dataesr/scientific-paragraphs-categorization huggingface dataset:
Evidence before this study We systematically searched Pubmed database from inception to January 2019, for articles on the effects and safety of statins in patients with cirrhosis. Among 180 records, a total of 9 studies were identified, including four randomized controlled trials (RCTs) and five cohort studies. In 3 of the RCTs, statin therapy was associated with a decrease in portal pressure and no serious adverse events were observed. In the largest RCT reported to date, treatment with simvastatin 40mg/day was associated with a significant reduction in mortality compared to placebo, but with an unexpectedly high rate of rhabdomyolysis. A search focused on rifaximin safety in liver cirrhosis was done for the same time period. Among 23 records, a total of 4 placebo-controlled clinical trials specifically designed to investigate rifaximin safety in patients with cirrhosis were identified.
CONTRIBUTORS PG, EP, ML, and ES conceived the idea of the study, designed the protocol, and supervised study execution. LN, AA, DC, CJ, SP, OR, FU, KW, GZ, CA, PA, MB, UB, PC, FD, RM JT, VV, and MC identified candidates to the study, enrolled and treated patients, acquired data and collaborated in the design of the protocol. JP and JF provided administrative and monitoring support throughout the whole study period. GD and FT designed and performed the statistical analysis. EP drafted the manuscript. PG directly reviewed the manuscript. PK, JGA, and RA gave advice for the design and development of the study and contributed to the interpretation of the results and intellectual content of the manuscript. All authors provided critical revision of the manuscript for important intellectual content and approved the final draft of the manuscript for submission.
The study was approved by regulatory Agencies of the six countries involved and by the Institutional Review Board of each participating center. Written informed consent was obtained from all patients. The study was monitored by the clinical trials units of the European Clinical Research Infrastructure Network (ECRIN, Paris, France) and the statistical analysis was performed by the Medical Statistics Core Facility of the Institut d'Investigacions Biomèdiques August Pi-Sunyer (IDIBAPS, Barcelona, Spain). The study was registered at European Union Clinical Trials Register (EudraCT Number 2016-004499-23) and at Clinicaltrials. gov (NCT 03150459).
This the dehydration of hydrophobic POP blocks, which represents the very first hen the polymer concentration is high enough, the micellization is followed unlike some empirical data from the literature  [61] , we experimentally formulations based on the sole P407 could not lead to a Tgel compatible with a not shown). According to information provided by the supplier, the 07 has recently evolved, thus modifying the average molecular weight of the and resulting in a direct modification of the gelation temperatures. Hence, to l temperature of P407 hydrogel, P188 was added in defined proportions. ixtures of P407, P188, and water were tested. The gelation temperatures and formulations were plotted on ternary diagrams (Figure  1 ). For all the eptable (optimum pH for nasal rote is 6 ± 3). Increase of P407 relative content perature decrease, and to higher osmolarity values.
Model consent form and other related documentation given to participants and authorized surrogates {32} The Supplementary Material le contains the French informed consent form that the patient signs prior to the inclusion in the trial (version 2, updated July 1, 2017) Additional consent provisions for collections and use of participant data and biological specimens {26b} Biological samples for bacteria strain will be collected, stored and kept if the patient signed the biocollection informed consent. This biocollection and consent procedure have been registered under number DC-2011-1399 to French Ethic Committee: CPP Ouest IV.
Sequence generation {16a} and Concealment mechanisms {16b} This balanced block randomization is computer-generated. Subjects are randomized into blocks as the allocation progresses, a block being a subgroup of predetermined size within which there is a random allocation of patients. The software used for the randomization is SAS version 9. 4. The randomization will be centralized and strati ed based on the 4 groups of contraception following: The randomization key is known to biostatistician and data managers, to make it impossible for the investigator to assign a particular position for the dressings. At the randomization visit (Day0) the investigator after checking the inclusion and non-inclusion criteria will validate the randomization on the eCRF. Drugs prescription for 2 months will be done (blinded spironolactone/doxycycline).
Data management {19} An electronic Case Report Form (eCRF) shall be drawn up for each subject. The identi cation of the subject will be performed using the rst letter of the family name, the rst letter of the rst name, the center number and the inclusion number, supplemented by the month and year of birth. This code should be the only information featuring in the eCRF enabling a retrospective link with the patient. All secondary objectives are expressed as a percentage or a status at a xed time of follow-up. General Linear Mixed models will be planned to take into account the repeated measurements collected on the same subject during their follow-up. Cox model with time dependent covariates can also be used for secondary objectives for which a status is considered. Signi cance will be set at 5% in a bilateral situation.
A sensitivity analysis will be performed in order to assess robustness of the ICER result. Methods in analysis to handle protocol non-adherence and any statistical methods to missing data {20c} All subjects receive the assigned study treatment until unacceptable toxicity, death, lost to follow-up, withdrawal of consent, or termination of the study by the sponsor, in accordance with local standard of care. Unacceptable toxicity and death will be considered as failure for the primary criteria analysis. Lost to follow-up, missing data for the primary criteria and early termination of the study by the sponsor will be also considered as failure by the intent-to-treat modi ed (ITTm) and not replaced. Patients who did not reach the inclusion criteria after randomization and those who never began their treatment will be replaced.
For the clinical secondary criteria analysis, patients with missing data will be withdrawn. Missing data in cost-effectiveness analysis will be imputed according the relevant method, depending on the type of missing data. If a patient is released from the study (e. g. in the event of Adverse Reactions (AR) preventing the continuation of the study), her data will not be collected, except for safety data (follow-up of AR or onset of AR associated with the experimental treatment, based on pharmacokinetics data). Plans to give access to the full protocol, participant level-data and statistical code {31c} According to French law, the results of the study will be published on the website of the regulatory authority. But the data-sharing is prohibited by the General Data Protection Regulation European law.
Frequency and plans for auditing trial conduct An inspection or audit may take place as part of this study, performed by the sponsor or/and by the regulatory authorities. Inspectors will check the documents, logistics, records and any other resources that the authorities consider to be associated with the clinical trial and that may be located at the trial site itself. Plans for seeking research ethics committee/institutional review board approval {24} Statement of who will have access to the nal trial dataset, and disclosure of contractual agreements that limit such access for investigators {29} The investigators will share the entirety the nal trial dataset,.
Oversight and monitoringComposition of the coordinating center and trial steering {5d} It been possible to carry out the and the trial thanks to an Executive Committee which includes a Scienti c Committee and a Steering Committee. The Scienti c Committee was created and coordinated by Prof. B. Dreno. Its membership comprises, biostatistician and methodologist, the study coordinator, the health economist and the project manager of the clinical investigation center (CIC1413). The Steering Committee is composed of the members of the Scienti c Committee with the addition of the data management team, the nurse study who coordinates assistance for patient inclusion in the other centers, and the monitoring Clinical Research Assistant (CRA). The sponsor project manager coordinates this committee and drafts the "FASCE newsletter" which provides, among other things, the latest news on patient inclusion, amendments to the protocol, etc.
Over the past twenty years, psychotropic drug abuse by young people, especially by teenagers, has received special attention. Here, we present the case of baclofen overdose in a 16-year-old male who recreationally, and probably recurrently, self-administered baclofen. In addition, a review of other cases was conducted. The 16-year-old boy presented to emergency department with digestive signs followed by agitated confusion. Detection and determination of baclofen concentration were achieved using liquid chromatography tandem mass spectrometry. Baclofen was detected in plasma and urine, at 420 ng/mL and 64 900 ng/mL respectively. Further, an English exhaustive literature search was performed using several different scientific databases without any limiting period in order to identify scientific articles dealing with baclofen overdose following a recreational use among adolescent and young adults.
After mixing and centrifugation, the organic layer was isolated and mixed with 100 µl of hydrochloric acid (0. 02 M). Following vortex mixing and centrifugation, the aqueous layer was kept for injection into the chromatographic system (1100 HPLC system, Agilent Technologies, Les Ulis, France). For both preparation procedures, the injection volume was 10 µL. The chromatographic separation was achieved over 20 minutes run time on an Uptisphere C18 ODB column (5µm, 100x 2. 1 mm (Agilent Technologies, Les Ulis, France)). Data acquisition was performed at three wavelengths (210 nm, 230 nm and 254 nm). Products were identified by comparing UV spectra and retention times to a homemade spectrum library (established using authentic standards). For GC-MS, 50 µL of internal standard (3-(5, 11-dihydro-10, 10-dioxo-11-methyldibenzo[c,f]-thiazepinyloxy) tropane maleate or RN927, a non-commercialized antihistaminic molecule, 1 mg/mL; final concentration, 50 mg/L) was added to 1 mL of urine sample then extracted using 5 mL of dichloromethane under alkaline conditions (500 µL of carbonate 1 M buffer pH 9.
Separation was achieved using a Kinetex C18 core-shell column (2. 1 x 50 mm); 2. 6 µm (Phenomenex SAS, Le Pecq, France). Mobile phase A was composed of ammonium formate buffer (2 mM in 0. 2 % formic acid) and deionized water while mobile phase B included ammonium formate buffer (2 mM in 0. 2 % formic acid) and acetonitrile. Chromatographic separation was performed at a column temperature of 50°C and a flow rate of 0. 40 mL/min. The injection volume was 10 µL and the run time was 5 minutes. Compounds were detected and quantified by an API 4000 QTRAP mass spectrometer (ABSciex, Les Ulis, France) equipped with an electrospray ionization (ESI) source. Data were collected in multiple-reaction monitoring mode (MRM). Protonated molecular ions [M + H] + of baclofen (m/z 214. 1) and baclofen-d4 (m/z 218.
Review of the literature An English exhaustive literature search was performed using several different electronic databases: PubMed, Scopus, Web of Science and Google Scholar, without any limiting period. The search was carried out using the terms: "baclofen", "overdose", "poisoning", "abuse", "misuse", "adolescent", "young adults", combining them in an appropriate manner. The goal was to identify all available scientific articles dealing with baclofen overdose following a recreational use among adolescent and young adults.
, 2010). Nevertheless, most of these studies do not explore potential mechanism of actions linking a drug to an ADE. Today, with the acquisition of knowledge in drug-induced toxicities and ADRs through high throughput studies (LINCS, ToxCast)  (Stathias et al. , 2020; Dix et al. , 2007)  and databases (SIDER, Drug Central, ChemProt)  (Ursu et al. , 2019; Kuhn et al. , 2016; Taboureau et al. , 2011) , development of innovative computational methods is feasible to decipher and predict biological targets associated to toxicity and ADE  (Ciallella and Zhu, 2019; Audouze et al. , 2010; Taboureau and Audouze, 2017; Hodos et al. , 2016). For example,  Bender et al. have developed a model to predict potential ADR(s) related to targets for drugs that are still on the market  (Bender et al. , 2007).
Data set for model development Drugs and their corresponding ADEs information were extracted from the DrugCentral V5. 1. 2 database (last access as of March 10, 2020)  (Drug Central, n. d. ). This database contains multiple types of data related to drugs (bioactivity profiles, chemical properties, clinical indication, pharmaceutical formulation, pharmaceutical exposure, …). Since 2018, the ADE data source Adverse Event Reporting System (FAERS) is included into the DrugCentral database  (Ursu et al. , 2019). FAERS was established in 1968 by Food and Drug Administration (FDA) to collect the ADE and medication error reports associated to the drugs on the market. The objective of FAERS is to support the FDA's post-marketing safety surveillance program for drugs and therapeutic biologic products  (openFDA, n.
d. ). In order to evaluate the associations between drugs and ADEs with disproportionally high reporting rates, the Likelihood Ratio Test (LRT) for safety signal detection method has been proposed by  Huang et al. (Huang et al. , 2011). LRT demonstrated the good power and sensitivity for searching the significant connection between ADE and drug  (Huang et al. , 2011; Huang et al. , 2019). To create our model, the LRT values available in the DrugCentral database were considered. We only kept the drug-ADE associations with a LRT ratio ≥ 1 for the further analysis. The LRT ratio was calculated by following equation:.
Generating a high confidence ADE-ADE network The relevant drug-ADE associations collected from the DrugCentral database were used to create the ADE-ADE network. The ADE model was generated based on the previously published and validated approach, the protein-protein association network (P-PAN)  (Audouze et al. , 2010; Audouze et al. , 2014). The P-PAN was based under the assumption that nodes are linked to each other if they shared at least one component (i. e. proteins in the original model, ADEs in the present study). Here, the proposed network was constructed by representing each ADE with a node (for example the node 'Vomiting'). Then, each ADE was connected to another ADE to form ADE's pair, if at least one overlapping drug between both ADE was identified. Therefore, ADEs are associated together in the drug space.
Weighted score A weighted score (wS) was calculated for each ADE pair within the aim to select the most significant ADE-ADE associations, and to reduce noise in the model. The wS was calculated as the sum of weights for overlapping compounds between two ADEs, where weights were inversely proportional to the number of assigned ADE  (Audouze et al. , 2010; Audouze et al. , 2014). The associations between ADE were kept if two ADE share at least three drugs. Only the top significant ADE pairs were loaded into Cytoscape V3. 7. 1, an open sourced software platform for visualization of the complex interacting ADE network  (Saito et al. , 2012).
Data compilation ADE-drug associations and their corresponding LRT were downloaded from the DrugCentral database (v5. 1. 2, last access as of March 10, 2020). In total, we extracted 487,698 associations between 9998 unique ADE and 1524 unique drug. The maximum number of ADEs assigned to one drug 'Methotrexate' was 3598, and the maximum number of drugs associated with one ADE 'Vomiting' was 1044. To facilitate further analysis, we only kept the associations with LRT ratio≥1, reducing to 90,827 drug-Fig. 1. Workflow of the computational systems toxicology approach to predict adverse drug events (ADE) of drugs. Data: As a first step, drug-ADE associations were extracted from the DrugCentral database (http://drugcentral. org/ (accessed March 10, 2020). Model generation: An ADE-ADE network model was created based on the compiled data, in which two ADEs were connected if they shared at least one drug.
3a ). The most significant predicted ADE was 'Sudden_onset_of_sleep'. Exploring the literature with the PubMed database, this ADE has been reported in a study with patients treated with safinamide in 100 mg/day  (Fabbri et al. , 2015). Others ADEs were predicted for safinamide ('Compulsive_shopping', 'Compulsive_sexual_behaviour', 'Gambling_disorder' and 'Impulse-control_disorder', 'Hypersexuality', 'Obsessive-compulsive_disorder'), Fig. 2. Representation the top significant ADE-ADE associations based on the weighted score (wS). For clarity, only the top significant associations selected based on the weighted score (wS ≥ 2) are shown. Each node represents one unique ADE, colored by the organ system classification (SOC) to which it belongs. The size of the node is according to the number of drugs known to be linked to it.
Case study 2: sonidegib Sonidegib is a hedgehog signaling pathway inhibitor that plays an important role in the developmental process of embryo cells. This drug is used for treatment of basal cell carcinoma of skin, which is one type of malignant neoplasms  (Katoh and Katoh, 2005). According to the DrugCentral database, only one known ADE exists for this drug that is 'Febrile_neutropenia'. The developed ADE model was screened for 'Febrile_neutropenia', and among the 1750 potential ADEs, 20 ADEs had a significant score (Fig. 3b ) (Table  S3 ). Among them, no literature evidence between exposure to sonidegrib and ten predicted ADEs was found (Table  S3 ). The most significant predicted ADE was 'Nicotinic_acid_deficiency'. Nicotinic acid (also known as niacin) is a vitamin B3 used in prevention of basal cell carcinoma of skin  (Totonchy and Leffell, 2017).
Although there was no direct evidence to support this association sonidegib-niacin deficiency, this drug is known to be metabolized by the cytochrome CYP450 3A4, while niacin is an inhibitor of this enzyme  (Gaudineau and Auclair, 2004). The ADE 'Chronic lymphocytic leukaemia recurrent' was also predicted. A published study revealed that perturbation for Hedgehog signaling pathway activates cancer stem cells in myeloid leukaemia  (Zhao et al. , 2009) , leading potentially to 'Richter's_syndrome' Nervous system disorders Fig. 3. Visualization of the most significant ADEs predicted to be associated to safinamide (4a), sonidegib (4b) and rufinamide (4c). In each subnetwork, central nodes represent the known ADEs related to each drug (from the DrugCentral database). The predicted ADEs, using the developed ADE-ADE network model, are linked to each known ADE.
The major limitation of our integrative systems toxicology approach is that the ADE predictions are limited to the ones present in the ADE model. Hence, drug effects that may be reported in the literature or in FDA reports (such as hematuria for rufinamide, or more complex effect as retinal degeneration and loss of photoreceptor cells reported for safinamide), but not in the DrugCentral database, cannot be predicted by the model. Such gaps could be paved when more data with LRT information will be available from diverse data sources. In addition to the ADE predictions for drugs, the model allowed to point out another interesting issue, which is that some ADE occurrence is dose related. Some ADEs occurred depending to the posology such as "impulse behavior" for safinamide, 'pneumonia' for sonidegib and "severe seizure" for rufinamide.
d. ;  Wu and Peters, 2019). The different formulation (oral and IV) or the relation of an ADE to a prodrug, the active drug or metabolite are some other issues that may also cause different adverse effects  (Uchegbu and Florence, 1996; Ma'ayan et al. , 2007). So far, our ADE model does not differentiate between these points as our approach is limited to binary data (drugs associated or not with ADEs) compiled from the DrugCentral database. In the future, more specific ADE models could be developed when such diverse information will be accessible for a large set of drugs. Such integrative toxicological modeling developed for drug attrition and pharmacovigilance, may be applied to other research areas such as the regulatory arena of toxicology that does not always embrace the complexity of biology.
METHODS: Patient Identification and Characteristics: BioVU Samples: Eleven self-reported Caucasian cases for this retrospective study were identified using de-identified clinical data extracted from BioVU by Nashville Biosciences, a subsidiary of VUMC established to support translational research. Clinical and genotype data were provided from BioVU in accordance with BioVU's data use policies; subjects provided informed consent prior to their inclusion in BioVU  [8, 9]. Since infliximab is administered via infusion, highconfidence doses were found in the BioVU's structured clinical data by comparing infliximab prescription code dates with infusion Current Procedural Terminology (CPT) code dates, which were later confirmed via manual review of the de-identified clinical notes to provide true infliximab exposure timeframes and dosing intervals.
g. 6mercaptopurine) during the course of therapy. Potential cases meeting the above criteria were then evaluated for likelihood of infliximab-induced liver injury by a DILI expert (PBW)  [10]. Along with structured data describing clinical lab values, dosing intervals, and relevant International Classification of Diseases (ICD, 9 th and 10 th revisions) codes with dates, infliximab-induced liver injury was confirmed using de-identified clinical notes from relevant specialists, including hepatologists. Only cases deemed "Possible," "Probable," or "Definite" were included in this study.
Controls: In order to achieve sufficient power (80%) to detect meaningful HLA associations in this study, we sought to include at least three matched controls per DILI case  [12]. The 60 Caucasian controls for this study were gathered from BioVU's de-identified structured clinical data and notes. Control candidates were identified as patients who (1) were exposed to infliximab for at least 12 months, (2) had multiple lab values with no record of ALT or ALP > 1. 5x ULN or BILI > 1x ULN within 6 months pre-or post-infliximab exposure, and (3) had no record of DILIrelated ICD codes. Wherever possible, controls were matched for self-reported ethnicity, indication of use, age, sex, and BMI. Cases with unknown indication of infliximab use were primarily matched to controls with Crohn's disease, as this is the most common disease among infliximab users available in BioVU.
Statistical Methods: Power calculations were performed using Quanto  [14]  (version 1. 2. 4). Population structure and outlier identification were determined through PCA using the smartPCA program from the EIGENSTRAT  [15]  package (version 3. 0). Reported ethnicities were assessed by combining the study population with Thousand Genomes Project  [16]  (1KG) data and demonstrating clustering as expected based on principal components. Identity by descent calculations and filtering were run using PLINK  [17]  (version 1. 90b6. 9) to confirm that samples were unrelated. To evaluate HLA variant association with case-control status, R  [18]  (version 3. 4. 0) was used to calculate two-sided p-values, odds ratios (ORs) and confidence intervals (CIs) using Fisher's Exact Test. Given the fairly low number of alleles tested for causal associations (N=76), as well as known HLA haplotype associations within homogenous ethnicities  [13, 19, 20] , no corrections for multiple comparisons were employed in this study and p-values below 0.