dataesr/scientific-paragraphs-categorization huggingface dataset:
The results on ER requirements for drug A and B are independent of the age of children as the ER does not depend on absolute values of Θ Qh and Θ CLint u;WL. However, the ERs of drugs are often unknown, especially in special patient populations, because as presented in Fig. 1 , ER in children changes due to maturation of both CLint ,u,WL and Qh and the impact of these changes are isoenzyme-dependent due to different maturation patterns. To facilitate the identification of model drugs for which clinical data could be obtained to precisely estimate population values and variance of CLint ,u,WL and Qh at different ages, maturation patterns of different isoenzymes were used to identify isoenzymes and ages for which drugs with the lowest and highest ER required for such estimation are likely to exist.
Since most paediatric PK studies include patients of a range of ages, estimation of a maturation function would be preferable over the estimation of parameter values for each specific paediatric age. In the future this work could be extended to the design of studies for the estimation of such maturation functions. This will require the a priori definition of the most suitable covariate for maturation function and optimization of study designs for the estimation of parameters in continuous covariate functions, the latter of which is not yet available in the PFIM software. In the developed analytical workflow, optimal design principles were applied in an innovative manner. The clinical design parameter optimized was the ER of the drugs included in the clinical trial. In a classical optimal design setting, optimized clinical design parameters include the number of patients and samples, sampling times, and drug dose.
ACKNOWLEDGMENTS AND DISCLOSURES We would like to thank  Sinziana Cristea  for reviewing all model codes used in this study. Trevor Johnson  is a paid employee of Simcyp Limited. Professor Amin Rostami-Hodjegan  holds shares in Certara, a company focusing on Model-Informed Drug Development. This author has completed the Unified Competing Interest form at http://www. icmje. org/coi_disclosure. pdf(available on request from amin. rostami@ manchester. ac. uk) and declares no support from any organisation for the submitted work. This study was supported by the  Innovational Research Incentives Scheme  ( Vidi grant ,  June 2013 ) of the  Netherlands Organization for Scientific Research (NWO)  to  Catherijne A. J. Knibbe  (2013). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.
First, the prototype usability was tested by six experts of the Regional Pharmacovigilance Centre of Rennes. Their experience was assessed with the MORAE software and a System and Usability Scale (SUS) questionnaire. Then, to quantify the timeline contribution to pharmacovigilance routine practice, three of them were asked to investigate possible ADR cases with the "Usual method" (analysis of electronic health record data with the DxCare software) or the "Timeline method". The time to complete the task and the data quality in their reports (using the vigiGrade Completeness score) were recorded and compared between methods. Results: All participants completed their tasks. The usability could be considered almost excellent with an average SUS score of 82. 5/100. The time to complete the assessment was comparable between methods (P = 0.
The re-use of clinical data from patients has been facilitated by the emergence of querying interfaces for clinical data warehouses, such as i2b2 tools  [2]  or complete systems (database and interface), such as eHOP  [3]. These systems allow querying and analysing heterogeneous clinical data, and have been successfully exploited for clinical research (e. g. , feasibility studies, patients' pre-screening and recruitment for clinical trials  [4] ) and epidemiological studies (cohort detection)  [5]. Pharmacovigilance is another field where the exploitation of health databases could play a key role  [6]. Among the different data-mining methods, visual data analysis could be useful for exploring complex data. Indeed, visualization of data on the history of individual patients or groups of patients could allow highlighting chronological clues and causal relationships between drug exposure and ADR.
Timeline description The prototype's graphical user interface (GUI) was developed using the JavaScript d3. js library  [14]. For a broad use, this component was designed as a web service, weakly coupled to the EHR data source (eHOP for the prototype). eHOP (from entrepôt de données biomédicales de l'HOPital), the biomedical data warehouse used by Rennes University Hospital Centre, contains comprehensive laboratory and clinical data on 1. 6 million patients. It provides access to heterogeneous clinical information in the form of free text (hospitalization reports, discharge letters, etc. ), of data coded using different terminologies (e. g. , the International Classification of Diseases, 10th revision, ICD-10, medical diagnosis codes), and structured data (laboratory results, prescriptions and drug administrations).
The prototype used a web interface to allow interactive navigation. Real patient data were used for the prototype assessment. Figure  1  is a screen mock-up in English to show how the information is displayed in the timeline. A real screenshot (in French) and demo are available in Additional file 1. To the best of our knowledge, this application includes new features. Specifically, semantic data aggregation/expansion was implemented by leveraging the hierarchical properties of different terminologies (Anatomical Therapeutic Chemical, ATC, classification system; French Common Classification of Medical Procedures, CCAM; ICD-10; and the local terminology used by the Clinical Laboratory of Rennes). This approach allows users to interactively control the granularity of the displayed information.
Recruitment of experts Expert users were recruited from a team of pharmacists and physicians who work at the Regional Pharmacovigilance Centre in Rennes and who are part of the target audience for our tool. Based on literature data  [16] , it was determined that five users were sufficient to identify 80% of the problems linked to the application's usability  [17]. Finally, six people (five women and one man among whom there were five pharmacists and one physician) could be recruited for the usability study, and three of them were also involved in the impact quantification study. These participants perform routinely pharmacovigilance activities. The six people received no financial compensation in exchange for their participation. Their mean age was 31. 3 ± 4. 5 years (range: 25 to 40). All routinely used computers as part of their pharmacovigilance activity, particularly to study patient records.
The working environment (operating system, computers, etc. ) was the same as the one used at the Pharmacovigilance Centre. Usability tests (six sessions with one participant per session) were performed in the presence of the evaluator, who observed the interactions between participant and interface and noted his/her observations on paper. Each participant received a notebook to record all relevant data retrieved on the use case. The usability testing sessions were recorded on video, as well as all interactions with the interface (keyboard input and mouse movements within the application). The recording and subsequent analysis were made using MORAE (TechSmith, software version 3. 3. 4). The video recordings were analysed after all participants had completed all the tasks. The MORAE software can capture on-screen video footage, while simultaneously recording the interactions between participant and GUI.
Selection of patient records for the impact study For the quantitative impact study, 743 potential cases from the 2015 eHOP data were selected according to a list of ICD-10 codes that were potentially related to ADR and the method described by Osmont et al. [28]. This list was defined in a previous internal study of the Pharmacovigilance Centre based on 2014 data. Only ICD-10 codes with the highest probability of matching an ADR were selected (Fig. 2 ). Then, 85 cases were randomly chosen, in order to have roughly the same proportion of the different ICD-10 codes for the three participants and for each analysis method (usual method versus timeline, see below). A Python script was used to randomize the cases among the three participants.
Procedure for impact quantification These 85 cases were reviewed by three members of the Regional Pharmacovigilance Centre of Rennes using two methods: the "usual method" (EHRs and DxCare® software, see Additional files 2 and 3), and the "timeline" method (timeline prototype). Participants were provided with a list of patient records and instructions on which method to use for each record (Fig. 2 ). The working environment (operating system, computers, etc. ) was the same as the one used at the Pharmacovigilance Centre. As in the routine pharmacovigilance practice, they had to gather information to establish a possible causality link between the diagnosis and the administration of a drug. They also wrote down the start time and end time for processing each file. The process was considered finished when the participant had retrieved all the required information or when he/ she considered that this information was not present or could not be found with the used method.
08). This difference could be explained by the systematic need to consult the patient's laboratory results to judge the drug involvement in this type of ADR. Among the 85 selected cases, 68 corresponded to actual ADR. Therefore, data quality was assessed and compared only for these records using the vigiGrade Completeness score. Overall, the average quality of the notifications (report for each adverse event) produced with the two methods was not significantly different (P = 0. 49). Similar results were obtained also when comparing the notification quality among the three participants (Table  4 ).
Availability and requirements Project name: RAVEL Timeline. Project home page: e. g. http://devtools. univ-rennes1. fr/ websvn/Timeline/ Operating system(s): Platform independent. Programming language: Java 8, HTML 5, CSS 3, JavaScript. Other requirements: Java 8 or higher, Oracle 11 g or higher. License: GNU GPL. Any restrictions to use by non-academics: For use by non-academics, please contact Pr Marc Cuggia: marc. cuggia@univ-rennes1. fr.
Immunophenotyping of Endothelial Cells On culture days 25 and 40, ECFCs cultured with and without OPG were detached using accutase then incubated with primary or isotype control antibody and analyzed by fluorescenceactivated cell sorting using a FACSCalibur cytometer (Becton Dickinson, France). We used directly conjugated primary murine monoclonal antibodies specific for the following surface antigens: CD34, CD144, CD105 (Beckman Coulter, Cylex, France); CD133 (Miltenyi Biotec, France); CD31, CD73, CD61/51, VEGF-R2 (BD Pharmingen, France); CD146 (Santa Cruz Biotechnology, France); CD54, CD106 (AbCys, France), CD45, and CD115 (Immunotech, France). Corresponding isotype stains were used as negative controls. Data plotting was performed using CellQuest software (BD Biosciences, France).
OPG Modulates CD34 Surface Expression by ECFCs To investigate OPG influence on ECFCs phenotype, we used flow cytometry to monitor the expression of selected markers on days 25 and 40 in the OPG and control groups. Mature endothelial cells markers (vWF, P-selectin) were highly expressed (data not shown). Immunophenotyping revealed that ECFCs cultivated under both conditions express the endothelial cellsurface antigens CD31, CD105, CD144, CD146, and VEGR-R2 at approximatively the same level. None of the groups expresses any significant amount of hematopoietic cell surface antigen CD45, CD14, and CD115. However, we found on day 25 that OPG-exposed ECFCs expressed higher levels of CD34 + compared to control ECFCs (Figures  2A, B , p = 0. 04). The percentage of CD34-positive cells was increased 1.
001). All reads were aligned against latest NCBI-nr database (downloaded in March 2018) using the DIAMOND-BLASTX algorithm. The significant matches were defined by e-value ≤ 10 * e-value of the top hits and were retained to define taxonomic groups  60, 61. Taxonomical levels were determined using the lowest common ancestor-based algorithm implemented in MEGAN  62  (MEGAN Community Edition, version 6. 10. 10). A bit score of 86 was selected for filtering the results  63, 64. Functional profiling was performed using HUMAnN2 (version 0. 11. 1)  65 and uniref50 database to establish KEGG orthologies and EC. Alpha diversity analyses were performed using vegan R package (version 2. 4-0). The Chao1 and observed-species biodiversity indices were computed and comparisons were performed using a nonparametric two-sample mean difference test with 999 Monte Carlo permutations.
Beta diversity analyses were performed using Bray-Curtis distances in the vegan R package and were compared between groups with ANOSIM  50. Relative abundances of species or functions were compared with non-parametric Mann Whitney and p-values were adjusted using false discovery rate. To identify the origin of the bacterial communities that composed the fecal microbiota in sample collected on Day 16 in mice, we used the Bayesian source tracking program SourceTracker (version 0. 9. 8) as described by Knights et al. 14. This probabilistic OTU-based algorithm employs an iterative Bayesian approach to predict which OTUs in several potential source communities (here, samples from untreated mice, samples collected on Day 8 and Day 12) are likely to contribute to those in sink communities (here, samples collected on Day 16).
Data Analysis. All statistical tests were executed using Graphpad Prism v6. 0b (Graphpad Software Inc. , La Jolla, California, USA) and SPSS Statistics v17 (SPSS Inc. , Chicago, Illinois, USA). All data are presented as average ± one standard deviation. Comparisons between infant and adult data groups were performed using Student's t-test following confirmation of the normality of the distributions. Statistical s i g n i fi c a n c ef o rt h ed i ff e r e n c eb e t w e e nt h et w og r o u p sw a s accepted at the level of p<0. 050. Data are presented as boxand-whiskers graph following Tukey's method as coded in the Graphpad software.
Background: The reporting of adverse drug reactions (ADR) by patients represents an interesting challenge in the field of pharmacovigilance, but the reporting system is not adequately implemented in France. In 2015, only 20 MS patients in France reported ADR due to first-line disease-modifying drugs (DMD), while more than 3000 patients were initiated on DMD. The aim of this study is to validate a proof-of-concept as to whether the use of a mobile application (App) increases ADR reporting among patients with relapsing-remitting multiple sclerosis (RR-MS) receiving DMD. Methods/design: We designed a multi-centric, open cluster-randomized controlled trial, called the Vigip-SEP study (NCT03029897), using the App My eReport France® to report ADR to the appropriate authorities in E2B language, in accordance with European regulations.
For example, in 2014, ADR reported by patients represented 4. 5% of all reports, compared with 10% in Europe, according to data from the French National Database of Pharmacovigilance  [16]. Most patients remain unaware of reporting systems or are confused about reporting  [17]. Therefore, "e-health" applications could be a convenient method to more closely connect with patients  [18]. It has been demonstrated that the use of mobile applications to report adverse events, such as those related to a medical device, can save time and increase the number of notifications. On average, there were 15 times more reports submitted per month via the App MedWatch® with patient community support than submitted via traditional pharmacovigilance portals  [19]. In 2016, it was estimated that 50% of the population and 99% of young physicians in France own a smartphone.
The most widely used medical Apps by young physicians are Apps related to drugs (drug database, drug interaction database)  [20]. A smartphone/tablet application seems to be a necessity for modern pharmacovigilance approaches (i. e. , MedWatcher® in USA or YellowCArd® in UK). In France, several applications dedicated to ADR reporting are available on the market (i. e. , VigiBip®, My e-Report®, Medisafe®), but these applications are not yet widely used by patients. For example, in the Toulouse University Pharmacovigilance Center, patient reports submitted by the App VigiBip® (6. 7% of total reports received by the center) were significantly more frequent than those submitted via classical methods (3. 4%) (p = 0. 01)  [21]. To further promote patient involvement, teaching tools, such as mobile applications, should be encouraged, and these tools should be tested rigorously.
Intervention All patients included in Vigip-SEP will be informed that they can report their ADR using a pharmacovigilance system. They will also be informed of the existence of the App My eReport France® developed to report ADR in accordance with European and French regulations. In experimental centers, patients will be introduced to its use by neurologists. In control centers, they will not be introduced to its use but will be encouraged to report ADR through usual reporting methods. The My eReport France® application was chosen for the Vigip-SEP study, developed by the eVeDrug® society, to report ADR to the appropriate French authorities in E2B language, in accordance with European regulations. The application is certified by the label "My health quality. " Data hosting is provided by a certified health data host.
Coordination is provided by an investigative team of MS academic experts at the center of CHU de Caen. This study was approved by the Ethics Committee of Nord-Ouest III with approval number 2016-42. The privacy of the participants and their personal medical records will be guaranteed by treating the data according to the French law n. 78-17 of 6 January 1978 and the "European Union Data Protection Directive (95/46/EC 24 October 1995). " The approved version of Vigip-SEP is V3 29. 11. 2016; the recruitment began in June 2017, and the end date is anticipated in May 2018. The sponsor CHU de Caen is responsible for reporting any protocol modifications to the centers, ethic committees and ANSM.
Outcome measurements Primary outcomes The primary outcome is the number of ADR patient reports. ADR reports will be collected in both arms for 6 months after the inclusion of each patient from the national database of French authority, ANSM (see "Data collection procedure" below). An example of the method of aggregation is as follows. For center 1, m1 = n1 reports/N1 patients, where n1 is the number of ADR in the center, and N1 is the number of patients included in the center. The method of aggregation in each group follows the formula Ʃmx/N, where N is the number of centers in the group.
Data collection procedure The data manager of CHU de Caen is responsible for form development, sponsor database development and data management. Regardless of how ADR are reported, they are sent to a regional pharmacovigilance center for assessment, which includes MedDRA coding and causality analysis. Then, the regional pharmacovigilance center records the ADR in the national database. At the end of the study, the 31 regional centers of pharmacovigilance will anonymously extract ADR reports pertaining to the first-line DMD in the trial period from the national database. The investigator's team will identify reports of the Vigip-SEP population (of the two groups), and the Vigip-SEP investigators will compare the received date and the region, age and sex of the patient. ADR reported by patients within the 6 months of inclusion and by neurologists within the same period will be recorded in the sponsor database.
Statistical criteria for stopping the study: no intermediate analysis is planned, and the study will be stopped when all centers have obtained the required number of patients. Method for accounting for missing, unused or invalid data: the patient is first informed of the study, and then, the patient's statements are collected for 6 months. Each patient will be contacted by telephone 1 month (± 15 days) after inclusion. The 1-month follow-up questionnaire will be used to determine whether the patient should be removed from the study and replaced according to the following considerations: If the patient has reported at least one ADR, the patient data are preserved If the patient did not report an ADR: Because they had no ADR, the patient data are preserved Because they did not like the method of reporting, the patient data are preserved For technical reasons (loss or breakage of the telephone/tablet), the patient is replaced by another patient, and these data are not analyzed The sponsor CHU de Caen applies random audits of their trials of low-risk studies.
Developing a culture of reporting ADR by RR-MS patients is a relevant goal. In 2015, in France, 20 ADR were reported by MS patients compared with 271 ADR reported by physicians concerning first-line DMD according to data from the French National Database of Pharmacovigilance. In the future, we hope that this new method of reporting will improve pharmacovigilance by RR-MS patients and can be extended to include all new DMD. The overall goal is to be able to improve the early detection of unexpected adverse reactions not revealed by clinical consultations. If our hypothesis is confirmed, other neurological diseases could be explored using this method.
Phytochemical analysis of an aqueous extract that mimics the traditional preparation. We next investigated whether borapetoside C (1) and other potentially toxic related clerodane furanoditerpenoids could be identified in an aqueous extract of fresh stems that mimics the traditional preparation. First of all, the peak assigned to 1 was also found predominant in the chromatogram obtained by HPLC/ DAD/MS and depicted in Fig. 2  (middle chromatogram). However, this finding was not really surprising since glycosides are essentially water-soluble. Subsequently, a targeted qualitative analysis of the furanoditerpenoids structurally related to 1 was carried out in two ways: by using ultra-high performance liquid chromatography/electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS/MS) and by analysing the generated data on the basis of the exact mass of the compounds and their fragmentation pathways, in combination with a molecular networking approach for the whole dataset processing and especially their filtering.
3 ). Major fragment ions of these compounds are also listed in Table  2 , as well as the parent ions values observed in the molecular network (bolded values). Tentative identification of furanoditerpenoids analogues of 1 was based on the determination of molecular formulas deduced from accurate m/z values and fragmentation patterns and additionally through dereplication using database search including the Dictionary of Natural Products, Reaxys and SciFinder. Chemical structures of the putatively identified furanoditerpenoids are presented in Fig. A second group of borapetoside C analogues is represented by compounds bearing a hydroxyl group at the C-2 position such as borapetoside B. They all shared common fragmentation pathways with probably similar reactions to those described for 1, as illustrated in Fig.
Standards. Authentic herbal reference standard. Stems were originally collected in 2013 in the Botanical Garden of the Hanoi University of Pharmacy in Hanoi, Vietnam and authenticated par Plant Records Officer Dr Quoc Huy Nguyen. A voucher specimen is stored in the herbarium collection of the François Tillequin Museum of Materia Medica (Faculty of Pharmacy, Paris) under the N°02486. Borapetoside C. Borapetoside C was isolated from T crispa in our laboratory by column chromatography and unambiguously identified by 1 H,  13  NMR and MS by comparison with literature data  9, 10.