dataesr/scientific-paragraphs-categorization huggingface dataset:
( USA), which has been systematically recording hospital discharge diagnoses and prescriptions on computers since 1972 [20]. In the late 80s, computers started to become rather common in general practice, notably in the United Kingdom where the VAMP Health group designed software to help general practitioners to file their patients' records. A joint effort between the VAMP and the BDCSP group led to the dream of any researcher: the first large database fed by data extracted from the routine activity of thousands of practitioners. The general practice research database (GPRD) was born and remains one of the rare examples of a tool specifically conceived for pharmacoepidemiologic research. In 2012, the GPRD became the clinical practice research datalink (CPRD) with larger ambitions and different stakeholders.
Samples LC-HRMS analysis In agreement with the literature recommendations  [14] , a previously reported method  [17, 19, 22, 23]  was used to investigate NPS presence in the 3 samples. Briefly, the ultraperformance liquid chromatography system consisted of two binary solvent manager LC pumps, a sample manager autosampler and a column manager oven AcquityTM. Mass spectrometry data were acquired using a XEVO G2-XS QTOF (Waters, Manchester, UK) instrument controlled with MassLynx™ 4. 1 software. Extractions of samples (75 µL of 1 mg/L methanolic solution of powders or 1 out of the 5 blotting papers extract using 500 µL of methanol) were performed using an OASIS HLB on-line column (30 x 2. 1 mm, 20 µm) (Waters, Manchester, UK). The mobile phases consisted in 0. 2 % ammonia solution (A1) and 100 % methanol (B1) and the elution profile was as follows: 0.
Conditions for the time of flight mass spectrometer (TOF MSe) scan mode were as follows: scan range 100-1000 m/z for the function 1 and 50-1000 m/z, with a collision energy ramp from 10-40 eV, for the function 2. Data process was performed using ChromaLynx, TargetLynx, MassFragment and MetaboLynx associated softwares (Waters) using a homemade database of more than 1. 600 substances including more than 650 (at september 2018) NPS or metabolites (including BZF and pFBF).
Fifty µL of a cofactor mixture (5 mM UDPGA, 1. 3 mM NADPH, 3. 3 mM G6P and 0. 5 U/mL G6PD) in 0. 1 M Tris-HCl was then added. The enzymatic reaction was performed at 37 °C for 60 min and stopped by the addition of 200 µL of methanol. Samples were frozen at -20°C until analysis. HLM incubate samples were consequently analyzed using the LC-HRMS method described above. Data process was performed using ChromaLynx, TargetLynx, MassFragment and MetaboLynx associated softwares (Waters, Manchester, UK). A software algorithm (MetaboLynx™) was used in order to in silico predict BZF and pFBF biotransformations. Main known routes of FDs metabolism includes N-dealkylation to from norfentanyl derivatives, hydrolysis to form despropionylfentanyl (ANPP) derivatives and numerous sites of hydroxylation. Many of these phase I metabolites undergo phase II metabolism to form corresponding glucuronides or sulfates.
For instance, data about parafluoroisobutyrfentanyl (pFiBF, structurally close to pFBF) metabolism were available since 2017. A total of 17 metabolites were identified: two monohydroxy metabolites and a hydroxymethoxy metabolite were suggested as specific and abundant pFiBF metabolites  [15]. As a consequence, taking into account these data, sets of theoretically possible biotransformations of BZF or pFBF were created, and the automated metabolite profiling process was completed through the use of the fragmentation interpretation software tool MassFragment TM 4. 1 (Waters), to enable software driven assignment of metabolite structures from fragmentation patterns. The LC-HRMS acquired data of HLM incubates are subsequently cross-checked to in silico predicted biotransformations: criteria used for identification of the targeted compounds included 3 mDa as exact mass error of the precursor ion, as well as product ions, and +/-0.
Flow cytometry analysis The cell death induced by laxaphycins was determined with the Annexin V-FITC Apoptosis Detection Kit (Immunostep, Spain) following manufacturer´s instructions. 1x10 6 SH-SY5Y cells per well were seeded in 6-well plates and incubated for 6h with laxaphycins. After treatment, cells were washed with PBS and resuspended in Annexin binding buffer containing Annexin V-FITC and Propidium Iodide. SH-SY5Y cells were incubated for 15 min and analysed by flow cytometry using the ImageStreamMKII (Amnis Corporation, Merck-Millipore). The fluorescence of 10000 events was analysed with IDEAS Application 6. 0 software (Amnis Corporation, Merck-Millipore).
d protein detection system. The immunoreactive bands were detected using the Supersignal West Pico Chemiluminiscent Substrate or Supersignal West Femto Maximum Sensitivity Substrate (Pierce) and the Diversity 4 gel documentation and analysis system (Syngene, Cambridge, U. K. ). Chemiluminiscence was measured with the Diversity Gene Snap software (Syngene). A list with the antibodies and concentrations used in this work can be found in supplemental information. For protein measurements in membrane fraction, cells were washed with PBS and a hypotonic solution was added (20 mM Tris-HCl pH 7. 4, 10 mM NaCl and 3 mM MgCl2, plus phosphatase/protease inhibitors cocktail). Then, cells were incubated 15 min on ice and centrifuged at 3000 rpm, 4 °C for 15 min. The supernatant was saved as the cytosolic fraction and the pellet was resuspended in a nuclear extraction buffer (100 mM Tris pH 7.
Acknowledgments: Financial support was provided to BB, IB and LB by the  Laboratoire d'Excellence (LabEx) "CORAIL" (Keymicals and Cyanodiv)  and the  GDR CNRS 3658 MediatEC  (https://www. gdr-mediatec. cnrs. fr/english-version/). Chromatographic, spectroscopic, and structural analyses were performed using the facilities of the  Biodiversité et Biotechnologies Marines platform at the University of Perpignan (Bio2Mar , http://bio2mar. obs-banyuls. fr/fr/index. html). We thank  Olivier Thomas  and  Stephane Greff  for HRMS facilities and  Christian Roumestand  for NMR measurements. USC receive support from the following FEDER cofunded-grants; from  CONSELLERIA DE Cultura , EDUCACION e ordenación  Universitaria Xunta de Galicia ,  2017 GRC GI-1682  ( ED431C 2017/01 ); from  CDTI and Technological Funds , supported by  Ministerio de Economía, Industria y Competitividad ,  AGL2014-58210-R ,  AGL2016-78728-R(AEI/FEDER,UE), ISCIII/PI16/01830  and  RTC-2016-5507-2 , ITC-20161072; from  European Union   POCTEP 0161-Nanoeaters -1-E-1 ,  Interreg  AlertoxNet  EAPA-317-2016 , and  H2020 778069-EMERTOX.
The field of polymer-based nanomedicine was negatively impacted in May 2016 when BIND therapeutics (a nanotechnology company based in Cambridge, Massachusetts) went bankrupt as a result of a failed high-profile clinical trial  (Weisman, 2016). The company's lead compound BIND-014 was a PEG-b-PLA nanoparticle formulation encapsulating docetaxel and targeting the PSMA, which is overexpressed in prostate cancer as well as on the neovasculature of several non-prostatic solid tumors  (Chang et al. , 1999; Rajasekaran et al. , 2005). BIND-014 was the first targeted controlled release polymeric nanoparticle to enter clinical trials  (Von Hoff et al. , 2016). A successful Phase II clinical trial in 64 patients with non-small cell lung cancer (NCT01792479) spurred its development. However, a disappointing Phase II clinical trial (NCT02479178) in head and neck cancer, halted its progression in the clinic.
For example, in a proof-ofprinciple study, 60 solid dispersion formulations were produced using ten chemically diverse, neutral, poorly soluble drugs, three commonly used polymers, and two manufacturing techniques (spray drying and melt extrusion). The extensive experimental dataset was used to build multiple linear regression models to correlate physicochemical properties of the drug with stability data. These models indicate which combination of processing method and polymer carrier is most likely to give a stable solid dispersion  (Fridgeirsdottir et al. , 2018). Many challenges remain to be solved before 3D printing can be applied to the manufacture of solid dosage forms, particularly, in scalability/business model, quality control, regulatory approval for any process which devolves the manufacture away from a central facility towards the patient and in the new constraints it brings to formulation.
Background : Modi ed nucleosides re ect nucleic acids turnover, and are eliminated into the urine. They can serve as noninvasive biomarkers for monitoring tumour dynamics, and treatment responses. Methods : 8 modi ed nucleosides were determined by LC-HRMS in urine voids collected in three linical trials recorded on NCT01693848, NCT01693861 and NCT01693835 by a total of 39 patients. The patients' circadian timing system was studied by wrist actimetry. Rhythms parameters were estimated using Hidden Markov model (HMM) for telemetric activity data and cosinor analysis for urinary nucleosides excretion. Results : Pseudouridine, was ~ 10-fold larger than those of 1-methylguanosine, 1-methyladenosine, or 4-acetylcytidine, and ~100 fold larger than those of adenosine and cytidine. In St 1, a signi cant increase in the overnight urinary excretion of 1methylguanosine was associated with prolonged 4-year survival in patients with R1 resection for liver metastases.
Study design The patients had a histological proof of metastatic colorectal cancer, a Performance Status of 0 or 1 according to the classi cation of the World Health Organisation and adequate biology. They had received one, two or three prior chemotherapy protocols and provided a signed informed consent for their participation. The study took place at the Department of Hepatobiliary Surgery (St1) or Oncology (St2 and 3) at Paul Brousse University Hospital (Villejuif, France). For St2 and 3, the rest activity rhythm was monitored using a wrist-worn piezoelectric accelerometer watch (Mini-Motionlogger Actigraph, Ambulatory Monitoring, Ardsley, New-York, USA) for 17 days (St2, data not reported here) or 5 days (St3). The user-de ned time interval for the count of wrist accelerations was 1 min.
Cosinor Analysis for Quantifying of Circadian Rhythmicity in Urinary Nucleosides Excretion A multiple-component cosinor model  [37]  was applied to describe the circadian pattern of nucleosides excretion for St 3. More speci cally the model contained two cosine functions with respective periods T12 = 12-h and T24 = 24-h: yt = M+A12cos 2πtT12+θ12+A24cos 2πtT24+θ24+et (1), where yt is the urinary nucleosides excretion at time t; M is the mesor (mean level); A12,A24 and θ12,θ24 are the corresponding amplitude and acrophase for each cosine term; et is the error. The ve parameters, i. e. , M, A12,A24, θ12,θ24 in the non-linear model (1), were estimated using the R package nlme  [38] To avoid over-tting, individuals with less than seven timed nucleosides excretion samples were excluded from the cosinor analysis.
There were two technical failures, with no data collection. Two hundred and ninety-three urine samples were collected by the 30 patients. The number of samples was insu cient for a single patient. Overall, a total of 226 nucleoside excretion data points were exploitable for the analysis of 29 patients, corresponding to a protocol compliance rate of 95. 9%. Overall, nucleosides excretions were determined in 557 urinary samples in the three studies. Overnight nucleosides excretions in the absence of treatment Within each St, the early morning levels of urinary nucleoside excretion, corresponding to overnight elimination into the urine, varied by up to nearly 100-fold according to nucleoside type in the absence of any treatment for the past 2 weeks (Figure  2 ). The excretion of pseudouridine was nearly 10-fold as large as those of 1-methylguanosine, 1-methyladenosine and N2-N2dimethylguanosine, and 20-30 fold as large as those of 4-acetylcytidine and 1-methylinosine.
Potential role of pharmacoepidemiology in pediatric medicine development and evaluation Considering the above mentioned, applications of observational research tools in pediatric drug development are quite straightforward. In this section, we suggest and develop some applications although not in an exhaustive manner. The specificities of pediatric pharmacoepidemiological studies will not be detailed as they have been explicitly presented in previous publications  [13, 14]. Still, it is important to stress that the development and use of healthcare databases for research purposes has considerably facilitated the conduct of pharmacoepidemiological studies and greatly reduced the cost and time of study completion  [15, 16].
Drug safety studies and Pharmacovigilance alerts According to prospective observational studies, the incidence of ADRs has been estimated at 10. 9% (95% CI 4. 8 to 17. 0) in hospitalized children and 1. 0% (95% CI 0. 3 to 1. 7) in outpatient children, while 1. 8% (95% CI 0. 4 to 3. 2) of ADRs leads to hospital admission  [61]. The risk of ADRs is not different from that reported in adults  [62] , but the analysis of spontaneous reporting databases found differences regarding medications and clinical manifestations, with anti-infective and respiratory medicines involved more frequently in reports in children than in adults, and a much larger proportion of the pediatric reports involving skin reactions compared to the adult reports  [63, 64]. Several examples exist on how findings from the analysis of spontaneous reporting data and/or observational epidemiological studies in the pediatric population led to a change in drug licensing status.
Future perspectives in pediatric pharmacoepidemiology Different study designs and numerous data sources can be used to conduct pharmacoepidemiological studies  [69]. Increasingly, studies incorporate computerized data obtained in the course of clinical care, or medical data to generate real-world evidence. Real-world data could help to fill the gaps in evidence regarding effectiveness and safety of many drugs in children  [70] , while being aware that robust methodologic approaches are needed to obtain valid results and minimize bias  [71]. Sources of realworld data primarily include electronic healthcare databases (electronic health records, claim and billing data), product-and disease-based registries, and also pharmacovigilance databases. In this section, we first focus on the relevant data sources in pediatrics, then develop research opportunities in pediatric pharmacoepidemiology and finally describe the interest of collaborative research.
Electronic healthcare databases Health care databases mainly consist of pharmacy databases, primary care databases, hospital databases or healthcare insurance databases. In 2008, Neubert et al. made an inventory of all databases available for pediatric research in Europe  [72]. A total of 25 data sources from 12 European countries were identified, of which the majority was suitable for paediatric pharmacoepidemiological research. In 2016, the survey of Lasky et al. , conducted in the Pediatric Special Interest Group (SIG) and International Society of PharmacoEpidemiology (ISPE) membership, expanded this information  [13]. The findings of this survey suggested that use of available electronic databases is currently suboptimal in the conduct of pediatric pharmacoepidemiological studies. Another important finding was that respondents reported working with a total of 59 distinct databases of various types and only 8 databases were mentioned by more than one respondent.
This seems to indicate that, in pediatrics, researchers work in widely varying settings, diseases and with numerous databases. Hence, this further outlines the need to identify the relevant databases available. Moreover, this diversity may pose a special challenge for the methodological approaches and raises the issue of the validity of extrapolating from a national database to a global context. Two additional concerns identified by the previously mentioned survey  [13] , which are frequently reported and still present, were the sample size and a lack of important information in children such as in utero exposure to medicines, detailed age information, drug dosing, measures of growth, nutritional status, performance in school, medical diagnosis or socioeconomic data. To increase study power, one alternative which is already applied and needs to be developed, is to combine the data from the different country-specific databases into one analysis dataset  [36, 73].
To collect all available healthcare data on one single individual, linkage of various databases in one country or region might also be appropriate. Linking all relevant databases through a patientunique key provides the researcher with necessary data. It is a common practice in studies on the teratogenic potential of a drug. For example, a recent cohort (POMME for PrescriptiOn Médicaments Mères Enfants) has been implemented for the evaluation of long-term consequences of drug prenatal exposure, linking data from i) the French health insurance database (drugs and medical care prescriptions and reimbursements to children and mothers during pregnancy) and ii) the mother and child protection centre database (child health certificates at birth, 9 and 24 months)  [74]. Linkage of relevant databases is still scarce in pediatric pharmacoepidemiology and this approach needs to be further developed in the future.
Pediatric registries Disease and product registries that systematically collect medical data on eligible patients are an essential resource to capture important information. Registries commonly enroll patients who are often "particular" in terms of the complexity of their underlying disease, their comorbidities, and their concomitant medications. Linking registries to healthcare databases may provide full medical information. Unfortunately, to date, a complete assessment of all relevant registries is not available in the pediatric population. The Food and Drug Administration (FDA) performed a study to determine which pediatric registries would be valid to conduct a post-marketing study  [75]. Characteristics identified as more common to successful registries utilizing a disease-based registry and a registry established prior to product approval.
Pharmacovigilance databases Databases collecting spontaneous adverse drug reaction (ADR) reports were implemented to identify possible pharmacovigilance signals at the earliest  [76]. To detect very rare ADRs, large databases are required. Because of its size, VigiBase ® , the WHO individual cases safety reports (ICSRs) database, can play a major role in pediatric drug safety surveillance. It covers reports provided from more than 125 different countries and since more than 45 years now. It is the largest database of spontaneous reported ADRs worldwide. Vigibase ® can be used to detect disproportional reporting and signals of specific risks  [77, 78]. However, one limitation of Vigibase® is missing data because reporting of most information is not mandatory. To improve the quality of the data and the reliability of the database, the WHO Collaborating Centre for International Drug Monitoring (Uppsala Monitoring Centre, UMC) has developed a completeness score to identify well documented ADR reports  [79].
Most studies apply traditional methods to control for confounding by indication, including multivariate modelling analysis and matching. Propensity scores adjustment may be implemented  [81]  but they are still seldom used in pediatric pharmacoepidemiological studies. Study designs, such as the case-only designs, have been developed and could be successfully applied in pediatric drug evaluation. Both, the self-controlled case series (SCCS) and the case-crossover (CO) designs comprise selfcontrolled analyses that eliminate confounding and selection bias by time-invariant characteristics not recorded in healthcare databases  [82]. Quality assurance programmes, and sensitivity analysis could also help improve or ensure the robustness of findings  [81]. Finally, an important future step would be for academic institutions to develop and funding agencies to support the development of high quality healthcare databases and rigorous educational training in pediatric pharmacoepidemiology.
The assumed average maintenance daily dose for a drug used in its main indication is validated in adults, not in children  [88]. Several research groups have developed methods to improve quantification of drug use in pediatrics however, these mainly focused on antibiotic use  [89]. Another important challenge concerns the over-the-counter (OTC) or non-prescription drugs widely used in children. In young children, visits to emergency frequently involve OTC drugs  [90] , but OTC drugs are not always assessed in routine healthcare and thus, are nor captured in electronic healthcare databases. Given the limited regulatory requirements for OTC drugs and the risks associated with their use, it is important to reinforce data collection on the use and effects such medications.
This pattern occurred because all the additional reinfections that occurred as the FOI increased were   1 ) as a function of force of infection (FOI) and are calculated using survival analysis. The multiplicity of infection (MOI) is drawn from data from Tanzania-a relatively high-transmission area. misclassified as recrudescence. Conversely, in the absence of any reinfections (when FOI ϭ 0), the noncorrected algorithm produced an accurate failure rate estimate by correctly classifying all recurrences as recrudescence (leaving only a slight underestimate due to patients who had recrudescent parasites at levels of Ͻ10 8 , such that no recurrence occurred during follow-up). The ability of the four molecular correction algorithms to accurately estimate drug failure rates depended on their ability to correctly classify recrudescences and reinfections.
This ability is shown (for an FOI of 8, i. e. , a moderate-transmission area) in Fig. 3. Each algorithm misclassified some proportion of recrudescences and reinfections. The number of recrudescences misclassified as reinfections was consistent as the FOI changed, but the number of reinfections misclassified as recrudescence increased as the FOI increased-the results are shown in the supplemental material. (Note that while results for all parameterizations of AR-LF, AS-MQ and DHA-PPQ are not shown, the   2 ). The multiplicity of infection (MOI) is drawn from data from Tanzania-a relatively high-transmission area. The x axis shows the true status of patients on the day of recurrence (i. e. , reinfection or recrudescence), and the color coding shows how these patients were classified by each algorithm.
A 49-day follow-up for AS-MQ appeared to be the most accurate with the Ն2/3 markers algorithm, but increased accuracy over the WHO/MMV algorithm was also seen with shorter follow-up periods. Models of nonfailing (i. e. , clinically effective) pharmacokinetic/pharmacodynamic (PK/PD) calibration of AR-LF and AS-MQ showed that the Ն2/3 markers algorithm FIG  4  Analysis of simulated trial data for DHA-PPQ showing the impact of changing follow-up period with follow-up lengths of 28 days, 42 days (as in Fig. 2 ), and 63 days. Estimated failure rates are shown for the different molecular correction algorithms (Table  1 ) as a function of FOI and calculated using survival analysis. The multiplicity of infection (MOI) is drawn from data from Tanzania-a relatively high-transmission area. slightly underestimated the true failure rate, but this difference was small, and there is no evidence that this algorithm would incorrectly identify effective drugs as failing.