dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow cytometric detection of apoptotic cells Melanoma cells were grown in control culture medium or treated with zeaxanthin 40 µM for 72 h before being stained for 30 min at 37 °C in PBS containing propidium iodide (PI 100 µg. ml -1 ) and Rnase A (100 µg. ml -1 ) (Molecular Probes, France). Cells were washed and suspended in 1 ml PBS before being analyzed using a FACS Cantoll flux cytometer (BD Biosciences, France) equipped with an air cooled blue LASER (=488 nm, 20 mW). Light diffusion parameters (forward and lateral scatter lights) were optimized to define the size threshold excluding cellular debris and cell clusters for single-cell fluorescence analysis. PI fluorescence was measured using a FL3 filter (nmand analyzed using the BD FACS Diva Software (BD Biosciences, France). Distribution of melanoma cells in the different cell cycle phases and hypodiploidy was determined according to their DNA content as measured by the fluorescence intensity of PI: Diploid cells (2n): G0/G1 phase; Replicative cells (2n<DNA content<4n): S Phase; Tetraploid cells (4n): G2/M phase; hypodiploid cells (DNA content < 2n): apoptotic Sub-G1 Phase).
Exposure to zeaxanthin inhibits the proliferation of A2058 melanoma cells A 72 h exposure to increasing concentrations of zeaxanthin [0-60 µM] induced a dosedependent reduction in the number of A2058 cells as compared to the untreated control, reaching 60% growth inhibition. The IC 50 value of zeaxanthin was determined as 40 µM using the free Graph pad Prism software "sigmoidal dose response" (variable slope) function. (Fig. 4. ).
Acknowledgements We are grateful to the  French cancer league (Comité 17 de la Ligue Nationale contre le Cancer)  for financial support and to the  Poitou-Charentes region for CJ 's PhD grant. We also thank the " Cancéropôle Grand Ouest , axe Valorisation des produits de la mer en cancérologie" for scientific support. LP is grateful to the  Brazilian Society of Pharmacognosy  for its kind invitation to the X th Brazilian Symposium of Pharmacognosy in Petrolina in September 2015. We thank  Dr Raymond Kaas  from  IFREMER  for his kind permission to use the microphotography of Porphyridium purpureum and  Antoine Bonnet  ( Platform for the High Resolution Analysis of Biomolecules, University of La Rochelle, France ) for excellent technical assistance with HRMS.
Data collection Capillary UA was measured using the HumaSens plus UA meter (European Conformity marked and approved for EU market use only) and compared to a standard UA measurement in the hospital biochemistry laboratory that used a venous sample taken and prepared as Li-Heparin plasma and an Abbott uricase automated colorimetric assay. LC-MS was performed on frozen Li-Heparin samples in AstraZeneca's Protein Biomarkers Laboratory (Precision Medicine and Genomics, Gothenburg, Sweden). Medical information (demographic characteristics, medication, and co-morbidities) were retrieved from medical files.
To estimate the confounding effect of metformin intake, which was frequent in this population of diabetic patients, the same analyses were repeated in patients with or without metformin intake. Calculations showed that 206 paired measurements were required for an ICC of 80% with precision 0. 10 and alpha risk 5%. Additional analyses were performed in the subset of patients with measurements of HumaSens plus capillary UA, plasma UA determined by uricase automated colorimetric assay, and plasma UA determined by LC-MS. Differences between methods were calculated. ICC and Lin's concordance coefficient were calculated between each pair of methods. Results for capillary UA and plasma uricase UA were plotted against plasma LC-MS UA results. Concordance between capillary UA and LC-MS UA data and between plasma uricase UA and LC-MS UA data were graphically assessed by the Bland-Altman method.
Medical forum's messages database: Detec't This was a retrospective study based on secondary use of data from the web -patients' medical forums in France. Detec't database is a private database aggregating messages from social media including safety information. Twelve well known medical forums were included in this study (Annex 1). Every discussion publicly available at the time of the search and containing at least one message with the name of the drugs of interest (active substance or brand name) was extracted using a webcrawler (Detec't Extractor). This messages scrapping was done by targeting the patients' messages using the HTML structure of each forum. This webcrawler was then adapted for each data sources. Messages were extracted with all the metadata related to the message (date, author of the post id, url of the discussion, name of the forum).
Finally data were cleaned (deletion of ads, quotation of other web users and signature) using the HTML structure of the posts. Retrieved messages went through several steps of processing so that the drug-event pairs necessary to perform the analysis were obtained. First, messages were de-identified. The data processing then automatically ensured that the messages contained the name of the drugs of interest and the concept of drug use or intake to constitute the corpuses of messages related to the drugs under study. These corpuses were formatted and screened to detect all references to medical events referring to potential adverse events (AE) (i. e. not the indication of the drug, not an AE preceding the intake of the drugs, not a question about adverse effects without having experienced it etc.
e. "Allergy NOS", were cleaned by removing the mention. Eventually, all terms were stemmed. A manual cleaning was performed to deduplicate the terms obtained after stemming. The detection of medical concepts was performed by looking for exact matches between the stemmed versions of MedDRA and messages. Potential adverse events were identified by applying an algorithm  [12]  based on a gaussian mixture model (GMM) and then a support vector machine (SVM) algorithm. This step identified drug-event pairs. Extracted messages were stored in the database with: -Metadata associated to the messages -Results of the annotation process: drug intake, medical concepts and their MedDRA code -Result of the adverse event detection algorithm The final step of the data preparation identified the number of messages for each drug-event pairs (Figure  1 ).
The comparison database used as Gold Standard was Vigibase ® , the World Health Organization (WHO) Global ICSR database. It consists of reports of adverse reactions received from member countries since 1968. VigiBase ® is updated with incoming ICSRs on a continuous basis. Vigibase ® data resource is the largest and most comprehensive in the world, and it is developed and maintained by the UMC on behalf of the WHO. By May 2015 over 11 million reports were available in the database. The Vigibase ® database system includes linked databases containing medical and drug classifications: WHO-ART/MedDRA, WHO International Classification of Diseases (ICD 10), and WHO Drug Dictionary. These classifications enable structured data entry, retrieval, and analysis at different levels of precision and aggregation.
Drugs Three drugs (insulin glargine, teriflunomide, zolpidem) were selected for the study to cover different therapeutic areas and different lengths of use since market authorization. Data corresponding to these selected drugs (active substance and brand name) were extracted from the Detec't database. The following drugs were therefore searched on the database: insulin glargine AND glargine AND Lantus®; teriflunomide AND Aubagio®; zolpidem AND Stilnox®. Synonyms of the drug name and spelling mistakes were taken into account (i. e. detail on misspelling). Misspellings were added by identifying most common errors and adding them as researched forms of the drug. In order to carry out the analysis of disproportionate reporting, a background group of 327 drugs was randomly selected from the Detec't database.
Descriptive analysis The data from 8 medical forums were considered for analysis and corresponded to messages published between January 1 st , 2005 and December 31 st , 2015 for insulin glargine and zolpidem and between April, 1 st 2014 and December, 31 st 2015 for teriflunomide. For teriflunomide, this time restriction was due to the fact that the product received its first marketing authorisation application in September 2013 and was first launched in March 2014. Figure  2  shows the messages flowchart. The extraction conducted to the identification of 102 messages for teriflunomide, 3326 messages for insulin glargine, and 4584 messages for zolpidem which were relative to drug intake. Among those, 61 messages for teriflunomide, 2335 messages for insulin glargine and 3732 messages for zolpidem contained medical concepts.
Comparative analysis For the period between 2005 and 2015, the medical forums data contained 545 drug-event pairs. 7618 pairs were identified in Vigibase ® , of which 422 drug-event pairs combinations overlapped with the forums data (Figure  3 ). The overlap considered an exact match with the event terminology. When restricting to pairs with at least 2 messages, the overlap was 275 drug-event pairs. Among the 545 drug-event pairs from the Forums, only 123 were not identified in Vigibase ®. Those 123 drug-event pairs corresponded each to only one message in the forums. The individual inspection showed that some PTs were not specific enough. For example, some PTs identified in the forums were "weight" but it was not possible to match them with the PT such as "underweight" or "overweight" or "abnormal weight gain" or "weight abnormal" that were identified in the drug-events pairs from Vigibase ®.
Up to 37. 5% of the common SDRs could be detected earlier when using the forums data which is an important finding. The qualitative exploratory analysis of the SDRs detected earlier showed that the events were related to serious as well as patient related symptoms. This finding are in light with recent studies  [15]  which addressed the question on earlier detection of drug-related adverse events in the social media as compared to FDA Adverse Event Reporting System (FAERS). The findings highlighted some of the promises of social media data sources for detecting early AE reports patterns compared to conventional pharmacovigilance sources and showed that social media AE reports helped predict the occurrence of FAERS reports several months later for one of the two drugs that were studied. In  [16]  the objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to FAERS.
Surveys A total of 94 vignettes were included in the study: 24 for training, 24 for results-free peer review, 13 for the use of reporting guidelines checklist, 10 for adding an expert to the process, 13 for the use of incentives, and 10 for post-publication peer review. Participants received an invitation via email with a personalized link to the survey. On the home page of the website, participants were informed that the data collected was anonymous and were asked to give their informed consent before starting the questionnaire. A maximum of three reminders were sent to participants, and no incentive was used to maximize the response rate. Participants were proposed two vignettes describing two different study designs to assess a same intervention, and had to indicate which study design they preferred (Fig.
Statistical analysis Answers for the online questionnaire were collected through the website. The results were recorded in a. csv file and analyzed with R v3. 2. 2 (http://www. R-project. org, the R Foundation for Statistical Computing, Vienna, Austria) and SAS 9. 4 (SAS Institute Inc. , Cary, NC). For each intervention and each outcome (overall preference, trust in results, and feasibility), the mean score for each vignette was calculated for each combination of designs in Does the use of reporting guidelines by the peer reviewer improve the quality of the final manuscript, compared to the usual process?.
Interrupted time series analysis In interrupted time series studies, data are collected at multiple time points before and after an intervention in order to detect whether or not the intervention had a significantly greater effect than any underlying secular trend. • Period 1: usual peer-review process. In the first part of the study, peer reviewers follow the usual process of peer review. • Period 2: addition of an expert to the usual peer review process. An assessment by an expert is systematically added to the usual peer review process.
RCT with randomization of peer reviewers Peer reviewers are randomized to the intervention group or the control group. Cluster RCT with randomization of journals Journals are randomized to the intervention group or the control group. All peer reviewers from a journal follow the same peer review process. Interrupted time series analysis Data are collected at multiple time points before and after an intervention to detect whether the intervention had a significantly greater effect than any underlying secular trend. • Period 1: Peer reviewers follow the usual process of peer review. • Intervention: All peer reviewers follow the process of the intervention. • Period 2: Manuscripts are evaluated after the peer review process.
Acknowledgements We thank  Elise Diard  for designing and managing the survey website. We thank  Iosief Abraha ,  Sabina Alam ,  Loai Albarqouni ,  Doug Altman ,  Joseph Ana ,  Jose Anglez ,  Liz Bal ,  Vicki Barber ,  Ginny Barbour ,  Adrian Barnett ,  Beatriz Barros ,  Hilda Bastian ,  Jesse Berlin ,  Theodora Bloom ,  Charles Boachie ,  Peter Bower ,  Matthias Briel ,  William Cameron ,  Patrice Capers ,  Viswas Chhapola ,  Anna Chiumento ,  Oriana Ciani ,  Anna Clark ,  Mike Clarke ,  Erik Cobo ,  Peter Craig ,  Rafael Dal-Ré ,  Simon Day ,  Diana Elbourne ,  Caitlyn Ellerbe ,  Zen Faulkes ,  Padhrag Fleming ,  Robert H. Fletcher ,  Rachael Frost ,  Marcelo Gama de Abreu ,  Chantelle Garritty ,  Julie Glanville ,  Robert Goldberg ,  Robert Golub ,  Ole Haagen Nielse ,  Gergö Hadlaczky ,  Barbara Hawkins ,  Brian Haynes ,  Jerome Richard Hoffman ,  Virginia Howard ,  Haley Hutchings ,  Philip Jones ,  Roger Jones ,  Kathryn Kaiser ,  Veronique Kiermer ,  Maria Kowalczuk ,  Yannick Lemanach ,  Alex Levis ,  Dandan Liu ,  Andreas Lundh ,  Herve Maisonneuve ,  Mario Malicki ,  Maura Marcucci ,  Evan Mayo-Wilson ,  Lawrence Mbuagbaw ,  Elaine McColl ,  Joanne McKenzie ,  Bahar Mehmani ,  John Moran ,  Tim Morris ,  Elizabeth Moylan ,  Cynthia Mulrow ,  Christelle Nguyen ,  Leslie Nicoll ,  John Norrie ,  David Ofori-Adjei ,  Matthew Page ,  Nikolaos Pandis ,  Spyridon N.
Plasma levels of insulin glulisine were measured by an exploratory LC-MS/MS method at Sanofi (Frankfurt). For calculation of mean concentrations, values below the lower limit of quantification (LLOQ = 0. 1 ng/mL in plasma) were set to zero. The PD response and the area above the curve (AAC) of blood glucose reduction were calculated and analysed using GraphPad Prism 7 software® and a two-way ANOVA with a Bonferroni post-test  (28). Results were expressed as the mean ± SEM. A significant difference was considered to be present if p < 0. 05. PK data was analysed using WinNonlin® 6. 4 software using a non-compartmental model and linear trapezoidal interpolation calculation. Peak concentration (C max ), the time taken to reach C max (T max ) and the AUC 0-4h were calculated from the plasma concentration profiles.
A C C E P T E D M A N U S C R I P T For histological evaluation, the isolated loops were immersed in 10 % (w/v) of formalin during 24 h and embedded in paraffin wax. 5 µm sections were cut on a microtome (Leitz 1512; GMI, USA), mounted on adhesive coated slides, and stained with haematoxylin and eosin (H&E) or Alcian blue. The different sections were visualised under a light microscope (Nikon Labphoto; Nikon, Japan) and images were taken with high-resolution camera (Micropublisher 3. 3 RTV; Q Imaging, Canada) and Image-Pro® Plus version 6. 3 acquisition software (Media Cybernetics Inc. , USA).
4  (31). Figure  1A  shows insulin glulisine net charge over the pH range 1-12, identifying the contribution of each chain to the final charge of the protein. In addition, an estimation of the isoelectric point and the molecular weight of the protein was performed. As observed in Figure  1B , the results obtained for the isoelectric point and the molecular weight via the software are very close to the values provided by the supplier (5. 0 and 5829 versus 4. 8 and 5823 g/mol, respectively). To complete the characterization of insulin glulisine, circular dichroism profiles were recorded over a range of pH and the CDNN software was utilized to estimate insulin secondary structure in the far-UV region (180-260 nm). These CDNN estimations are based on the comparison between the CD spectras of the test protein to reference profiles of known proteins structures.
The approximate contribution of the different protein components (α-helix, β-sheet, β-turn and random coil) to the overall structure (Circular dichroism spectra deconvolution) was estimated using the CDNN software. Only minor conformational changes were detected at the different pHs, with the exception of pH 5, where the α-helix values decreased compared to other profiles (Figure  1D ). These changes in the insulin conformation could be explained by the isoelectric point of the protein, which is close to 5, where the protein may show a different structure relative to other pHs. These results, together with the cationic nature of the click propyl-amine CD, indicate that complex formation will be favoured at pH above 4. 8. As it is envisaged that the final dosage would be a gastro resistant capsule, which would only release in the small intestine, a pH of 6.
Evaluation of the binding properties of the amphiphilic cationic CD and optimization of insulin glulisine NPs prepared using the layer-by-layer approach A range of nanocomplexes were prepared between the CD and insulin glulisine (molar ratio 1:3; 1:7 and 1:14 insulin: CD) as described in Section 2. 3. The binding properties of the CD following NP synthesis at pH 6. 5 were analysed by PAGE electrophoresis. The results indicate a high degree of complexation at the different ratios tested: the complexed insulin  was estimated to be 93, 95 and 100 % for molar ratio 1:3, 1:7 and 1:14, respectively (Figure  2A ). In spite of these promising results, the NPs displayed poor stability upon contact with simulated intestinal fluids (data not shown). In order to enhance the stability particularly in the presence of proteolytic enzymes, two components were added to the primary complex: a negatively-charged polymer, dextran sulphate, and a PEGylated phospholipid, mPEG-DSPE 2000.
ACKNOWLEDGEMENTS This work was supported by the  European TRANS-INT  Consortium, which received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement No. 281035. The authors would like to thank  Ms. M. Suarez-Fariña  and  Mr. M. Chenlo  for the technical expertise in the cell bioactivity assay,  Dr. M. Chronin  for the assistance with the use of the circular dichroism equipment and  Mr. Giuseppe Alessio  for the atomic force microscopy analysis.
Cutoff values for classifying good-quality plasma samples were calculated as described below. An outline of the study is provided in Fig. 1  of the Data Supplement that accompanies the online version of this article at http://www. clinchem. org/content/vol64/ issue5. The robustness of the candidate biomarkers was assessed using blood collected from patients in an intensive care unit (ICU), which included patients with severe sepsis (n ϭ 6), patients with liver cirrhosis (n ϭ 6), and a patient with liver cirrhosis and sepsis (n ϭ 1), and patients who had undergone cardiopulmonary resuscitation (n ϭ 4). Furthermore, samples from individuals of 2 exercise studies were included. Exercise study 1 involved circuit training until exhaustion and blood collection directly before and after the exercise bout (n ϭ 7), and exercise study 2 involved cycling for 3 h at 50% of the maximum volume of oxygen that each participant could use (VO 2 max) with 4 blood collection time points: directly before the exercise bout and after 1 h, 2 h, and 3 h (n ϭ 10).
TARGETED UHPLC-TRIPLE QUADRUPOLE-MS ANALYSIS For the quantification of candidate biomarkers, a targeted analysis was established using a UHPLC system (LC 30 AD, Shimadzu) coupled to a TQ 8050 triple quadrupole mass spectrometer (Shimadzu). S1P-d18:2 was separated on a 2. 1 ϫ 30-mm ACQUITY 1. 8-m high-strength silica T 3 column (Waters). Multiple reaction monitoring in the positive mode was applied. The multiple reaction monitoring parameters for S1P-d18:2 were as follows: precursor, m/z 378; product, m/z 262; Q1 prebias, 25 V; CE, 18 V; and Q3 prebias, 20 V. All further analytical details, as well as the sample pretreatment before mass spectrometric analysis and method validation, are provided in the online Data Supplement. Furthermore, raw data from the method validation are provided as skyline files (see Material file in the online Data Supplement; see also online Data Supplement for instructions where to get this freeware and how to use it).
STATISTICAL ANALYSIS The panel data (e. g. , data on blood kept at various temperatures) were evaluated using linear mixed-effects models to account for serial correlation between mea-surements of blood samples from the same individuals kept under different storage conditions. ROC curve analyses were used for the pairwise assessments of the diagnostic accuracy of the candidate biomarkers tested. These analyses were also used to calculate the analytical sensitivity and specificity. The Youden index was then used in accordance with our previous study  (21 )  to identify an optimal cutoff value to characterize good-quality plasma. P values Ͻ0. 05 and a false discovery rate of Ͻ0. 05 were considered statistically significant. Stata 13. 1 and SPSS 23. 0 (IBM) were used for the data analyses.
The time points 2 h and 4 h were chosen because in clinical and research settings the separation of plasma from blood can realistically be done in 2 h in most studies and in 4 h in almost all studies. Subsequently, a subset of 188 identified metabolites out of the 1843 ion masses was used to elucidate potential biomarkers that could reflect delayed processing of blood samples. The metabolite ion masses m/z 378. 239 and 89. 026 were found to be the most promising candidate biomarkers (see Fig. 2  of the online Data Supplement). These 2 potential biomarkers of sample quality were identified as (4E,14Z)-sphingadienine-C18-1-phosphate (m/z 378. 239; common name, sphingosine-1-phosphate d18:2; S1P-d18:2) and lactate (m/z 89. 026). Details of the unequivocal identification of S1P-d18:2 are presented in the online Data Supplement (see Fig.
Population pharmacokinetic modelling Population PK analysis was performed by non-linear mixed effects modelling using Monolix 3. 1R2 (Lixoft, Antony, France)  (Chan et al. , 2011). The model was developed in a stepwise manner. First, an exploratory graphical analysis of the midazolam and α-hydroxymidazolam concentration-time profiles in adults and, adolescents (rich data profiles), was performed using R version 2. 16  (R Core Team, 2017). PK data in adolescents were analysed, using WinNonlin version 5. 3 (Pharsight Corporation Inc. , California, USA). The model was developed using these rich data profiles. Sparse data from the young children data set were then added and the model was fine-tuned. Finally, significant covariates were considered to produce the final model. The α-hydroxymidazolam concentration-time profiles were analysed by adding a one-compartment model to the midazolam model.