dataesr/scientific-paragraphs-categorization huggingface dataset:
ITT can however be used when the duration of follow-up is limited to the expected duration of treatment. Analysis will commonly use time-dependent variables in survival analysis methods with Kaplan-Meier curves and Cox proportional hazards analyses. When death is a common occurrence that may act as a competing risk, specific analyses such as Fine and Gray competing risk model should be used for the other, non-fatal outcomes  [95]. Cohort studies can provide absolute risks and added risks, which may be important to make regulatory decisions. There are many other methodological approaches or considerations, that are amply discussed each year during the annual ICPE meeting (www. pharmacoepi. org), with the use of big data, or the enrichment of claims data with data from patient data warehouses or new data sources that include not only health expenditures, but also clinical, pathology, societal or genetic information.
In summary Spontaneous reporting of medical drug-related anecdotes has been the mainstay of drug safety or Pharmacovigilance for years, with derived analyses such as causality (imputology) or disproportionality analyses. If the absence of other information possibilities, it has served for many regulatory decisions to restrict or remove drugs from the market. The development of large population databases such as electronic health records or claims databases allows for the more precise evaluation of the association of exposure to drugs of interest with specific events of interest, using scientifically validated methods. These should become the main source of information for regulatory decision-making. Spontaneous reporting would be used for that which it is best suited: an alert or hypothesis generating system.
Selection of articles Among the 357 articles retrieved, 126 were selected on the basis of the abstract (118 in English, 7 in Spanish and 1 in Italian) (Figure  1 ). We excluded 22 articles associated with cocaine consumption and 5 to hematological complications. A further 15 were excluded for insufficient clinical data or already reported (n=9) or unclear cause of vasculitis or vasculopathy etiology (n=6). Overall, 111 articles were selected for the analysis, reporting 193 patients including one reported in 2 separate manuscripts. One of the cases reported in a series was excluded because of the absence of vasculitis or vasculopathy. Finally, we identified 192 patients. (Table of patients and references in supplemental data).
No information on specificity was available for 40 patients. Complement C3 and C4 protein levels were quantified in 87 patients; concentrations were within the normal range for 58 (66. 7%); C3 and C4 levels were decreased for 14 (16. 1%); C3 level was decreased and C4 level normal for 4 (4. 6%); and C4 level was decreased with normal C3 level for 11 (12. 6%). Cryoglobulin was detected in 12/63 patients (19%) and found weak (n= 1), detectable (n=2), trace (n=1), and positive (n=8). In all, 10/12 patients with cryglobulin underwent hepatitis C virus (HCV) serology and 2 were positive, including 1 with negative HCV PCR findings and 1 in whom PCR was not performed. Among patients with cryoglobulinemia, 4 had cutaneous vasculitis, 6 thrombotic vasculopathy without vasculitis and 1 unspecific cutaneous histology and 1 skin biopsy findings (no indirect IF data available).
Indirect IF assay was performed for 21 biopsies and was positive for immunoglobulins (Igs) and/or C3 deposits in 16 (76. 2%) (IgM=12, IgG=8, IgA=8, C3=15). Among 24 patients with acute renal failure, 13 underwent renal biopsy. Crescentic glomerulonephritis was identified in 10/13 biopsies (77%); 2 were pauci-immune because of no deposits seen on IF assay, and 1 showed IgG, IgM and C3 deposits on IF assay (IF data not specified for others). One biopsy identified immune-mediated glomerulonephritis with IgG and C3 deposits, and another showed an association of crescentic glomerulonephritis and membranous nephropathy with IgG, IgM, C3 and C1q deposits. The last one showed glomerulonephritis with thrombotic microangiopathy.
Acknowledgements The authors thank the  Directorate General of Higher Education (DGHE) of Indonesia  for the financial support of  Nurbaeti S. N. (the funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication). DTA/TGA analyses were performed at the  University of Poitiers Pôle Commun de Mesures Physico-chimiques platform. SEM imaging was performed at the  University of Poitiers imaging platform ImageUP. Spray-drying experiments were carried out in Biocydex, Poitiers. The authors thank  Ms. Agnès Audurier ,  INSERM U 1070 and Faculty of Medicine and Pharmacy , for her technical assistance, and  Mr. Patrice Godin  and  Mr. Christophe Adier ,  INSERM U 1070  and  CHU de Poitiers , for their technical assistance in the spectrophotometric and HPLC determinations and the Quality Management of the analytical process.
Chemical Maleic acid (99. 98 %) was provided by Merck (Darmstadt) as TraceCert® quality for quantitative NMR. Deuterated acetonitrile (ACN-d3), deuterated methanol (MeOH-d4), deuterated water (D2O) and deuterated dimethylsulfoxide (DMSO-d6) were purchased from Eurisotop (Saarbrücken). Nuclear Magnetic Resonance analysis 5 to 10 mg of each powder was completely dissolved in deuterated acetonitrile and deuterated methanol (because of possible low solubility of compounds in acetonitrile). Several spectra were recorded: 1 H, 13 C{ 1 H}, 13 C DEPT135, 1 H-1 H gCOSY, edited 1 H- 13 C gHSQC and 1 H- 13 C gHMBC. In case of fluorinated compound, 19 F and 19 F{ 1 H} NMR spectra were also recorded. Based on the combined analysis of these 1D-and 2D-data and the molecular mass, a structure was established by the operator.
However, 256 scans were in this case not enough to record a decent 13 C{ 1 H} NMR spectrum (spectrum not shown). Except for the two quaternary C2 and C5 atoms, all carbon nuclei could be detected indirectly (by HSQC and for C8, using HMBC), so we could identify the main compound in Powder 3 unambiguously without recording another dataset using more spectrometer time. The COSY spectrum of powder 3 (Figure  19 ) allows the identification of 3 different spins systems for the main compound. The first one, displayed in green, corresponds to the already identified paradisubstituted pattern. The presence of higher order coupling at such a frequency between the two aromatic protons is in line with a chlorine atom directly attached to the phenyl moiety. The black one corresponds to a CH3-moiety directly attached to a CH moiety.
Methods Recently, the ESCEO commissioned several systematic reviews and meta-analyses of randomized, placebo-controlled trials to reassess the safety of various anti-OA medications, including topical nonsteroidal anti-inflammatory drugs (NSAIDs), symptomatic slow-acting drugs for OA (SYSADOAs) and intra-articular hyaluronic acid (IAHA)  [30] [31] [32]. For this purpose, the MEDLINE, Cochrane Central Register of Controlled Trials, and Scopus databases were comprehensively searched. The methodology and the outcomes of these systematic reviews have been extensively described in each manuscript. From the articles included in each systematic review, we collected information on how AE data were reported; details on how AE-related results were reported in each article retrieved from the systematic review processes can be found in the tables describing the characteristics of the included studies in each specific manuscript reporting the outcomes of these new meta-analyses (in this supplement)  [30] [31] [32].
This working group included rheumatologists, specialists in physical medicine and rehabilitation, clinical epidemiologists, endocrinologists, pharmacologists, orthopedic surgeons, geriatricians, specialists in public health and health economics, preclinical scientists and representatives of patients with OA. The working group took place on 1 December 2017 in Geneva, Switzerland, where the results of the new meta-analyses on the safety of anti-OA medications were first presented. The majority of the working group members have been clinical investigators for anti-OA medication trials and/or clinicians. All 20 of the members of the ESCEO working group on the safety of anti-OA medications were invited to take part in the consensus-making process. The Delphi process aimed at reaching consensus on a final general framework for the reporting of harms-related data in manuscripts.
any AE ("total AEs") 2. any "severe AEs" 3. any "serious AEs", as well as 4. the total number of patients who withdrew from the trial due to AE(s) For "total AEs" and "withdrawals due to AE(s)" (1 and 4), the five most frequent specific events should also be reported. For "severe AEs" and "serious AEs", always report all specific events. Report the number of participants who experienced each specific event at least once. These data should be separately reported for each arm of the randomized trial All severe and serious events need to be captured, rather than just the top five 6 Report separately any clinically relevant AEs that would not be captured on the basis of items 4 and 5 of the current recommendations, considering all TEAEs This reporting should be done, irrespective of the SOC (consider all MedDRA SOCs) 7 Report information on drug effects on any biological parameter that may be relevant to the drug mechanism of action or potential for any adverse biological effects.
For consistency, the following parameters should always be measured: serum level of urea and creatinine, AST and ALT, full blood count (white and red blood cells, platelets) and hemoglobin. When reporting information about drug effects on biochemical and other biological parameters, numbers of trial participants with abnormal changes in these parameters at all timepoints, from baseline to end of treatment, per arm should be reported Do not consider a change in the normal range. A change from the normal to the abnormal range should be considered as abnormal change. For patients with abnormal values at baseline, a change of at least 1. 5 times from the baseline value (worsening), should be considered as abnormal change 8 Harms-related data must always be reported in a table within the results sections of manuscripts on RCTs; additional text description of harms-related results in this section is optional Examples of tables for harms reporting are presented in the Electronic Supplementary Material to this paper same events (of interest) might not be reported by different authors.
Various environmental chemicals acting as substrates, inhibitors or modulators of transporter activity or expression have been shown to impact TK, just as drugs do. However, because pollutant concentrations are often lower in humans than drugs and because exposure levels and internal chemical doses are not usually known in contrast to drugs, new approaches are required to translate transporter data and reasoning from the drug sector to CRA. Here, the generation of in vitro chemical-transporter interaction data and the development of transporter databases and classification systems trained on chemical datasets (and not only drugs) are proposed. Furtheremore, improving the use of human biomonitoring data to evaluate the in vitro-in silico transporter-related predicted values and developing means to assess uncertainties could also lead to increase confidence of scientists and regulators in animal-free CRA.
, 2008; Worley and Fisher, 2015). Recently, FDA scientists reported the use of PBK models as a tool to evaluate the contribution of major transporters to drug ADME processes, and summarised the PBK models provided in regulatory submissions by drug developers to illustrate the transporter-related questions that arise in light of regulatory assessments  (Pan et al. , 2016). A similar exercise with the available data would benefit the toxicological community. Furthermore, PBK models are increasingly being used as an effective tool for designing toxicology experiments and for conducting extrapolations essential for RA  (Paini et al. , 2017b; Paini et al. , 2019). Commercial PBK modelling software tools, such as Simcyp, PKSim and GastroPlus among others, allow predictions of drug disposition in virtual patient populations.
These challenges include mechanistic understanding of transporter biology or interaction, robustness of the in silico models, broad substrate specificity of transporters, accuracy or size of the dataset, lack of metabolite libraries or divergent membrane topologies  (Clerbaux et al. , 2018; Giacomini et al. , 2010). Again, quantitative knowledge of transporters at protein and functional levels in cell lines and tissues, and for multiple species, should be systematically and carefully reported. This knowledge can be applied as a scaling factor in PBK models, such as for oral drug absorption  (Harwood et al. , 2012). To enhance the overall predictive performance of transporter-based PBK models, it is necessary to have a mechanistic understanding of transporter biology for proper representation in the PBK models in addition to knowledge of transporter specificity for the chemical of interest  (Pan et al.
All highly permeable chemicals would act as Class 2 ("efflux transporter limit oral absorption") even if highly soluble, as no saturation of the efflux pumps would be achieved, in contrast to drugs. While chemicals with low permeability, even highly soluble ones such as glyphosate, would likely behave as Class 4 ("efflux and uptake transporters impact oral absorption"), if they are substrates of transporters, considering that their intestinal concentration remains low. As the concern for pollutants is generally the opposite as for drugs, e. g. to limit bioavailability, this would be protective. However, highly soluble and permeable pollutants could still behave as Class 1 and pass through the membranes if they are not substrates of efflux transporters. These considerations are still theoretical and a classification system built on chemical datasets, taking into consideration low intestinal concentrations, would clearly represent a useful tool to support animalfree CRA.
Transporter databases Several large-scale efforts have already been implemented to systematically gather transporter data into databases (Table  from   Clerbaux et al. , 2018 , summarising existing transporter databases). However, such databases mostly constitute transporter information for drugs, while databases based on environmental chemicals, such as pesticides, metals, food contaminants or cosmetic ingredients, are lacking. Again, several experts emphasised that there is a need for publicly available databases reporting transporter protein levels and activities in cell lines versus in vivo tissues, across species and in normal and diseased human individuals  (Clerbaux et al. , 2018; Gordon et al. , 2015).
Model predictions need to be validated with available and relevant in vivo data. In this review, we highlighted some areas for further improvements in CRA, including the need to: (i) characterise toxicological (cytotoxic or metabolic) in vitro methods in terms of their transportome profile; (ii) develop and perform highthroughput transporter in vitro assays to generate data on chemicaltransporter interactions; (iii) develop transporter databases and classification systems trained on in vitro chemical-based datasets; (iv) make better use of human biomonitoring data to evaluate the in vitro-in silico predicted values; and (v) develop means to quantitatively assess uncertainties. Furthermore, although a vast amount of knowledge has been gained during recent decades regarding transporters in many tissues of the body, there is still limited data in other toxicologically relevant organs.
Data collection Physicochemical properties and in vitro Absorption, Distribution, Metabolism and Elimination data were obtained from the Drug Bank (https://www. drugbank. ca) and from the literature. All data are presented in Table  1. The 13 compounds chosen for the analysis represent a broad range of physicochemical properties (lipophilicity, biopharmaceutical classification system, and compound type) and major elimination pathways (five CYP3A, three CYP1A2, one CYP2C9, one CYP2C8, one UDP-glucuronosyltransferase (UGT)2B7, two renal). Clinical PK data for the different adult and pediatric populations were collected from the literature. A total of 59 clinical studies from the 13 compounds were available, of which 4 premature infants (< 1 month), 4 neonates (< 1 year), 19 infants (1-2 years), 11 young children (2 years up to 6 years), 13 children (9-11 years), and 6 adolescents (12-18 years) were included.
The details of these studies are presented in Table  S2. The plasma concentration-time profiles were digitized using the software WebPlot Digitizer v. 3. 12 (https://automeris. io/WebPlotDigitizer/). Models PBPK. Whole-body PBPK models are in silico models that mathematically describe the mechanisms underlying drug disposition in an organism. They predict the PK profiles based on compound physicochemical properties and multiple anatomical and physiological parameters of the individual, such as organ volumes, tissue composition, and blood-flow rates. The gastrointestinal compartment in the whole-body PBPK model is further divided as described by the advanced compartmental absorption and transit model. 21 The inclusion of gut metabolism, efflux, and influx transport in the gut compartments enables the prediction of the gut bioavailability of compounds.
For the purpose of this analysis, we use the PBPK model implemented for adult and pediatric populations in Simcyp version 17. PBPK workflow overview. Pediatric PK parameters are typically predicted using a pediatric PBPK model that has been developed using an adult PBPK model and clinical PK data. Workflows for pediatric PBPK model development with i. v. and oral adult PK data are shown in Figures  2  and 3. Intrinsic CL is obtained from the CL observed in adult PK studies (preferably i. v. ) using the retrograde calculator in Simcyp. Simple allometry. Pediatric CL is estimated knowing the adult CL using body size (mg/kg) according to the following equations  22  : Allometry with ADEs. Allometry with ADEs used the following ADEs  6, 20  : Preterm Term 0. 25-2 years 2-5 years > 5 years 1. 2 1.
SUMMARY AND CONCLUSION For children > 2 years of age, the performance of all methods evaluated (PBPK, allometry, and popPK) are comparable. However, for children aged < 2 years, the adult to pediatric extrapolation with PBPK is superior to that with simple allometry and comparable to allometry with ADEs. Adult to pediatric extrapolation with either PBPK or allometry with ADEs is superior to pediatric extrapolation using a pediatric popPK model. However, pediatric interpolation with pediatric popPK provides better predictions in the individual pediatric subpopulations. These results support the use of adult to pediatric extrapolation by either PBPK or allometry with ADEs to select starting dose and to design the confirmatory studies in children, the sparse data from which can be used for building a popPK model to predict exposure in different subpopulations of children < 2 years of age with better precision.
Acknowledgments. The authors acknowledge the leadership of  Quantitative Pharmacology & Drug Disposition, Merck KGaA  for providing the framework and technical support of this research. Christian Lüpfert kindly supported the thorough review of the physiologically-based pharmacokinetic simulations and calculations and  Faiza Rharbaoui  kindly provided useful discussions. Funding. The research was funded by  Merck KGaA, Darmstadt, Germany.
S1 ). For this gene, the oligonucleotides included the ATG and stop codons. R. bifida cDNA fragment (744 bp) was cloned and sequenced from several clones providing a unique full-length R. bifida N4OMT cDNA sequence. Our study demonstrates that a single N4OMT gene is expressed in R. bifida bulbs. The putative R. bifida N4OMT gene coding sequence shows respectively 93, 92, 92, 92 and 93% identity with the coding sequences of the NpN4OMT1 (KJ584561. 1), NpN4OMT2 (KJ584562. 1), NpN4OMT3 (KJ584563. 1), NpN4OMT4 (KJ584564. 1) and NpN4OMT5 (KJ584565. 1) genes encoding the N. aff. pseudonarcissus N4OMT protein  27  ; Online Resource, Fig. S1 ). The 239-amino acid R. bifida protein shows 92 to 93% identity to the Pseudonarcissus protein. Thus, the gene we cloned here represents the R. bifida Norbelladine 4′-O-Methyltransferase ortholog (RbN4OMT; GenBank: MH765575).
The CYP96T enzyme catalyzes the transformation of 4′-O-methylnorbelladine to noroxomaritidine, leading to the formation of noroxomaritidine derivatives, such as haemanthamine  18  and montanine. To identify the CYP96T gene ortholog in R. bifida, we also aimed at designing oligonucleotides based on conserved regions of the Pseudonarcissus CYP96T1 gene. The coding sequence of this gene was 1539 bp long and encoded a 513 amino acids protein  18. However, the N. pseudonarcissus highly homologous sequences corresponding to CYP96T3 (AMO65743. 1), CYP96T2 (AMO65742. 1) and Noroxomaritidine synthase Cytochrome P450 96T1 (A0A140IL90. 1) present in the Genebank did not allow defining conserved regions. Using genomic data from Lycoris, we found a partial genomic sequence homologous to the Pseudonarcissus CYP96T genes covering 415 bp of the 3′ part of the coding sequence.
Using a combination of oligonucleotides specific of the CYP96T genes including the Lycoris sequence, we succeeded to amplify a Rhodophiala cDNA sequence covering 384 bp (166 aa) of the putative R. bifida CYP96T sequence. This sequence covered 25% of the 3′ part of the Pseudonarcissus CYP96T1 coding sequence and showed 85% of nucleotide identity and 84% of identity (93% similarity) at the protein level relative to Pseudonarcissus CYP96T (AMO65743 18 ) (Online Resource, Fig. S2 ). The partial sequence corresponding to a putative para-para'C-C phenol coupling cytochrome P450 in R. bifida was named RbCYP96T (GenBank: MH765576). RbCYP96T and RbN4OMT gene expression in R. bifida. Studies initiated in the 80's showed that alkaloids may influence plant growth as growth stimulators and regulators. However, the most important function of AmAl is the protection of plant cells from physical stresses such as UV-light, heat, pathogens and herbivores.
Gene expression analysis. Total RNA extractions and qRT-PCR reactions were performed as described by Berrabah and collaborators  54. Primer efficiencies were calculated with LinReg PCR: Analysis of Real-Time PCR Data, version 11. 1. RbN4OMT and RbCYP96T gene expression were normalized, using RbACTIN as reference gene. These primers specifically designed to monitor R. bifida ACTIN gene expression were TCCATCATGAAGTGTGATGTTGATAT as forward primer and CCTCCAATCCAGACACTGTACTT as reverse primer. To measure the expression of the RbN4OMT and RbCYP96T genes, the primers used were CTGCTTGGAATTCAAGCCCGA as forward primer and ATGCCCCTGGACCATCCGAA as reverse primer. For the RbN4OMT CGTCAGGGTTGGAGGAGCGAT (forward primer) and ATCGCTCCTCCAACCCTGACG (reverse primer) were used. R. bifida cDNA Cloning. All the primers were designed based on CYP96T1 and N4OMT Amaryllidaceae sequences conserved at the amino acids and nucleotides level in publicly available sequences (NCBI; GenBank  55  ).
Acknowledgements The authors thank  Programa CYTED  ( 416RT0511 ) for financial support,  CAPES  and  CNPq. LRT is thankful to  CAPES -Brasil  for a doctoral fellowship (Processo No  13553135 ). JASZ acknowledges  CNPq (Brazil)  for a research fellowship. The authors thank  Dr. Marie Garmier  for careful reading and editing of the manuscript.