dataesr/scientific-paragraphs-categorization huggingface dataset:
MTT Assay Two-fold dilutions of plant extract and geraniin ranging from 1000 µg/mL to 2 µg/mL were used to treat A549 cells at a density of 1. 5 × 10 4 cells per well cultured in 96-well culture plates. Cells were rinsed with PBS 1X and 120 µL of culture medium mixed with 5 mg/mL MTT (3-[4,5-dimethylthiazol-2-yl]-2,5diphenyltetrazolium bromide) solution was added 24 h post incubation at 37 • C. The cells were re-incubated for 2 h at 37 • C. MTT medium was then removed, and the formazan crystals were solubilized with 50 µL of DMSO. Absorbance was measured at 570 nm with a background subtraction at 690 nm. The CC 50 was determined using a nonlinear regression on Graphpad prism software.
Flow Cytometry Assay Cells were rinsed twice with PBS1X, trypsinated with 30 µL of trypsin/EDTA, and then fixed with 3. 7% PFA in MEM for 15 min. The cells were then subjected to a flow cytometric analysis using Cytoflex (Beckman Coulter, Brea, CA, USA). Results were analyzed using cytExpert software (Brea, CA, USA).
RT-qPCR Total RNA including genomic viral RNA was extracted from cells with RNeasy kit (Qiagen, Hilden, Germany) and reverse transcribed using E reverse primer (5'-TTCACCTTGTGTTGGGC-3') and M-MLV reverse transcriptase (Life Technologies, Villebon-sur-Yvette, France) at 42 • C for 50 min. Quantitative PCR was performed on a CFX96 Real-Time PCR Detection System (Applied Biosystems, Life Technologies, Villebon-sur-Yvette, France). Viral genomic cDNA was amplified using 0. 2 µM of each primer and GoTaq Master Mix (Promega, Charbonnières-les-bains, France). For each single-well amplification reaction, a threshold cycle (Ct) was calculated using the CFX96 program (Bio-Rad) in the exponential phase of amplification. A synthetic gene coding for nucleotides 954 to 1306 of the MR766 strain (GenBank: LC002520) cloned in the pUC57 plasmid was used as template to generate a standard curve, which then served to make an absolute quantitation of bound viruses.
Immunofluorescence Assay Cells were rinsed twice with PBS1X, fixed with PFA, 3. 7% in PBS1X, and permeabilized for 5 min (PBS 1X 0. 15% Triton X-100). Cells were then stained 1h at room temperature in the dark for ZIKV E protein using 4G2-Alexa 594 (1:1000 in PBS-BSA 2%). DAPI staining was used to label cells nuclei. Coverslips were mounted in Vectashield, and the fluorescence was observed using Nikon Eclipse E2000-U microscope. Hamamatsu ORCA-ER camera and NIS-Element AR (Nikon) imaging software were used to capture images.
Data Analysis Comparison between different concentrations was done by a one-way ANOVA test. All values were expressed as mean ± SD of at least three independent experiments. All statistical tests were done using the software Graph-Pad Prism (version 8. 0; La Jola, CA, USA). Values of p < 0. 05 were considered statistically significant for a Dunnett's multiple comparisons test. Degrees of significance are indicated on the figure as follow: ** p < 0. 01; *** p < 0. 001, n. s. = not significant.
Acknowledgments: We thank the  Emmy Noether Programme  of the  German Research Foundation DFG  (Grant number:  OS 497/6-1 ) for funding. We thank  Frédéric Prochazka  ( Lactips, Saint-Jean-Bonnefonds, France ) for providing us the sodium caseinate. A. K. T. thanks the  German Academic Exchange Service (DAAD)  for financial support. We thank  Alexandra Montembault  and  Thierry Delair  ( IMP, UCBL, France ) for helpful discussions. We thank  Agnès Crépet  and the efforts of the  Plateforme de Chromatographie ICL -IMP, France ,  Barbara Enderle ,  Elke Stibal ,  Daniela Mössner  and  Andreas Warmbold  for technical assistance. We thank  Gerald Urban ,  Marie-Pierre Laborie  and  Andreas Walther  for their collaboration.
Task assessment The usability of the syringes was evaluated for the following critical and essential tasks: 'Remove the needle cover,' 'Perform the injection', 'Deliver the full dose' and 'Disposal of the used syringe. ' As per FDA guidance, critical tasks would or could cause serious harm to the patient or user in case of use error or failure and essential tasks are the ones necessary for successful use of the product and associated with high risk but resulting in severity level below than critical tasks. Correct use was defined as achieving all assessed tasks in compliance with the IFU, including success and success with operational difficulties (SOD). The moderator recorded success with and without operational difficulties and use errors. Usability data are described as categorical data (N), and proportion frequencies (%).
Task assessment The primary endpoint was the number and percentage of participants that correctly used the X225PLUS-8mmUTW in comparison to the X225PLUS-12. 7mmSTW. For both products, 5 critical tasks were evaluated: 'Remove the needle cover,' 'Perform the injection,' 'Deliver the full dose,' 'Activate the guard,' and 'Disposal of the used product. ' For each simulation, the moderator assessed all tasks and recorded the results based according to the IFU of the products, without giving any feedback or indication of performance to the participant. The moderator recorded any use errors, SOD as well as success. For each use error or difficulty encountered by participants, the root cause was investigated. Usability data are described as categorical data (N), and proportion frequencies (%). Both performance-based and subjective data were analyzed to ensure that no new risks were identified.
Mathematical model of intramuscular and intradermal risks A mathematical model was developed with the R statistical software (version 3. 6. 3) based on published skin thickness data at 4 common injection sites for adults with diabetes (abdomen, arm, buttock, and thigh)  [33]. This model estimates intradermal (ID) and intramuscular (IM) injection risks using several needle penetration depths (NPD) for two needle lengths (12. 7mmSTW and 8mmUTW) at two injection angles (90° and 45°). These data were stratified by factor (gender, Body Mass Index, injection site). Tissue ID thickness, IM depth, and NPD values were generated using a random number generator function from the respective distributions, each defined by their mean and standard deviation. Logarithm transform was used for IM depth to correct for non-normality of the observed distribution.
The data presented in this article confirm that the 8 mm needle is not more likely to induce dermis administration than the 12. 7 mm needle when injecting at a 90° as well as at a 45° angle without raising a skinfold regardless of the injection sites (abdomen, arm, thigh, and buttocks). Limitations of these studies are inherent to the nature of simulated use studies, meaning that participants may perform differently their injections when observed during a simulation study than in their actual use. All subjective feedbacks were recorded according to participants' point of view including feelings, perceptions, and concerns at the time of the simulated testing. Therefore, clinical studies of the full combination product are required to confirm the results obtained in those human factors studies and go further by evaluating pain and depot localization of the drugs.
GC-MS identification The ethyl acetate extracts of endophytic fungi were analyzed using GC-MS QP2010-Shimadzu equipment operating in the EI mode at 70 eV. An agilent DB5MS column (30 m × 0. 25 mm × 0. 25 μm) was used with a 26 min temperature program of 60-320°C at 5°C/min. The temperature for the injector was 250°C, the carrier gas (helium) flow rate was 1 mL/min, and the split ratio was 1:5. The identification of the compounds was done by comparing the measured data with literature data as indicated in the text.
Bruker MS Workstation version 8. 2. 1 was used for LC-MS system control and data analysis. Alkaloid identification was done by multiple reaction monitoring (MRM) transition and retention time. The parameters used for the mass spectrometer under the ESI + mode were as follows: spray voltage 4500 V, cone temperature 300°C, cone gas flow 20 psi, heated probe temperature 250°C, probe gas flow 35 psi, nebulizer gas (nitrogen) flow 40 psi. Parameters for the cone energy and collision energy are listed in Table  2. All parameters were automatically optimized by Workstation.
Identification of natural compounds To investigate compounds present in the ethyl acetate crude extracts of S. amaranthi and G. masseei endophytic fungi, GC-MS and LC-MS were used. For GC-MS analysis, the identification of the compounds was performed by comparing the measured data with literature data. Thirteen and thirty-five compounds were identified in extracts of S. amaranthi (Table  3 ) and G. masseei (Table  4 ) respectively. Long-chain unsaturated or saturated fatty acids, such as oleic acid and octadecanoic acid were found in the both endophytic fungi while eicosanoic acid, 3-heptenoic acid, 2-nonenoic acid, butyric acid and oxalic acid were identified only in G. masseei extract. Gamma-sitosterol was reported only in the extract of S.
Acknowledgments. This paper is based on my PhD thesis [Xue17], which I wrote at  Université Paris-Sud. I thank my advisors  Vincent Lafforgue  and  Gérard Laumon  for suggestion of the project and support during the writing of my thesis. I thank  Jack Thorne  for his helpful comments on a draft of this paper. I thank the referees for their suggestions of improvements. The author is supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  714405 ).
Introduction Cancer management is challenged by large inter-and intra-patient variabilities in both disease progression and response to treatments. Thus, the quest for accurate and personalized cancer therapies has fostered the development of new technologies enabling multi-type measurements in individual patients and complex drug scheduling. To translate datasets available for an individual patient into personalized therapies and further ensure their precise administration, new mathematical approaches are required. Indeed, systems medicine, that involves the implementation of theoretical approaches in medical research and practice, is critically needed as emphasized in the roadmaps of the Coordinated Action for Systems Medicine (CaSyM) from the European Union (https://www. casym. eu, [1] ) and of the Avicenna action (http://avicennaisct.
Therefore we have set the SN38 clearance via Organs to be twice that of the liver clearance  [29]. Oxaliplatin clearance was set such that 54% of the total administered drug amount was cleared via the kidneys  [28]. The amount of platinum (Pt) bound within the Organs or within the Liver was set to 84% and 12% of the total dose, respectively  [31]. The Boughattas et al paper was used to give tissue concentrations, no data to our knowledge exists for humans so we have used this mouse data as a best approximation. The amount of platinum in the tissues was calculated from total amount found in the respective organs relative to total dose. 5-FU was shown to be mainly cleared through hepatic metabolism, so that the amount of drug cleared through the Liver was assumed to account for approximately 80% of the total dose  [30].
OPTILIV clinical datasets The OPTILIV trial included 11 colorectal cancer patients with liver metastases (7 men and 4 women with median age of 60). The combination of irinotecan, oxaliplatin and 5-fluorouracil was delivered to patients by Hepatic Artery Infusion (HAI) using the Melodie pump  [20]. The patients received an intravenous administration of cetuximab 500 mg/m2 over 2 h 30 min on the morning of day 1 which was not modelled. From day 2, chronomodulated HAI of irinotecan (180 mg/m2), oxaliplatin (85 mg/m2) and 5-fluorouracil (2800 mg/m2) were given (  Fig 10). Irinotecan was delivered as a 6-h sinusoidal infusion starting at 02:00, with a peak at 05:00 on day 2. Oxaliplatin was administered as an 11h 30min sinusoidal infusion beginning at 10:15 with a peak at 16:00 on days 2, 3 and 4.
5-fluorouracil was also delivered as an 11h 30min sinusoidal infusion beginning at 22:15 with peak delivery at 04:00 at night, on days 3, 4 and 5. The superiority of this drug scheduling compared to non-circadian based administration was demonstrated for intravenous administration within several international clinical trials  [37]. Between each drug infusion, there was a glucose serum flush which cleared the tubing. This was a 30-min sinusoidal infusion beginning at 09:45, and then again at 21:45 i. e. at the end of each infusion (  Fig 10). Plasma pharmacokinetics (PK) data was gathered after the first dose of irinotecan, oxaliplatin and 5-fluorouracil and measured longitudinally for each individual patient. Plasma concentrations of irinotecan and its active metabolite SN38 were determined, using high performance liquid chromotagraphy (HPLC), at the start of infusion, then at 2, 3, 4, 6, 8 h 15 min and 31 h 45 min post HAI onset, for a total of seven time points, including baseline.
Mathematical modelling A pump-to-patient mathematical model was designed as follows, irrespective of the drug delivery device. The drug solutions dynamics from the pump to the patient's blood was modelled using a Partial Differential Equation (PDE) considering time and 1 spatial dimension. This method was chosen as PDEs can take into account both time and space which was key for modelling systems such as pump delivery. The PDE was solved using a backward finite difference method written by the authors within Python 3. 5. 2 (https://www. python. org/). The drug PK models were based on Ordinary Differential Equations (ODEs) programmed using Python 3. 5. 2 and solved using the odeint function from the scipy library version '1. 1. 0' [44]. PK model parameter estimation involved a weighted least square approach, with conditions also placed on the drug clearance routes.
For the fit of the data of a given patient, the residuals were weighted by a estimated measurement error of 10% inline with precision values of the assay methods  [45] [46] [47]. This method allowed to correct the residuals to be of the same order of magnitude for the parent drug CPT11 and the metabolite SN38, or for oxaliplatin free and bound concentrations. The minimization of the least square cost function was performed by the Covariance Matrix adaptation Evolution Strategy (CMAES) within Python which has been shown to be successful at handling complex cost function landscapes  [48]. Model goodness of fit was assessed using the sum of squared residuals (SSR) and R 2 values. PK model parameter numerical identifiability given the available data was investigated in a two-step process as follows.
First, parameter sensitivity regarding the least-square cost function was computed via a global Sobol sensitivity analysis as a necessary condition for identifiability  [49]. This method assesses the relative contributions of each parameter to the variance in the cost function obtained when parameter values are varied, and thus allows for the identification of parameters which have no effect on the cost function and are therefore not identifiable from the available dataset. This step allowed a first reduction of the PK models. Next, likelihood profiles of parameters of the reduced models were derived following the procedure outlined in  [50]. Additional biological constraints derived from literature were added to ensure numerical identifiability of all parameters. This two-step model design process was undertaken as computing likelihood profiles is associated with a high computational cost.
PK models were fit to pooled data first to get an indication of general model fit then to single-patient plasma PK datasets independently to obtain patient-specific parameter values. Data was available for 10 to 11 patients which was too few to undertake mixed-effect population analysis and to reliably estimate the parameters variance within a patient population  [51, 52]. Sampling points at 6 hours post injection for irinotecan and 11 hours 30 mins post injection for oxaliplatin and 5-fluorouracil theoretically occurred at the same time as the start of the 30 min glucose flush, that is 9:45 for irinotecan and 5-fluorouracil, and 21:45 for oxaliplatin. As described in the results section, the flush was equivalent to the administration of the drug quantity remaining within the tube and logically influenced plasma drug concentrations.
Inter-patient variability and patient clustering based on PK parameters Given the relatively small number of patients, the inter-patient variability in parameter values was assessed using a nearly unbiased estimator of coefficient of variation (CV), CV ¼ 1 þ 1 4n � � � s m � 100; where μ is the parameter mean, σ the parameter sample standard deviation and n is the number of patients. Next, fuzzy c-means clustering was used to define patient clusters based on individual PK parameters, for each drug separately. The fuzzy c-means clustering was done using a python library sckit-fuzzy version '0. 2' (http://pythonhosted. org/scikit-fuzzy/). The method is based on the determination of cluster centroids and classification of patient parameter vectors into the clusters such that the following quantity is minimised: X n i¼1 X c j¼1 w 2 ij ðx i À c j Þ 2 where n is the number of patients, c is the number of clusters, x i is the parameter vector of patient i, c j is the centroid of cluster j.
w ij is the probability of patient i belonging to cluster j and can be expressed as: w ij ¼ 1 P c k¼1 x i À c j x i À c k � � 2 Note that, for a given patient i, the following holds: X c j¼1 w ij ¼ 1: The validity function proposed by Fukuyama and Sugeno was used to determine the number of clusters for each drug. The function is defined as: X n i¼1 X c j¼1 w 2 ij ðjjx j À c j jj 2 À jjc j À � cjj 2 Þ where � c is the average of the centroids. The number of clusters were chosen between 2 and n-1 inclusively such that the V FS was minimised. Plotting the clustering results was done using a multidimensional scaling (MDS) algorithm which projects multidimensional data onto a 2D plane while keeping distance metric scaled relatively to original data (Python library sklearn. manifold  [53] ). Correlation coefficients between original Euclidean distance and 2D-Euclidean distance were calculated were high for all models (> 0.
Methods For this retrospective and comparative study, all questions from GPs (PMIP-GPs and NoPMIP-GPs) to TUPVC from January 1st, 2015 to the December 31st, 2017 were reviewed. During the study period, the network involved 165 GPs among a total of 3,565 GPs in the TUPVC intervention area. TUPVC is one of the 31 French regional PV centers. One of the missions of these structures is to answer to questions about drugs from health professionals or from drug consumers. The questions can be asked by phone, email or mail or with the free smartphone app VigiBIP ®  [11]. TUPVC answers using safety databases such as the French pharmacovigilance database, the World Health Organization (WHO) global individual case safety report database (VigiBase), the Drugdex System, drug reference books (Martindale, Meyler's), and scientific publications (Medline).
05). The proportions of GPs who asked at least one question were also compared between the two groups using a Chi 2 test, with a 5% significance level. Categories of questions (''General'' and ''Related to a patient'') and involved drugs were compared between PMIP-GPs and NoPMIP-GPs, using a chi-square test (or when the effectives expected were lower of 5, a Fisher test), with a 5% significance level. Statistical analyses were performed with the SAS software Version 9. 4.
DDD staining. After two-month incubation of the six M. ulcerans strains in the Hungate tubes in anaerobic conditions, a 20 µL-volume of each suspension was smeared on glass slide, air dried and stained using DDD staining protocol as previously described  [12, 13]. DDD staining was also applied on the aerobic and heat inactivated cultures of the six M. ulcerans strains. Microscopic observation was performed using a 63X/1. 4NA oil immersion objective of Zeiss Spinning Disk Confocal microscopy driven by Zen software (Zeiss, Marly-le-Roi, France).
Eligibility criteria and journal selection Two reviewers (NL and AG) used Web of Science to select journals classified in the "SURGICAL" category with a 2017 impact factor higher than 2. Surgical journals were included if there were explicit instructions for clinical trial publication in the instructions for authors, or if they published at least one RCT between 1 January 2016 and 31 December 2018. Two authors (NL and AG) independently extracted the data. Disagreements were resolved by consensus or in consultation with a third reviewer (DB). The list of journals was extracted in December 2018, and the official instructions for authors (OIA) were downloaded on 13-14 January 2019. Our primary outcome was the existence of a datasharing policy in the instructions for authors. The following classification was used to describe the types of policies for data sharing: "Inclusion of data-sharing policy mandatory", "inclusion of data-sharing policy optional" and "no data-sharing policy available".
Additional details on research transparency promotion were also collected, namely, the existence of a "prospective registration of clinical trials requirement" policy, a "COIs" disclosure requirement, and a specific reference to reporting guidelines such as CONSORT for RCT. Other variables were also extracted, in order to detail journals features as well as potential predictors for journals' adherence to research transparency rules. The 2017 journal impact factors were extracted from the Web of Science data base. The number of RCT published in 2016 and 2017 (1/1/16 to 12/31/17) was extracted from PubMed. As described in the protocol, it was originally planned to extract the number of RCT published in 2016, 2017, and 2018, but this was not possible because at the time of data extraction, not all RCT published in 2018 were fully indexed in PubMed.
Quantification of Ergosterol Quantification of ergosterol was performed as previously described by Barreira et al. [10]  using high performance liquid chromatography (Ultimate U3000 Thermo Fisher Scientific Inc. , San Jose, CA, USA). The system was operated using the Chromeleon software, version 7. 0. Chromatographic separation was achieved on an ODS Hypersyl C18 column (250 mm × 4. 6 mm, 5 µ m, Thermo Fisher Scientific Inc. , Waltham, Massachusetts, USA), with a column temperature maintained at 30 °C. Mobile phase consisted of a mixture of methanol and acetonitrile (isocratic 30:70 during 30 min, then methanol 100% during 10 min, flow rate: 1 mL/min). 20 µ L of the extracts (5 mg/mL) were analysed. Ergosterol was quantified by comparison of the area of its peak with the calibration curve (250 µ g/mL, 62.
1% formic acid/3% water + 0. 1% formic acid system (flow: 2. 8 mL/min). Fractions were collected using the Agilent Technologies 1260 Infinity G1364C fraction collector and the ChemStation for LC 3D software for automatic UV peak detection (diode array detector G13115A). This led to 8. 7 mg of xanthochymol (2)  [18]  and guttiferone F (3)  [19]  as a mixture, namely GX. Another semi-preparative HPLC on a PFP column (Hypersil Gold PFP, 150 mm × 10 mm), using a 50 mg/mL concentration for the injection (100 µL), with a 75% methanol/25% water + 0. 1% formic acid system (flow: 4. 7 mL/min) yielded 2. 9 mg of pure xanthochymol (2) and 3. 6 mg of pure guttiferone F (3) from the remaining F7.