dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION We conducted a systematic analysis of all sources available (i. e. , pharmacovigilance databases, a clinical trial, and real-world data) to assess the safety of inulin and sinistrin. To our knowledge, this is the first time that such an evaluation has been performed. In this retrospective analysis of 28 years of inulin and sinistrin use in France, the annual number of AR registered in the pharmacovigilance system varied from 0 to 15 cases with a reporting rate of ARs range from 0 to 2. 54 cases per 10 million inhabitants. This reporting rate was low and may underestimate the true rate due to the expected underreporting of ARs. The low reporting rate may also be a reflection of the limited use of inulin and sinistrin: Only a few centers in France use these products for the measurement of GFR.
We thus did not come to the same conclusion as Bacchetta et al. who found that "hypersensitivity to inulin is not serious"  (Bacchetta et al. , 2008b). These apparently contradictory results highlight the determining role of production in the occurrence of AR to drugs and diagnostic reagents. A limitation of this study was the use of pharmacovigilance databases. These resources can be biased due to underreporting of ARs. Further, certain cases were poorly documented in the FPVD. A strength is that in comparison to other studies, we analyzed a larger number of AR cases. Whereas most previous studies of the tolerance of inulin and sinistrin were case reports, our study is the largest study on this subject so far and is based on different sources, not only on spontaneous reporting in pharmacovigilance databases but also data from both clinical trial and observational cohorts, and thus more accurately assessed the hypersensitivity to inulin and sinistrin.
Morphology SEM images of optimised formulae B-PLN4 represents spherical shapes and with a rough surface. Rough surfaced and agglomerated particles could be seen, probably due to the melting of lipid matrix as stearin melts around 72 • C [52] (Figure  5 ). The size of the particles was approximately identical as measured by the DLS technique. lease simultaneously by diffusion of water into the matrix, swelling of matrix and dissolution of matrix. Additionally, the release data were fitted to the Korsmeyer-Peppas model at a dose of 100% drug release showed the release exponent n = 0. 3619. The n value less than 0. 43 in the spherical encapsulation shape indicates a Fickian diffusion release of the drug  [47] [48] [49] [50]. The initial burst release followed by sustained release of drug during dissolution also supports the goodness of Korsmeyer-Peppas kinetic models  [51].
Fourier Transform Infrared (FTIR) Studies FTIR spectra of pure BTB, PLGA, tristearin, SL and their B-PLNs (B-PLN1-B-PLN4) were recorded using "FTIR spectrometer (Jasco FTIR Spectrophotometer, Tokyo, Japan)". For the preparation of the sample, each sample was diluted with potassium bromide (KBr) crystal (1:10, w/w) to prepare pellets. FTIR spectra were recorded in the range of 4000 to 1000 cm -1 , and peaks were interpreted using "spectral manager" software.
The animals were fasted overnight and the blood samples were collected in pre-heparinised tube was at a fixed time span (0, 0. 25, 0. 5, 1, 2, 4, 8, 12 and 24 h) after administration of respective formations. All blood samples were centrifuged at 4500× g for 5 min to separate the plasma and all the samples were safely stored in a deep freezer (80 ± 10 • C) until analysis by UPLC-MS/MS. The non-compartmental pharmacokinetic model was selected to calculate the different pharmacokinetic parameters using "WinNonlin software, Pharsight Co. , Mountain View, CA, USA". All the results were presented as mean ± standard deviation (SD). The parameters; peak plasma concentration (C max ), time to reach peak concentration (T max ), area under cure (AUC) [(AUC 0-24 ) (AUC 0-∞ )], elimination half-life (T 1  2 ) and rate constant (kz), mean residence time (MRT) were calculated.
DRPs and PIs data During medication order review, pharmacists assessed the appropriateness of each prescribed medicine, according to their expertise, and looked for possible drug interactions. Pharmacists had access to electronic medical records, biology, and drug prescriptions. Any identified relevant DRPs lead to a PI, which was submitted to the physician. Pharmacist could be a senior or a resident managed by a senior pharmacist. The type of DRPs and PIs was classified according to the SFPC (Société francaise de pharmacie clinique -The French society of clinical pharmacy) criteria using the report form developed and validated for routine documentation of the PIs  [18, 19]. Whenever a DRP was identified, the pharmacist could record their PIs in the Act-IP© database through the online report form (http://actip.
Data analysis Data were retrospectively retrieved from the Act-IP© database and split into 2 groups: patients aged 18-74 years (younger patients) and patients aged ≥ 75 years (older patients). Database quality controls were performed looking for aberrant entries to ensure that data coding and entry errors were minimal. Continuous variables were expressed as means ± standard deviation. Categorical variables were expressed in frequencies and percentages. Descriptive statistics were used for characterising population characteristics, DRPs, PIs and physicians' acceptance. Top 20 of the most frequent DRPs and physicians' acceptance in both older and younger were examined and compared. DRPs and physicians' acceptance proportions between older patients treated in a dedicated geriatric ward or in a general ward were also compared.
Discussion This study was conducted in a multi-centre teaching hospital over an 8-year period. It aimed at quantifying and describing DRPs and PIs among older patients and at giving an insight into the clinical contexts and medications involved in DRPs. DRPs and PIs between younger and older patients were also compared to identify DRPs that are specific to older patients. The description of DRPs in geriatrics allows PIs to be targeted and allows clinical pharmacy activities to be adapted to geriatrics specificities. A substantial number of DRPs was collected with 56,223 DRPs recorded in the database. In older patients, 19,056 DRPs were recorded and the most frequent DRPs was 'supratherapeutic dosage' (22. 4%). Accordingly, the main PI was 'dose adjustment' (27. 4%). These rates were consistent with Ruiz-Milo et al.
A supratherapeutic dosage of hypnotics, vitamin D supplementation and the misuse of long half-life benzodiazepines were mostly described. Fig. 1  also showed a misuse in hydroxyzine mostly before 2013 when the 100-mg tablets were still marketed. However, in this study, a choice was made to associate a problem with a drug to classify DRP. In this context, a same specific geriatric DRP can be coded in different ways in the Act-IP database according to pharmacists' practices. For example, hydroxyzine is not recommended in older patients due to anticholinergic and sedative effect. This can be coded either in ''Nonconformity to guidelines/contra-indication'' or ''Adverse drug reaction'' or ''Drug without indication''. Guidelines that are more detailed should be written to ensure better coding and to allow more precise analyses of the database.
In the context of this study, focus was not only on the drugs or the problems, but also on the association of both. This allowed bettering understanding of the rationale behind each PI. However, it was not possible to understand rationale behind physicians' refusal of PIs because this information is not collected in the database. It would have been crucial to confirm the relevance of PIs and adjust pharmaceutical recommendations. Lastly, the clinical significance of PIs could not be assessed. However, other similar studies showed clinical significance in older patients in 55,2% to 75. 3% of the PIs  [44, 45]. Identifying main DRPs associated to drugs is a helpful way to point out targeted actions, such as internal audit on specific prescriptions or drug use. A recent audit on the proper use of fluoroquinolones has been recently conducted in geriatric units  [46].
Data collection Data related to patients' characteristics and treatments before hospitalisation, patients' pharmacists and general practitioners (GPs), as well as drug prescriptions made upon hospital discharge were collected using hospital computerised patient medical records. Information on ambulatory drug prescriptions over a 3-month period of time following hospital discharge was gathered through patients' pharmacists: the latter were requested to forward all anonymized prescription forms (with an identification number) from hospital discharge. Pharmacists were also questioned on events occurring within the 3 months following hospital discharge such as readmission or death. Reasons for drug changes were collected by interviewing patients' general practitioners.
Re-hospitalisations rates were congruent with data reported in existing literature  [39, 40, 43]. To limit information bias, our data came from collected drug prescription forms. This enabled a detailed and reliable assessment of drug prescription evolutions. Our appraisal, however, did not take into account real pharmacist delivery and patients' drug consumption. Some prescriptions may have been missed when submitted to a pharmacy other than the usual one. This risk was limited by contacting pharmacies close by, whenever forms seemed to be missing and by questioning GPs when significant change was observed. Memory and social desirability may have possibly influenced drug changes reasons given by GPs. As few of them refused to participate, the majority of observed changes were elicited, and GPs were questioned by a fellow GP to ensure optimal trust-worthy conditions and confidentiality, through a series of open-ended questions.
Sirius red staining Fresh hepatic tissues were xed in 4% paraformaldehyde for 24 h, and then embedded with para n. The 4 µm liver sections were prepared and stained with Sirius red to semi-quantify the extent of collagen deposition, which demonstrates the severity of liver brosis. The collagen deposition area of Sj single egg granuloma as displayed in deep red was measured and analyzed by Image J software. Each section was evaluated in double-blind fashion by two independent researchers.
Immunohistochemical (IHC) assay Endogenous peroxidase in mouse liver sections was blocked with 3% hydrogen peroxide (H 2 O 2 ). Immunohistochemical (IHC) staining assay was used to determine the expression level and location of α-SMA and Col I in the mice liver tissue using anti-α-SMA (1:200) and anti-Col I (1:400) primary antibodies, followed by HRP-conjugated secondary anti-rabbit or anti-mouse antibodies. The images were observed and captured with an optical microscope equipped with a camera (Olympus, Tokyo, Japan). The semiquantitative analysis was determined by image J software  [27]. Cell culture and treatment A human HSCs line, LX-2 was cultured in DMEM medium with 10% fetal bovine serum (FBS). When the con uence reached 80-90%, LX-2 cells were exposed to LPS or syntheticPGE2, or these cells were pretreated by TAK242 for 30 minutes or NS398 for 15 minutes at the indicated concentrations prior to stimulation with LPS.
METHODS As part of a randomised, cross-over PK study of alternative routes of TXA administration in healthy volunteers conducted at the Clinical Investigation Centre of Necker Hospital in Paris, we assessed the accuracy of TXA concentrations collected using volumetric absorptive micro-sampling devices. The study was approved by the London School of Hygiene & Tropical Medicine ethics committee (16286) and the Comité de Protection des Personnes Île de France III  (2019-000285-38)  and registered in the  EudraCT (2019-000285-38)  and ClinicalTrials. gov (NCT03777488) databases. The study methods are described in detail elsewhere  (Grassin-Delyle et al. , 2021a). Briefly, adult volunteers (non-pregnant women and men) aged between 18 and 45 years received TXA by three routes (1 g intravenous, 1 g intramuscular, 2 g oral) on three separate days with a minimum washout period of 48 h between each treatment.
In order to better explain unclear hypoglycemic events such as in the context of Münchausen or Münchausen by proxy syndromes or in situations of incapacitation, hair analysis seems to be a very useful to complement blood and urine since it gives retrospective data on drug exposure. In this study, metformin was tested for the first time in the hair of two subjects deceased from fatal metformin overdose, using a new GC-MS/MS specific method.
Results and Discussion The groups of the studied compounds are presented in Table  1. The descriptors of the molecular structures available in Molecular Operating Environment (MOE), v. 2019. 0102 (Chemical Computing Group, https://www. chemcomp. com/) were calculated and analyzed. Differences in the descriptor means among the three groups of compounds were evaluated with ANOVA. Principal component analysis (PCA) was used to extract information from the calculated descriptors; cluster analysis was applied to the chemical compounds. ROCS similarity search between the compound groups was performed. The interactions of the three groups of compounds with SULTs were further analyzed by docking to three alloforms of SULT1A1, the crystallographic structures of which were retrieved from the Protein Data Bank (PDB, https://www.
Compounds Collection for the In-House Library of Natural SULT Ligands The search in the scientific literature  [15,  for natural compounds reported as ligands of SULTs led to the creation of an in-house library of 118 structures. It is freely available at http://biomed. bas. bg/qsarmm/. Sixty-two compounds were reported as substrates, 53 compounds were inhibitors of SULT1A1, and for 3 compounds, both substrate and inhibitory kinetic parameters were available. Most of the compounds were also reported to be substrates or inhibitors of other SULT isoforms, as listed in the database. The library of collected natural SULT ligands contains the following information:.
Docking Results Next, we performed docking of all 118 compounds into different SULT1A1 conformations, using MOE software. We applied two scoring functions, Alpha HB and London dG with different terms of the binding energies calculations (see Section 3 for details). Figure  5  illustrates the docking scores (approximating the binding energies) obtained for the natural SULT ligands from their docking in eight SULT1A1 structures: four crystallographic (with PDB IDs: 4GRA for SULT1A1*1, 1LS6 and 2D06 for SULT1A1*2, and 1Z28 for SULT1A1*3) and four conformations generated from Molecular Dynamics Simulations (MD) (see Section 3 for details). Alpha HB and London dG scoring functions are applied, and the lowest scores of 30 docking poses for each compound (indicating better binding affinity and stronger interactions with the enzymes) are plotted.
Protein Targets Eleven crystallographic structures of SULT1A1 available in Protein Data Bank (PDB, https://www. rcsb. org, accessed on 16 2021) were downloaded and analyzed. Three alloforms, SULT1A1*1, SULT1A1*2, and SULT1A1*3 were considered. The crystallographic structures with PDB ID 4GRA for SULT1A1*1 and PDB ID 1Z28 for SULT1A1*3, crystallized in their apoforms, were selected as the only representatives of these alloforms in PDB. Among the nine structures available for SULT1A1*2, three are co-crystallized with Pnitrophenol (1LS6 is co-crystallized with two molecules of P-nitrophenol), another three with 7-hydroxy-2-oxo-2H-chromene-3-carbonitrile (3QV), one with estradiol (EST), one with naphthalen-2-ol (O3V), and one is in its apoform. Based on the analysis of proteinligand interactions in crystallographic structures, as well as the placement of loop Phe81-Ser91, which is important for the molecular clamp mechanism in SULT1A1, the PDB IDs 1LS6 co-crystallized with two molecules of P-Nitrophenol (NPO) and 2D06 co-crystallized with estradiol (EST) were selected for further consideration.
Datasets Preparation A library of natural compounds known to be ligands of SULTs was assembled. The library consists of 118 structures collected from the scientific literature  [15,  and is described in detail in the Results section. A library of 102 synthetic SULT1A1 ligands was created with compounds collected from Refs. [14, 15, 39, 42, 54, 56, 59, 63, 71]. A library with 1220 other natural products was taken from Ref. [72]. The 118 natural SULT ligands were not present in this library.
Chemical Structures Preparation and Calculation of Molecular Descriptors In this study, the 3D structures of chemical compounds were built using MOE software v. 2019. 0102. The molecules were minimized with the MMFF94x force field. The molecular descriptors available in MOE were calculated (322 descriptors). They include number of atoms or chemical groups of a given type, electronic, steric, hydrophobic descriptors, shape connectivity indices, and semi-empirical quantum mechanical electronic descriptors. The semi-empirical PM3 Hamiltonian was used for calculation of the quantum mechanical descriptors. Descriptors without variance in the compound sets were removed from the subsequent analysis.
Molecular Dynamics Molecular dynamics (MD) simulations were performed on three alloforms of SULT1A1 in presence of PAP, starting from PDB IDs: 4GRA (apo), 2D06 (holo with bound EST), 1LS6 (apo), and 1Z28 (apo) to ensure a broader conformational exploration of the SULT1A1 structure for subsequent docking simulations. MD simulations were performed with NAMD 2. 11  [73]  with the Charmm36 force field  [74]. Each system underwent a 10,000-step minimization. The system was then gradually heated from 0 K to 310 K with 1000-step/K in an NVT simulation. Then, equilibration of 2 ns was performed. Finally, a 40 ns production run was performed with an NPT simulation. Three independent MD simulations at three different initial velocity conditions were executed for each system. Finally, structural clustering was done over the conformational space generated for each of the studied systems including the binding pocket with an RMSD cutoff of 1.
ROCS Software ROCS software v. 3. 3. 0. 3 (OpenEye Scientific Software, Inc. , Santa Fe, NM, USA https://www. eyesopen. com) was used to identify chemically similar compounds. The procedure included aligning and scoring the dataset of molecular structures of the other natural products to template molecular structures of the group of SULT ligands. The Tanimoto-Combo score (shape + "color"), which combines the shape and chemical features of the aligned datasets, was used to score the similarity. The score varies from 0 (non-similar compounds) to 2 (very similar compounds).
The two groups of compounds were similar in relation to the number of H-bond donor atoms, logP(o/w), ASA+, and the ASA_P, but some properties differed (size, number of rings, H-bond acceptor properties, ASA-, and ASA_H), also confirmed by the grouping observed in the cluster analysis. Combining chemical space analysis and docking of natural SULT ligands, flavonoids and similar compounds were outlined as the most favorable ligands for SULT1A1 in agreement with published data (e. g. , Ref. [75] ). Interestingly, the search for other natural products with similarities to the natural SULT ligands and with good predicted binding energies resulted in identification of new putative natural products interacting with SULT1A1. Further elucidation of these compounds would be necessary in order to validate them as inhibitors or substrates of SULT1A1.
Aims: To describe and compare the adherence to different direct oral anticoagulants (DOACs) in eight European databases representing six countries. Methods: Longitudinal drug utilization study of new users (≥18 years) of DOACs (dabigatran, rivaroxaban, apixaban) with a diagnosis of non-valvular atrial fibrillation (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). Adherence was examined by estimating persistence, switching, and discontinuation rates at 12 months. Primary non-adherence was estimated in BIFAP and SIDIAP databases. Results: The highest persistence rate was seen for apixaban in the CPRD database (81%) and the lowest for dabigatran in the Mondriaan database (22%). The switching rate for all DOACs ranged from 2. 4 to 13. 1% (Mondriaan and EGB databases, respectively). Dabigatran had the highest switching rate from 5.
0 to 20. 0% (Mondriaan and EGB databases, respectively). The discontinuation rate for all DOACs ranged from 16. 0 to 63. 9% (CPRD and Bavarian CD databases, respectively). Dabigatran had the highest rate of discontinuers, except in the Bavarian CD and AOK NORDWEST databases, ranging from 23. 2 to 64. 6% (CPRD and Mondriaan databases, respectively). Combined primary non-adherence for examined DOACs was 11. 1% in BIFAP and 14. 0% in SIDIAP.
, 2016; Martinez et al. , 2016; Simons et al. , 2016). A cohort study in the Clinical Practice Research Datalink (CPRD) database, showed that persistence at 1 year was significantly higher for DOACs (79. 2%) than for VKA (63. 6%), and that this alone could lead to fewer cardioembolic strokes  (Martinez et al. , 2016)  as was seen in another Sweden observational study where the DOACs' persistence was 70% and non-persistence patients had an OR 2 of stroke and transient ischemic attack  (Komen et al. , 2020). However, in another study performed on the CPRD, no evidence of a difference in nonpersistence between VKA and apixaban [HR 0. 92 (95% CI 0. 68 ti 1. 23)] was found, and non-persistence was higher with dabigatran  [HR 1. 67 (1. 20 to 2. 32) ] and rivaroxaban  [HR 1. 41 (1. 02 to 1. 93) ] than apixaban  (Johnson et al.
, 2016). Other studies performed in claims databases  (Coleman et al. , 2016; Simons et al. , 2016)  or administrative registries  (Forslund et al. , 2016)  report on differences in adherence for patients using different DOACs. Users of rivaroxaban had a higher persistence and a lower rate of discontinuation at 2 years, compared with those using dabigatran and warfarin  (Coleman et al. , 2016; ; Forslund et al. , 2016). Differences have been found in the hazard ratios for risk of discontinuation among the different types of DOACs with a higher persistence with warfarin and apixaban than with dabigatran or rivaroxaban [rivaroxaban vs apixaban,   et al. , 2018). And for one performed with the SFK, nonpersistence at 1 year to DOAC was 34% higher compared to VKA (22%)  (Zielinski et al. , 2019).
Setting and Study Population A longitudinal drug utilization study of new users (≥18 years) of DOACs (dabigatran, rivaroxaban, apixaban; ATC codes: B01AE07, B01AF01, and B01AF02, respectively) with a diagnosis of NVAF (see codes in Supplementary Table  S1 ) was conducted between January 2008 and December 2015 in eight data sources from six European countries (Table  1 ). A common protocol was applied for data extraction and analysis for each database (EU PASS Register No: 16,014) (EU PAS Register, 2021). New users were defined as patients ≥18 years initiating a DOAC with a diagnosis of NVAF during the study period, without any use of DOACs for at least 12 months prior to the index date (the day of the first DOAC prescription for each patient during the study period). Patients with previous use of VKA were included.
Data Sources Data were retrieved from the following eight databases: 1) the Dutch Mondriaan project, which includes the Julius General Practitioner Network (JHN) database  (Smeets et al. , 2018) ; 2) the Danish National Registries (DNR), which includes the Danish National Patient Register, Danish National Prescription Registry, and Danish Civil Registration System  (Schmidt et al. , 2015; Kildemoes et al. , 2011; Schmidt et al. , 2014) ; 3) the AOK NORDWEST database, Germany  (Jaunzeme et al. , 2013;  ,  Hoffmann and Koller, 2015) ; 4) the Bavarian Association of Statutory Health Insurance Physicians database, Germany, referred to here as Bavarian CD,  (Mehring et al. , 2017) ; 5) the 'base de datos para la Investigación Farmacoepidemiológica en Atención Primaria' (BIFAP),  Spain (de Abajo et al.