dataesr/scientific-paragraphs-categorization huggingface dataset:
Propensity score approaches The propensity score (PS) is defined as the probability of being exposed to a drug of interest given the observed covariates  [30]. It is a balancing score, which means that conditionally on the PS, treatment exposure and the observed covariates are independent, so it is possible to deal with measured confounding. Recently, this methodology was extended to exploit large healthcare databases. In this framework, covariate selection algorithms are used to automatically select potential confounders for inclusion in the PS estimation model of a given drug exposure  [31]. In Courtois et al. [17] , several PS-based approaches were proposed in the context of signal detection from spontaneous reporting data. These approaches consisted in estimating a PS for each drug reported in the database.
The PSs were built by selecting among all the other drugs those to be included in the PS estimation model. Because of the large number of candidate covariates to be included in these models, covariate selection algorithms were used and compared. Here we used the lasso-bic approach presented earlier to select the set of covariates to be included in the PS logistic regression model. This procedure was repeated for all drugs in the database. Following Courtois et al, we accounted for these PSs in the final regression model through adjustment and weighting with two different weightings for the latter: Inverse Probability of Treatment Weighting (IPTW)  [32]  and Matching Weights (MW)  [33]. We also investigated the weights truncation approach with IPTW. This consists in assigning to individuals whose corresponding weight is below the rth percentile or above the (1r)th percentile of weights, the value of the rth or (1r)th percentile, respectively  [34].
Comparison set-up We simulated the occurrence of a given AE according to a logistic regression model y i ∼ Bernoulli(α i ) with α i = 1 1+exp -β 0 -P p=1 β p x ip. As for the drug exposure matrix, we used the French pharmacovigilance database for the period 2000-2017 which contains 452 914 individual reports and 2 378 different drugs (see "Real-world data analysis" section for a description of the data). For each replication of each scenario, we first randomly selected 100 000 individual reports out of the 452 914 individual reports. For each of these datasets, we then randomly selected a subset of 500 drugs among those reported more than 10 times. Thus, for each simulation scenario, N and P were set at 100 000 and 500 respectively. We investigated 27 scenarios that differed according to: • the value of the intercept β 0 , the latter being used to simulate outcomes of varying scarcity β 0 ∈ {-2, -4, -6}; • the number of drugs associated with the outcome among the 500 drug covariates: n TP ∈ {0, 5, 20}; • the value of the regression coefficients for the n TP true predictors: β TP ∈ {1, 2}; • the reporting frequency of the true predictors (if any): frequent (at least 100 reports over 100 000) or rare (between 20 and 100 reports over 100 000).
In total, we compared 14 signal detection approaches: one disproportionality method, four lasso-based approaches, five adaptive lasso-based approaches and four PS-based approaches. All these approaches (except RFET) are implemented in the R package adapt4pv available on the CRAN. All the analyses were performed with R version 3. 6. 0. All the logistic regressions were computed with the speedglm R package v0. 3-2 designed to handle sparse matrices efficiently. All lasso regressions were implemented using the glmnet R package v3. 0-2.
Real-world data analysis The french pharmacovigilance database We applied the aforementioned signal detection approaches to the French pharmacovigilance data extracted from 1 January 2000 to 29 December 2017. We discarded spontaneous reports involving (i) drugs recorded as vaccines, phytotherapy, homeotherapy, dietary supplements, oligotherapy or enzyme inhibitors, (ii) reactions recorded as overdoses or medication errors. Drugs are listed according to their active substance which is coded with the 5th level of the Anatomical Therapeutic Chemical (ATC) hierarchy. AEs are coded according to the Preferred Term (PT) level of the Medical Dictionary for Regulatory Activities (MedDRA). This extraction of the French pharmacovigilance database included 452 914 reports with 6 617 different AEs and 2 378 different drugs.
Comparison set-up To assess the performances of these approaches, we used a reference signal set pertaining to the adverse event Drug-Induced Liver Injury (DILI)  [28, 29]. The set was established by text-mining the FDA-approved drug labels with a list of keywords related to the DILI event. A level of DILI severity was assigned to each keyword: mild, moderate or severe DILI. According to where keywords appeared in the labelling section of the FDA-approved drug labels, drugs were classified in two DILI-related categories: "less-DILI-concern" and "most-DILI-concern". If no keywords were found in the label, drugs were considered as "no-DILI-concern". The majority of "most-DILIconcern" drugs were associated with severe DILI. This classification was refined later to assess the causal relationship between each drug and a DILI event using other data sources.
Only drugs confirmed as a cause of DILI were retained. We translated the list of keywords used to define a DILI event into Preferred Terms (PT) codes from the MedDRA classification. If a spontaneous report involved at least one of the PT codes, it was considered as a reported DILI event. This resulted in considering 25 187 DILI reports in the French pharmacovigilance database. We considered the "no-DILI-concern" drugs as true negatives, and the "most-DILI-concern" drugs as true positives. Over the study period, the database consisted of 1 692 different drugs reported more than 10 times. Among these drugs, 1 136 had more than three reports in common with a DILI. As in the simulation work, RFET and all the PS-based signal detection approaches were implemented on these 1 136 drugs and the remaining 556 had their pvalue set to one.
All the approaches based on this criterion: lasso-cv, adapt-cv and adapt-univ show a high sensitivity at the cost of a very high FDR in the vast majority of scenarios. The use of the permutation method with lasso regression (lasso-perm) does not show fully satisfactory results with an FDR generally higher than the one of our proposals and a moderate gain in sensitivity. Among the PS-based approaches for signal detection, results are concordant with our previous work  [17]. Our simulation study shows that weighting on the propensity score with matching weights perform very well when the outcome was frequent but become very conservative with a substantial drop in sensitivity as the outcome became rarer. This is a disadvantage since it is common in pharmacovigilance datasets to have very few reported outcomes.
Lasso-bic and CISL are also relevant detection approaches, with the few nuances in terms of performance detailed above. RFET and to a lesser extent ps-adjust provide sensitivity that is sometimes superior to our proposals but with a fluctuating FDR which can be high, especially when the number of predictors increases and are strongly associated with the response. Finally, making the concession of a large number of false discoveries (and thus a large number of signals to review), lasso-cv is the approach that provides the best sensitivity in all situations. It is more difficult to assess the differences in performances of the approaches from the results of the application to real data. As the DILI adverse event is highly reported and since the majority of drugs registered in the French pharmacovigilance database are much less reported, the most comparable simulation scenarios to this situation are scenarios 16 to 19.
Considering that here we are in the P << N situation, implementing the original BIC to perform variable selection seemed reasonable. In the case of the adaptive lasso, Hui et al. [44]  developed the Extended Regularized Information Criterion (ERIC) to perform variable selection. They argued that the BIC cannot account for the prior information carried by the AWs. In their work, they considered the BIC defined with the penalised log likelihood. By using the BIC which is based on the unpenalized likelihood, we avoid some of the issues raised by Hui et al. However, it would now be interesting to compare the variable selection for the adaptive lasso operating with the original BIC versus ERIC in our context. To preserve the specificities of pharmacovigilance datasets, i. e. a large size and sparsity, we based our simulation study on real data.
In silico molecular modelling studies The molecular docking simulations were carried out in the Glide (grid-based ligand docking) application implemented in the Maestro 9. 3 software (Schrodinger, LLC, New York, 2012)  44, 45. The crystal structure of b-CD was obtained from the Protein Data Bank PDB (ID: 1BFN). For adding the missing hydrogen, we used the Protein Preparation Wizard. Then, b-CD crystal structure was separated from a-amylase, followed by energy minimisation at RMSD convergence (0. 30 Å with OPLS_2005 as a force field). In order to obtain possible MA conformers, the MA structure was designed using the Maestro structure builder and optimised with LigPrep tool. To get the appropriate ionisation state, LigPrep was run with the Epik option set version 3. 7 (Schr€ odinger) to generate a possible state at pH 7.
4. Finally, the geometrical optimisation was performed using the OPLS-2005 force field. The "Generate Grid" sub-application of the Glide tool allowed the generation of the grid by selecting the entire b-CD structure as the receiving site to locate the coordinates of the centre of the targeted receptor cavity. Then, the generated grid was configured as the MA docking receiver using the "extra-precision" (XP) flexible docking method, from the Glide tool. The binding affinity "DG" was calculated using the Prime MM-GBSA module version 4. 5 (Schr€ odinger). The same work was done for the 2:1 MA:b-CD inclusion complex. On the other hand, in order to study the MA:b-CD interaction in the 1:2 complex, the b-CD structure was extracted from the crystallographic parameters. This last was provided by the structural database system of the Cambridge Crystallographic Data Centre and optimised by minimising energy to get the most stable state.
These results were obtained using the Materials Studio 6. 0 software  44. Then, the resulting structure was simulated in the Maestro 9. 3 software  45. We followed the same steps for the first two conjugates. Once the docking search was completed, the conformations were applied with the best binding energy. The complex inclusion structures resulted from the docking calculations were computed with Materials Studio using the Dmol3 method with the database (B3LYP/6-31g (d, p))  46, 47. This last allowed the computation of the descriptor dielectric energy (solvation). We noted that this last provided more information on the solubility of inclusion complexes between MA with b-CD.
Nuclear magnetic resonance (NMR) The 1 H and  13  C NMR spectra were recorded at 400 MHz on a Br€ ucker DRX 400 spectrometer in DMSO-d 6. Chemical shifts are expressed in ppm (d) downfield from internal tetramethylsilane (TMS). The NMR spectra were processed and analysed by MestReNova software 11. 02. 18153.
In silico molecular modelling studies The molecular docking was used to explore the interaction of b-CD with MA in the inclusion complexes with different stoichiometry. The best pose of MA in inclusion complexes is illustrated in Figure  3. The binding affinity expressed in the form of Glide docking score for the MA with b-CD is given by À7. 890 kcal. mol À1 for IC1. Actually, this value is comparable to that of the IC3 (À7. 375 kcal. mol À1 ), where the IC2 presents the lowest docking score. However, for complex stabilisation, contribution (from Van der Waals interaction) it takes higher value compared to lipophilic and hydrogen bonding interactions for all binary complexes (Table  1 ). On the other hand, Prime MM-GBSA module version 4. 5 (Schr€ odinger) is used to obtain the binding affinity (DG) that represents the free energy change upon formation of the complex, in comparison to total individual energy based on change in the solvent-accessible surface area.
Acknowledgements The authors are very grateful to " Minist ere de l'Enseignement Sup erieur et de la Recherche Scientifique de l'Alg erie " as well as University of Ferhat Abbas S etif 1 for the funding and the support of the project. Our thanks go to SALEM Pharmaceutical Laboratories headed by  Dr. A. Maiza  for the supply of the different chemicals used in this study. Pr. M. Boutahala  is gratefully acknowledged for the provision of the equipments used in the analyses as well as the laboratory facilities. The authors wish to acknowledge the helpful suggestions made by  Mrs. Christine Janssen.
First, 20 mg of the dry HT was dissolved in 1 mL of water and vortexed for 2 min. The resulting solution was diluted five times with water and vortexed for another 2 min prior to the analysis. The UHPLC-DAD system consisted of a Thermo Scientific™ Dionex™ UltiMate™ 3000 BioRS equipped with a WPS-3000TBRS autosampler and a TCC-3000RS column compartment set at 35°C (Thermofisher Scientific, Waltham MA, USA). The system was operated using kV, the cone voltage was set to 30 V, and the extract or voltage was set to 3 V. The source temperature was 100°C, and the desolvation temperature was 450°C. MS spectra were obtained by scanning ions between 100 and 1500 m/z. The system was operated using MassLynx 4. 1 software. The quantification of vitexin-2-O-rhamnoside, a common and abundant flavonoid found in hawthorn species  (Alirezalu et al.
For the GR analysis, homogenates (diluted at 1:1 or not diluted, depending on the sample) were added with a cocktail containing reduced nicotinamide-adenine dinucleotide phosphate, oxidized glutathione, and ethylenediaminetetraacetic acid. The reading was realized at 340 nm per min for 10 min and the area under the curve was calculated  (Carlberg & Mannervik, 1985). GSH was determined by first mixing a tris reagent plus ethylenediaminetetraacetic acid buffer with diluted homogenates (1:4) or a standard and read at 421 nm. This step was followed by the inclusion of 5,5′-dithiobis-(2-nitrobenzoic acid) and a 15 min incubation period. The second reading was also done at 421 nm. GSH was utilized as the standard. The calculation was realized by subtracting the first reading data from the second one, followed by linear regression  (Yoshikawa et al.
ACKNOWLEDGMENTS This study was financed in part by the 1. For each category of the score (leukocyte infiltration, inflammation extent, and crypt damage), points were multiplied by a factor of involvement of the colonic tissue. The sum of the three categories adds up to the total score of each section  (Dieleman et al. , 1998). Results are presented as mean ± SEM. One-way ANOVA followed by Tukey. Different letters indicate statistical significance (p<0. 05). *Score calculation: each parameter (crypt damage, inflammation extent, and leukocyte infiltration) was multiplied by the factor of involvement of the mucosa, and the total is the sum of the three results  (Dieleman et al. , 1998)  1. 94a C: control, CC: control colitis, H: hawthorn, HC: hawthorn colitis, MC: mesalamine colitis. FRAP: ferric reducing antioxidant power, ORAC: oxygen-radical absorbing capacity.
Introduction The availability of the sodium-free potassium binder patiromer opens new opportunities for hyperkalemia management. Objective Our objective was to compare data from a 4-year global pharmacovigilance database of adverse events (AEs) reported in patients prescribed patiromer in clinical practice compared with data obtained from the clinical trial program. Methods Postmarketing safety data regarding patiromer (Veltassa ® ; Vifor Pharma, Inc. ), collected and recorded in the company's global pharmacovigilance database, were analyzed for the period from January 2016 through September 2019. These data were both solicited (i. e. , via an organized data-collection method such as a patient-support program) and unsolicited (i. e. , voluntarily reported by healthcare professionals, consumers, and competent authorities worldwide).
The cumulative annualized mortality rate (events per 100 patient-years [PYs]) for the pharmacovigilance database analysis period were compared with the rate obtained in the longest patiromer clinical trial to date (up to 52 weeks of treatment). For individual AEs, reporting rates (% of events/100 PYs) for events collected in the global pharmacovigilance database were compared with the frequencies (% of patients with event/patients exposed) of events collected in the clinical trial program (N = 666). Results Over 4 years, the global pharmacovigilance database contained an estimated 45,000 PYs of exposure (17,823 individual case reports and 38,109 AEs), with most cases (95%) from the USA; > 85% of cases utilized 8. 4 g/day. In total, 1214 deaths were reported, with a cumulative annualized mortality rate of 2.
58 mmol/L  [1]. These data were derived from four clinical trials  [10, 11, 15, 16]  in which patients were initiated on patiromer in divided doses between 8. 4 and 33. 6 g daily. Real-world data indicate that the most widely used patiromer starting dose in clinical practice is 8. 4 g daily  [17, 18]. Because clinical trials are conducted under widely varying conditions, AE rates may not reflect those observed in practice  [1]. In this paper, we describe the reporting rate of AEs observed during the post-approval use of patiromer, using the global pharmacovigilance database of the patiromer manufacturer, and compare these with data from the clinical trial program.
Data Sources Postmarketing safety data regarding patiromer (Veltassa ® ) were collected and recorded in the company's global pharmacovigilance database. The data recorded in the global pharmacovigilance database were analyzed for the period from January 2016 through September 2019. The majority of AEs were from the USA (95%), and the remainder came from Austria, Belgium, Denmark, Finland, Germany, Israel, Netherlands, Norway, Spain, Switzerland, Saudi Arabia, UK, and Uruguay. From a pharmacovigilance perspective, an AE can be any unfavorable or unintended sign, symptom, or disease temporally related with the use of a medicinal product, whether or not it is related to the medicinal product  [19]. In addition, the US FDA indicated that a report or information submitted by a reporter does not necessarily reflect a conclusion that the report or information constitutes an admission that the drug caused or contributed to an AE  [20].
Temporal association is defined as events associated in time but not necessarily linked as cause and effect. The Uppsala Monitoring Centre, which is a part of the World Health Organization (WHO) Collaborating Centre for International Drug Monitoring, holds guidance on causality assessments. The company's pharmacovigilance database uses the WHO-Uppsala Monitoring Centre Standardized Causality Assessment System to assess the relationship between AEs and drugs  [21]. The global pharmacovigilance database consists of AE reports obtained in both a solicited and an unsolicited (spontaneous) manner. Solicited reports are defined as those obtained from an organized data-collection method (including, but not limited to, patient-support programs, disease-management programs, surveys of patients by healthcare providers, observational studies, and interactions between healthcare providers and company representatives), and all AEs regardless of whether the relationship with patiromer was mentioned within the exchange were captured.
The patient-support program provides a single point of contact to help the patient quickly start patiromer and stay on therapy. This enables the patient and prescriber to obtain easier access to the product, including refills, reimbursement, insurance verification and approval, and all necessary documentation needed to fill prescriptions. Unsolicited (spontaneous) AEs are defined as those voluntarily reported by healthcare professionals, consumers, and competent authorities worldwide (e. g. , FDA, European Medicines Agency [EMA]). All information obtained from a healthcare provider (e. g. , physician, pharmacist, nurse) are considered medically confirmed; however, these reports may not signify information that confirms a suspected relationship between the drug and the reported AE. In addition, a systematic literature and monitoring review of widely used reference databases, such as Embase ® (Elsevier) and PubMed (US National Library of Medicine), with a defined search string of any mention of "patiromer" or "Veltassa" or "RLY5016," is used to identify public reports of adverse drug reaction (ADR) cases and record these in the global pharmacovigilance database.
All ADRs are analyzed to determine whether a possible change to the benefit-risk profile of patiromer is needed and, in particular, to detect potential new safety signals, including AEs that have been identified in any literature search. All cases reported in the pharmacovigilance database were medically reviewed for clinical content, which includes medical history, concomitant medications, and possible causal relationship of AE to drug using the WHO-Uppsala Monitoring Centre Standardized Causality Assessment System. Further, three follow-up attempts are made to obtain information.
Data Collection The collection and the analysis of data retrieval, which includes duplicate checks, is a required standard pharmacovigilance process across the industry. Drug safety physicians review and analyze all individual case safety reports (ICSRs). Safety data were structured by ICSRs that could include one or more AEs in a single patient (i. e. , number of events is not equivalent to number of patients; each ICSR is one patient). Although individual patients may have multiple events reported in the database, the timing of some of these events is unknown. In addition, because of confidentiality rules or limited information reported on patients, a given AE may have been reported multiple times from different sources with a limited possibility for reconciliation and duplicate removal.
Verbatim AE terms from solicited and unsolicited sources from medical and nonmedical reporters are coded according to the most appropriate preferred terms defined by the Medical Dictionary for Regulatory Activities (MedDRA V22. 1). For AEs associated with laboratory abnormalities (e. g. , "blood potassium decreased," hypomagnesemia, etc. ), quantitative measures to confirm the accuracy of the reported AE term are inconsistent. Therefore, the reporting of these AEs is less rigorous than in clinical trials, where such quantitative laboratory data are available. In addition, the patiromer clinical trials had an independent adjudication committee that evaluated all deaths that occurred in the studies. In the global postmarketing database, no adjudication was conducted other than follow-up to obtain further information.
Data Analysis Data were analyzed using summary tables of all AEs and all serious AEs generated from the global pharmacovigilance database to identify any emerging events, unusual trends, or reporting frequencies. An AE is considered serious if it meets one or more of the following criteria: 1. Results in death or is life threatening. 2. Requires inpatient hospitalization or prolongation of existing hospitalization. 3. Results in persistent or significant disability or incapacity. 4. Results in a congenital anomaly (birth defect). 5. Is otherwise "medically significant" (e. g. , does not meet the preceding criteria but is considered serious because treatment/intervention would be required to prevent one of the preceding criteria). In addition, any AEs identified in the list of important medical events  [22]  (a list created by the EMA for the review of suspected AEs that deserve special attention) were upgraded as a serious AE in the global pharmacovigilance database.