devices, Sunnyvale, CA, USA) image acquisition software was used. Samples were imaged by fluorescence using appropriate filters: Leica L5 filter (excitation filter BP: 480/40 nm, dich. 505LP, emission filter BP: 527/30 nm) was used for FITC and LY, and Leica 49,008 ET mCH/TR filter (excitation filter BP: 560/40 nm, dich. 585LP, emission filter BP: 630/75 nm) was used for PI imaging (exposure time = 1 s). All filters were purchased from Leica Microsystems, Rueil-Malmaison, France. Images were processed for contrast and brightness, and analyzed with ImageJ (National Institute of Health, Bethesda, MD, USA).
Acknowledgments: We would like to thank  Zarel Valdez-Nava ,  Lionel Laudebat  and  Sorin Dinculescu  ( LAPLACE, Université de Toulouse, CNRS, INPT, UPS, France ) for their helpful discussions and the elaboration of the electroporation support. We also thank  Elisabeth Bellard  ( IPBS, Université de Toulouse, CNRS, UPS, France ) for her help and advice on fluorescence imagery.
However, the final experimental validation stage of the pipeline using fluorescence in situ hybridization (FISH) proved to be crucial in identifying an orientation error in the final O. pumila genome, pioneering an additional quality control check towards guaranteeing complete genomes. By sequentially incorporating four state-of-the-art technologies including 2nd generation seq (Illumina), 3rd generation (Pacbio) and physical mapping methods (Bionano optical maps, Hi-C libraries), Rai and colleagues achieved a chromosome-level assembly of the O. pumila genome (Fig. 2 ). This task was initiated by assembling de novo PacBio reads using Canu  [7]. The resulting Canu-assembly was subsequently used as a reference genome to guide the assembly of Bionano optical maps into a Bionano de novo assembly using the Bionano Solve software suite.
The Canuassembly consisting of 243 contigs was scaffolded using the Bionano de novo Optical assembly into a hybrid Canu-bionano assembly, consisting of 45 scaffolds (scaffold N50: 21 Mb) using Bionano Solve software. Hi-C proximity ligation reads were then mapped to the hybrid Canu-bionano assembly to further scaffold fragments. This sequential scaffolding method using Bionano optical maps, followed by Hi-C library scaffolding, significantly improved the continuity of the assembly from 45 to 13 scaffolds and reduced the number of gaps from 117 to 85. The continuity of the genome was further improved through gap filling and three rounds of polishing using PacBio reads, followed by error correction with Pilon using Illumina paired-end reads resulting in a final reference genome assembly of 21 gaps.
ACKNOWLEDGMENTS X. Shi ,  S. Mignani  and J-P. Majoral thank the  PRC NSFC-CNRS  2019 (21911530230 for X. S. and 199675 for S. M. and J-P. M). S. Mignani  and  X. Shi  acknowledge the support of  FCT-Fundação para a Ciência e a Tecnologia (Base Fund   UIDB/00674/2020  and  Programmatic Fund   UIDP/00674/2020 ,  Portuguese Government Funds)  and  ARDITI-Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação  through the project  M1420-01-0145-FEDER-000005-CQM+ (Madeira 14-20  Program). S. Mignani ,  A. Karpus  and J-P. Majoral acknowledge transnational EuroNanoMed III funded projects including a proper acknowledgement of ERANET EuroNanoMed III and the respective funding partner organizations. J. P. Majoral thanks  CNRS (France)  for financial support.
5 mm/s. External lubrification was applied to minimize confounding of the results due to friction. A spraying time of 500 ms and an atomizing pressure of 3 bar were used as settings for the external lubrication system implemented in the compaction simulator. Additional binder characterization (particle size distribution, dissolution kinetics, wettability, surface tension and viscosity) was previously performed and described by Vandevivere et al. [16]. The characterization data were used in the current study to examine the link between granule quality and binder attributes. To determine the energy plots of the different binders, the powder was individually weighed (400 mg, n = 6), manually filled into the die, and compacted using a STYL'one Evolution compaction simulator (Medelpharm, Beynost, France).
The main compaction pressure of 191 MPa was applied without a pre-compaction step at a punch speed of 13. 5 mm/s. External lubrification was applied to minimize confounding of the results due to friction. A spraying time of 500 ms and an atomizing pressure of 3 bar were used as settings for the external lubrication system implemented in the compaction simulator. Additional binder characterization (particle size distribution, dissolution kinetics, wettability, surface tension and viscosity) was previously performed and described by Vandevivere et al. [16]. The characterization data were used in the current study to examine the link between granule quality and binder attributes.
Multivariate Data Analysis PCA and partial least squares (PLS) were performed on various datasets. SIMCA ® 16 software (Sartorius Stedium Biotech, Umeå, Sweden) was used for multivariate data analysis. Prior to analysis, data were pretreated by scaling to unit variance-making the analysis independent of the units used and allowing the simultaneous analysis of quantities with different magnitudes-and by mean-centering. If needed, a logarithmical transformation was executed to non-normally distributed responses.
Granulation Experiments In order to study the effect of binder attributes on granule quality, it was of interest to include binders showing different intrinsic attributes. PCA (PCA_1) was executed on a dataset including 13 variables (intrinsic binder characteristics) and 11 observations (binders). These binders covered the different chemical natures of the most commonly used binders for a continuous twin-screw wet granulation process. Table  2  summarizes the binders and characterization techniques with corresponding abbreviations used for PCA. A PCA model containing two principal components (PC) explaining 51. 3% and 22. 6% of the variation, respectively, was fitted. The different binders' relation according to their attributes in the loading scatters plot was illustrated by the PCA score plot.
However, at t30, each binder (except for HPMC E15) showed good wetting properties of mannitol (<40 • ), with PVP K12 resulting in the most favorable wetting, as a very low CA was obtained with this binder (<30 • ). A second PCA (PCA_2) was developed using a different dataset in comparison to PCA_1. Descriptors for binder wettability on polytetrafluoroethylene (PTFE) surface were replaced by descriptors for binder wettability on mannitol tablets. Furthermore, the particle size of the binders was excluded in PCA_2 since this descriptor showed no relation with the binder effectiveness. Furthermore, only the binders selected based on PCA_1 were included. PCA_2 included 7 observations (binders) and 10 variables (binder characteristics). Table  2  summarizes the PCA variables with according abbreviations.
The inverse relationship between dissolution kinetics and viscosity is widely acknowledged in literature  [40, 41]. The surface tension strongly impacted the second PC. A second PCA (PCA_2) was developed using a different dataset in comparison to PCA_1. Descriptors for binder wettability on polytetrafluoroethylene (PTFE) surface were replaced by descriptors for binder wettability on mannitol tablets. Furthermore, the particle size of the binders was excluded in PCA_2 since this descriptor showed no relation with the binder effectiveness. Furthermore, only the binders selected based on PCA_1 were included. PCA_2 included 7 observations (binders) and 10 variables (binder characteristics). Table  2  summarizes the PCA variables with according abbreviations. The binder characterization data are reported in Table  4.
Acknowledgments e authors are grateful to Professor ( Dr. ) Nnanga Nga  for the antibacterial assays reagents, Professor ( Dr. ) Hortense Gonshu  ( Head of Laboratory for Bacteriology of Centre Hospitalier et Universitaire de Yaoundé ) for providing the bacterial strains, and  Dr. Tchokouaha Yamthe Laure Rachel  and  Dr. Tchamgoue Deutou Armelle  for providing technical advice.
Data collected For each file, the following parameters were collected: demographic characteristics of the patients (age and gender), poisoning context (route and circumstance of exposure) and relationship with drugs or addiction. In the drug user group (UD group), we also collected clinical data regarding the intoxication (symptoms, length of hospital stay). Severity was estimated using the Poisoning Severity Score (PSS)  [2]. Digestive burns were defined using Zargar classification  [3].
Statistics Data were analysed using IBM SPSS v20 software. Descriptive statistic analysis was performed for all variables: frequency, average and standard deviation. Association between categorical variables was studied using the Chi 2 test, and Fischer's exact test for small numbers. Quantitative variables were studied using Student's t-test. All of the percentages were calculated and shown in columns. All percentages shown are valid, i. e. do not account for missing data. Statistical significance was set at 0. 05.
Characterisation of NanoPHE: The hydrodynamic diameter (D H ) and polydispersity index (PdI) of NanoPHE were measured at 25 • C using a dynamic light scattering (DLS) instrument (NanoZS, Malvern Instruments, Orsay, France) piloted by Zetasizer Software (zetasizer software. ink, version 7. 10, Malvern panalytical, Worcester, UK). Each sample was diluted 1:10 in ultrapure water, then measured using a 633 nm laser with a detection angle of 173 •. NanoPHE zeta potential was measured at the same dilution and with the same equipment, with a detection angle of 13 •. All measurements were carried out in triplicate. PVA-GS NanoPHE-loaded film preparation: Six different film-forming solutions were prepared within a constant matrix: PVA, Glycerol and AMA (abbreviated as PVA-GS), with different concentration of NanoPHE (abbreviated as C0, C1, C2, C3, C4, and C5).
Samples were left to air-dry overnight in darkness. Films for each concentration were prepared in triplicate and identified as R1, R2 and R3. Acquisition: Raman images were acquired using an Alpha 300 R microspectrometer (WITec, Ulm, Germany). Spectral data were recorded using a 532 nm excitation laser source with a power of 20 mW at the sample. The instrument is calibrated daily using a two-step procedure. First, the TrueCal function of Project 5 (WITec, Ulm, Germany) was used; this is an automatic multipoint calibration routine performed with a Mercury-Argon (HgAr) light source integrated in the Raman microscope. Second, prior to data acquisition, verification was done using the peak at 520. 7 cm -1 from a silicon substrate. The laser was focused onto the sample through a 50× objective (Zeiss, LD Epiplan HD, N.
A. 0. 5). Backscattered light was detected by dispersing Raman-shifted radiation onto a Deep Depletion CCD detector (1024 × 128 pixels) using a 600 lines/mm grating centred at 2500 cm -1 , resulting in a spectral resolution of ≈4 cm -1. The acquisition time was set to 10 s, with two accumulations. 100 × 100 µm 2 Raman maps were recorded with an X and Y lateral step size of 4 µm (corresponding to 625 spectra). For each concentration and condition, three separate films were prepared (R1, R2, R3), and the measurements were performed once for each sample. Additionally, reference Raman spectra were recorded for all constituent compounds of the films. Data handling: Raman maps were pre-processed and analysed using Matlab ® (Mathworks, Portola Valley, CA, USA). Spectra were cut in the range 300 to 1800 cm -1 and then subjected to offset correction followed by vector normalisation.
61 ± 0. 79, 13. 6 ± 0. 15, 3. 42 ± 1. 81 and 1. 92 ± 0. 70 for C1, C2, C3, C4 and C5. Except for C2, the concentrations are predicted with relative errors below 5%. NCLS analysis of Raman microspectroscopic profiles can therefore be employed to perform quantitative analysis of the percentage of AI content within a composite solid film matrix with a high degree of accuracy. The methodology of mapping provides a qualitative analysis of the film in terms of, for example, the degree of homogeneity of the composition, and also provides relatively large datasets to construct and validate calibration models for absolute quantitation purposes.
Determination of percentage entrapment efficiency Percentage entrapment efficiency is percentage of drug entrapped in the microparticles regarding to original quantity used. Measured amount of microparticles MCP-1 to MCP-6 i. e. 10 mg were taken one by one, after crushing they were added to 100 ml of phosphate buffer pH 7. 2, by using an ultrasonic bath for complete removal of 6-Mercaptopurine monohydrate from microparticles respectively. Then solution was filtered with the help of 0. 45 um filter paper. 0. 5 ml of filtered solution was taken with pipette and diluted to 5 ml with phosphate buffer solution of pH 7. 2. After dilution, absorbance of 6-Mercaptopurine was measured at 316 nm by using a UV-VIS spectrophotometer. And that absorbance was then converted to the quantity of 6-Mercaptopurine monohydrate by applying data in standard calibration curve.
Drug Release Kinetic Analysis Drug release kinetic analysis regarding all formulations MCP-1 to MCP-6 was applied in excel by using add-in DDSolver software to find out the highest values of regression coefficient that tells us about best fit model either Zero order, First order, Highuchi, Hixon-Crowell, Korsmeyer-Peppas and also value of n was seen by applying Korsmeyer-Peppas model to check out the pattern of drug release. The ideal kinetic models for drug release and permeation were estimated using the different log time (Korsmeyer-Peppas model)  29. Following equations were used for the calculation of each kinetic model. Zero order equation: Qt = Q0 + K0t First order equation: 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 = -𝐾𝐾 Higuchi equation: Q = K3√ t Hixon -Crowell equation: Q0.
Drug Release Kinetics Mathematical models are an important tool to design pharmaceutical formulations, evaluate drug release processes in vitro and in vivo and, in general, come up with the optimal design for new systems  35. They allow the analysis of some important physical parameters (e. g. , drug diffusion coefficient) and resort to model fitting on experimental release data. The amount and type of active agent, polymer and adjuvants as well as the size and shape of the system designed to achieve a certain drug release profile can be predicted theoretically  36. Data collected by in-vitro release studies of all formulations was fitted in Zero order release model, first order release model, Higuchi model, Korsmeyer-Peppas model and Hixon -Crowell Model by using DDSolver software. The values of correlation coefficient were found for each kinetic equation is mentioned in Table  3.
Background: : Tranexamic acid (TXA) is an antifibrinolytic drug that reduces surgical blood loss and death due to bleeding after trauma and post-partum haemorrhage. Treatment success is dependant on early intervention and rapid systemic exposure to TXA. The requirement for intravenous (IV) administration can in some situations limit accessibility to TXA therapy. Here we employ physiologically based pharmacokinetic modelling (PBPK) to evaluate if adequate TXA exposure maybe achieved when given via different routes of administration. Methods: : A commercially available PBPK software (GastroPlus®) was used to model published TXA pharmacokinetics. IV, oral and intramuscular (IM) models were developed using healthy volunteer PK data from twelve different single dose regimens (n = 48 participants).
The model was verified using separate IV and oral validation datasets (n = 26 participants). Oral, IM and sub-cutaneous (SQ) dose finding simulations were performed. Results: : Across the different TXA regimens evaluated TXA plasma concentrations varied from 0. 1 to 94. 0 µg/mL. Estimates of the total plasma clearance of TXA ranged from 0. 091 to 0. 104 L/h/kg, oral bioavailability from 36 to 67% and Tmax from 2. 6 to 3. 2 and 0. 4 to 1. 0 h following oral and intramuscular administration respectively. Variability in the observed TXA PK could be captured through predictable demographic effects on clearance, combined with intestinal permeability and stomach transit time following oral administration and muscle blood flow and muscle/plasma partition coefficients following intra-muscular dosing.
, 2018). Although IV TXA is the treatment of choice, this is not an option for tens of thousands of women. Finding alternative to IV TXA administration in women with PPH is a WHO research priority  (Vogel et al. , 2018). To facilitate this, developing understanding on how alternative routes to IV TXA administration could impact the pharmacokinetics (PK) is needed. The objective of this study was to use published literature data to develop a physiologically-based PK model that predicts the plasma concentration-time (Cp-time) profile of TXA following IV, oral (PO) and intra-muscular (IM) dosing. After the TXA model was defined and validated, simulations were performed to predict if appropriate systemic concentrations of TXA could be achieved rapidly enough by either oral, IM or sub-cutaneous (SQ) administration.
Data extraction and handling PK data sources and key study information are summarised in Table  1. TXA pharmacokinetic data was either manually transcribed or digitally extracted from the selected source publications using DigitTM version 1. 0. 4 (a plot digitizer tool from Simulations Plus Inc. , Lancaster, CA, USA). Where concentration-time (Cp-time) data is reported for 3 (or less) subjects the individual subject data has been directly transcribed into GastroPlus. Where Cp-time data in more than 3 subjects is reported an arithmetic mean Cp-time profile has been calculated and this average profile used during modelling. To aid data visualization all model predictions and simulation results have been exported from the modelling software and summary plots have been constructed using R version 3.
Pharmacokinetic model development The modelling reported here has been performed using GastroPlus® (version 9. 5, Simulations Plus Inc. , Lancaster, CA, USA) a commercially available PBPK software. Where demographic data was not reported in the source reference, the default GastroPlus® Population Estimates for Age-Related (PEAR) settings have been used. Where body weight was not recorded default weights of 86. 3, 69. 1 and 61. 7 kg, were assumed for European/American, Chinese and Japanese subjects respectively. When demographic information provided included age but not body weight, the default PEAR body weight for the observed age was used. TXA is known to be primarily excreted unchanged in the urine with overall renal clearance equalling total plasma clearance  (Pilbrant et al. , 1981). Glomerular filtration rate (GFR) as calculated by GastroPlus® is dependant on the ethnicity, gender, age and body weight defined in the PEAR physiology file.
Pharmacokinetic model validation Intravenous and immediate release (IR) oral TXA PK profiles taken from a Lysteda registration study; NDA 022430 study XP12B-101, were used as an external model validation dataset  (Xanodyne Pharmaceuticals Inc. , 2009). During model development the key sources of IV and PO PK variability were identified. To allow the external validation data set to be predicted 'typical' estimates for these key parameters were defined as follows: All subjects in study  XP12B-101 (Xanodyne Pharmaceuticals Inc. , 2009)  were female however no body weight or age information for the individual participants was available. The American female PEAR physiology (30 years old, 75. 2 Kg) along with the core model parameters (Table  3 ) defined the clearance and distribution of TXA enabling prediction of the IV validation PK (1000 mg).
Across the model development data sets plasma concentrations ranged from 0. 2 to 94. 0 µg/mL, 0. 2-14. 5 µg/mL and 1. 3-21. 1 µg/mL following IV, oral and IM dosing respectively. For the method validation datasets plasma concentrations ranged from 0. 1 to 90. 4 µg/mL after 1000 mg IV and from 0. 1 to 12. 9 µg/mL after a 1300 mg oral dose. TXA (4-(Aminomethyl)cyclohexanecarboxylic acid) is a low molecular weight (157. 21 g/mol) zwitterionic compound and aqueous solubility and plasma protein binding are reported to be 167 mg/mL in water (Merck Index Online, 2020), >50 mg/mL at pH 6. 5-8. 0 and 3% bound respectively (Pharmacia and Upjohn Co, 2020). Visych et al. (2016)  reports a Caco-2 average permeation rate of 12. 4 × 10-06 ± 0. 1 cm/s,  (Visych et al. , 2016)  however no marker data is published alongside thereby making the result of limited use in PBPK model development.
The effective human permeability (Peff) predicted from structure using the ADMET predictor software is 0. 75×10-04 cm/s. No other relevant measured physical-chemical information was found in the literature. Table  3  summarises all core (fixed across all datasets) model parameters, highlighting if parameters were measured, predicted from structure or fitted using the model development PK datasets. Using NCA of the model predicted IV Cp-time profiles evaluated in this study, the average total plasma clearance (CLp) and average volume of distribution at steady state (Vss) of TXA are estimated to be 0. 10 L/h/ kg (range: 0. 091 to 0. 104 L/h/kg) and 0. 59 L/kg (range: 0. 546 -0. 613 L/kg) Variability in the oral PK across studies was primarily captured through variability in permeability and stomach transit time, see Table  4.
Estimates of Peff ranged from 0. 38 to 0. 50×10-4 cm/s (average = 0. 45×10-4 cm/s) across the model development studies evaluated, and STT ranged from 0. 38 to 0. 75 h in the fasted state (average = 0. 54 h). A stomach transit time of 1. 0 hour adequately described the two fed state datasets evaluated. However, when modelling the Pilbrant fed Model development datasets:  Chang et al. , 2004; Eriksson et al. , 1974; Pilbrant et al. , 1981; Puigdellivol et al. , 1985; SANO et al. , 1976; Sindet-Pedersen, 1987  state data,  (Pilbrant et al. , 1981)  an increase in the default Jejunum pH was required (J1 compartment pH increased from 5. 4 to 6. 0) because of the significant impact that intestinal pH had on the amount of predicted paracellular permeability, and therefore overall fraction absorbed (Fa), see Fig.
2  (right panel). Using the default fed state J1 pH of 5. 4 a small negative food effect (FE) is predicted (approx. 20% reduction in AUC compared to fasted state) and this was not observed by  Pilbrant (Pilbrant et al. , 1981). Only 2 IM PK datasets were identified, and the IM specific model parameters used to describe the observed variability in IM PK are summarised in Table  5. Both IM datasets were simulated using the minimum calculated depot volume of 42. 4 mL. Blood flow (Q) at the site of injection was found to be a very influential parameter on the predicted IM PK profile and the  Sano (SANO et al. , 1976)  and Puigdellivol  (Puigdellivol et al. , 1985)  IM PK were best described using muscle blood flow 2-3. 5 times lower than the default muscle blood flow (2. 86 mL/min/100 g). In addition, while the core model parameters (pKa, Log P, PPB%, Rbp and specific permeability surface area product) combined with the Poulin and Theil extracellular tissue partition co-efficient (Kp) estimation  method  (Poulin and Theil, 2009, 2000)  adequately describe tissue distribution for 6 out of the 7 studies evaluated this was not the case for the Puigdellivol dataset  (Puigdellivol et al.