dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants The inclusion criteria have been already described  (Selvy et al. , 2020) , and for this secondary analysis were as follows: sensory scores of the QLQ-CIPN20 ≥ 30/100, treatment with adjuvant oxaliplatin-based chemotherapy (FOLFOX-4) for colorectal cancer, ≤5 years from the time chemotherapy was discontinued, and no cancer relapse during these 5 years (cancer survivors). The exclusion criteria were sensory scores of the QLQ-CIPN20 < 30/100, age <18 years, and patients with neurological diseases (stroke, Parkinson's disease, Alzheimer's disease). Missing data on any of the variables were added to the exclusion criteria. Patients were identified from the database of the chemotherapy prescription software of each participating center. Thereafter, according to the inclusion/exclusion criteria, each center phoned their patients to inquire whether they would participate in the study.
Data Selection Patients' (gender, age, tobacco, hazardous alcohol, BMI first cycle, weight variation) and treatment characteristics (cumulative dose, dose intensity, time since last administration) were collected to characterize and compare our patients. Several studies have shown that weight and BMI  (Aprile et al. , 2008; Shahriari-Ahmadi et al. , 2015) , gender and age  (Vincenzi et al. , 2013; Molassiotis et al. , 2019; Selvy et al. , 2020) , tobacco and alcohol  (Kawakami et al. , 2012; Scott et al. , 2013; Vincenzi et al. , 2013; Molassiotis et al. , 2019) , and chemotherapy regimen  (Beijers et al. , 2014; Molassiotis et al. , 2019; Selvy et al. , 2020)  are related to chemotherapy toxicity in colorectal cancer patients. Items of QLQ-CIPN20 were collected to characterize (sensory, motor and autonomic symptoms) and determine the intensity of CIPN.
Statistical Methods Statistical analyses were performed using Stata software (version 15, StataCorp, College Station, US) and R 3. 5. 1 (http://cran. rproject. org/). All the tests were two-sided with a type I error set at 0. 05. Categorical variables were expressed as number of patients and percentages, and quantitative variables as mean ± standard deviation or as median [interquartile range], according to statistical distribution. Normality was verified by the Shapiro-Wilk test and/or histogram. A multiple correspondence analysis (MCA), which can be considered as a generalization of principal component analysis for categorical rather than quantitative variables, was applied to study the associations between the sociodemographic (e. g. age, gender, BMI) and chemotherapy (e. g. cumulative dose, dose intensity) characteristics of the patients, QLQ-CIPN20, neuropathic pain, anxiety, depression and HRQOL.
The WHO does not recommend self-medication with any medicines (including anti-infective agents) as a prevention or cure for COVID-19  [10]. However, detailed reports on self-medication and its consequences during the COVID-19 pandemic are lacking. The primary objective of the present study was to describe the characteristics of ADRs linked to selfmedication as notified to the French Pharmacovigilance Database (FPVD) during the first French COVID-19 outbreak (from March to May 2020). The secondary objective was to compare the incidence and characteristics of ADRs linked to self-medication notified during the COVID-19 outbreak with those notified during the same calendar period a year previously (i. e. before the outbreak).
For comparative purposes, we selected a period 12 months previously, i. e. from March 15th to May 31st, 2019. We examined all ADRs recorded in the FPVD during each of the two periods and selected those linked to self-medication (i. e. overdoses, medication errors, addiction, drug interaction, breastfeeding, or other non-specific ADR) with the ''suspect'' drug. The present dataset analysis was approved by the 31 regional pharmacovigilance centres and the French National Healthcare Product and Drug Safety Agency [Agence nationale de sécurité du médicament et des produits de santé (ANSM), Paris, France], which manages the database and helped us with data extraction. It should be noted that the authors of the present article were solely responsible for interpretation of the data, and that the ANSM was not involved in this process.
Statistical analysis Descriptive statistics were reported for the main study variables. Results were expressed as median [interquartile range] or the number (percentage). The frequency of ADRs linked to self-medication was calculated as the proportion of all ADRs notified over the same period. Data were compared by year using Fisher's test. A P-value ≤ 0. 05 was considered to be statistically significant. All statistical analyses were performed using SPSS software (version 18. 0, SPSS Inc. ,.
Data collected during the COVID-19 outbreak Of the 3114 notifications recorded in the FPVD from March 15th to May 31st, 2020, 114 (3. 7%) were linked to selfmedication. Most of these 114 cases were reported by specialist physicians, only 14% of the ADRs were reported by the patient. Fifty-eight of the 114 notifications (51%) were serious: 33 cases of hospitalization, 9 life-threatening ADRs, 1 death, and 15 other cases with serious medical conditions. The median [interquartile range] age of the affected patients was 30. 5 [15. 0; 57. 5], 25 of the 114 ADRs concerned children, and 65 (57%) concerned females; with regard to comorbidities, 13 people (11%) presented anxiety and depressive symptoms, 9 (8%) had a history of suicide attempts, and 6 (5%) were drug addicts. Medication errors and overdoses accounted for 31.
Discussion This is the first report on self-medication and on ADRs linked to self-medication in the context of the COVID-19 outbreak. The study's objective was to describe the main characteristics of ADRs linked to self-medication, as recorded in a national pharmacovigilance database during a COVID-19 period. Firstly, we found that ADRs linked to self-medication (most of which were notified by physicians) accounted for 3. 7% of all ADRs registered in the database during the COVID-19 period; this proportion was twice that recorded for a similar but COVID-19-free period one year previously (1. 6%). Second, we found that half of the notifications concerned ''serious'' ADRs, and almost two thirds concerned overdoses and medication error (including a significant number of accidental ingestions by children).
Three drug classes accounted for a little more than half of the notifications: analgesics, psycholeptics, and antibacterials for systemic use. Firstly, it appears that the proportion of reported ADRs linked to self-medication was higher during the COVID-19 outbreak -even though this variable was probably underestimated due to poor notification. The ADRs linked to self-medication proportion was 1. 6% in COVID-19-free period one year previously; the value is in line with a previous analysis (1. 3%) of the same database in another period  [13]. The number of ADR (n = 114) related to self-medication could appear low in comparison to the French population. A wellknown problem in the spontaneous reporting system is the underreporting of ADRs  [14] , however spontaneous notifications (as in the present report) represent an important tool to identify safety signal.
Indeed, disproportionate fear of COVID-19 is likely to prompt dramatic changes in behaviour that reduce contact with physicians and lead to excessive, inappropriate selfmedication and thus to related ADRs. The present study also had limitations -notably the inherent limitations associated with pharmacovigilance database analyses, including underreporting. The degree of underreporting is probably especially high for selfmedication; in the present study, only 14% of the ADRs were reported by the patient.
Determination of Phenolic Compounds The EA-RT and E-RT were analyzed for their content of flavonoids and polyphenols by the HPLC method. Chromatography separations were performed on a reversed-phase (RP-18) column (Agilent Technologies 250 mm × 4. 6 mm, 5. 0 µm), protected by a pre-column (Agilent Technologies RP-18, 10 mm × 4. 6 mm). Both columns were placed in a column oven set at 25 • C. The HPLC system consisted of a pump (SCL-10), an automatic injector (SIL-10AD) and a detector (SPD 10A UV-visible) set at a spectrum beginning at 200 nm and ending at 700 nm (Shimadzu, Japan). Data collection and analysis were performed using Shimadzu LC Solution chromatography data station software. Two solvents were used with a constant flow rate of 1 mL/min and injection volume of 10 µL. Solvent A consisted of 5% acetonitrile and 95% water, whereas solvent B consisted of a phosphate buffer dissolved in water (pH 2.
Statistical Analysis Statistical analyses were performed using the computer software Sigma Plot 12. 5 for Windows. All data were expressed as mean ± standard error (SEM); p values less than 0. 05, 0. 01 and 0. 001 were considered to be significant. Comparisons between different groups were performed using one-way analysis of variance (ANOVA). Significant differences between the control and experimental groups were assessed by Tukey's test.
This was conducted to determine the accuracy and limitations of current in silico models. The commercial models were DSkin from Scientific Consilience  [8, 9] ; TCAT™, a module in GastroPlus™ software from Simulations Plus; and the SimCyp Certara Population-Based Simulator ("SimCyp"). Open source models were from the University of Surrey  [10] , the CDC "Finite Dose Skin Permeation Calculator"  [11] [12] [13] [14] ; and the EPA Dermwin™ program in EPI Suite™. The models vary in complexity and features (Table  1 ) but are primarily based on the physical chemistry of absorption, with different degrees of physiological relevance built in. All models (except DermWin) were built to simulate the kinetics of the amount of chemical in each of the skin layers. Some models were built to simulate different scenarios (e.
Models and partners The three commercial models were TCAT™ model a module in GastroPlus™ software Version 9. 6 from Simulations Plus (www. simulations-plus. com), DSkin from Scientific Consilience (www. scientificconsilience. com) and SimCyp Simulator software Version 16. 1. from Certara (www. certara. com). The open source models were from the University of Surrey  [10] , the CDC "Finite Dose Skin Permeation Calculator" (https://www. cdc. gov/niosh/topics/skin/finiteskinperm calc. html); and the EPA Dermwin™ program in EPI Suite™ -Estimation Program Interface Version 4. 11 (https://www. epa. gov/tsca-screeni ng-tools/epi-suitetm-estimation-program-interface). Simulations using three of the models were run by the model developers, namely, TCAT, DSkin and Surrey, whereas the other three were run by end-users with varying levels of expertise with these models: CDC simulations were run by modelling experts, the SimCyp simulations were run by an end-user who was familiar with the Simulator but not with the skin module, and the Dermwin™ simulations were run by an end-user who routinely uses this software for safety assessment.
Data supplied There were 25 chemicals included in this evaluation with penetration data into and through the human obtained using a standardized protocol  [5]. As PBS was used as a common vehicle, aqueous solubility was a key criterion for chemical selection; however, one chemical was not soluble in water; therefore, ethanol was selected as an alternative solvent. As a result, 24 chemicals were evaluated in PBS and one was evaluated in ethanol. To investigate the impact of ethanol on the skin penetration, three chemicals evaluated in PBS were also tested using 100% ethanol as the solvent. Therefore, two separate simulations are required for these three chemicalsone reflecting cutaneous The data supplied are shown in Supplementary Table  1. The input parameters for each chemical could be used in all simulations but it was not a stipulation to do so.
This evaluation was conducted to assess the performance of six in silico models and their appropriateness for application in the cosmetics industry. Given the differences in the model inputs, outputs, simulated scenarios and intended purpose, a purely quantitative comparison was not readily achievable. Along with performance, several other factors with respect to the suitability of the models for application and development within the cosmetics industry were considered. The models were evaluated using a dataset of 25 chemicals, for which in vitro skin penetration data were generated in a previous Cosmetics Europe study  [5]. This evaluation highlighted common gaps in the models where development is required and indicated key mechanisms that were not accounted for in all models. The models produced reasonable predictions for some outputs, with the majority of models producing conservatively high estimates of DD for the tested chemicals.
Three models were open source, allowing for more transparency of equations, assumptions and source codes for end-users, unlike commercial models (although they often list QSAR equations). However, open source models tend to be more simplistic (e. g. DermWin predicts only Kp values, rather than cutaneous distribution) or require expert modelers to handle the software in the absence of a user-friendly interface (e. g. the Surrey model). The online version of the CDC model appears straightforward; however, it also relies on the user having know-how of which parameters to use and their impact on the simulations (e. g. partially or fully hydrated skin). While some level of expertise and understanding is required for all models tested in this evaluation, it is important that new users can be easily trained.
If the models can better predict cutaneous distribution, the ability to predict accumulation from repeat application should be evaluated. More experimental data are required to achieve this. The data used to evaluate these models, along with planned analysis of similar data on a further 25 chemicals from the same dataset, may be of use in refining the models and associated QSARs so that improved predictions of cutaneous distribution can be made. This would give increased confidence that the models are accurately representing the kinetics of dermal transport.
Acknowledgements Cosmetics Europe would like to thank the following partners involved in this project:  J. Jaworska ,  J. Troutman ,  B. Laidig  (P&G) (conducted CDC simulations);  D. Keller  ( Henkel ) (conducted DermWin simulations);  T. Chen  ( University of Surrey ) (conducted "Surrey" simulations);  Scientific Consilience  (conducted DSkin simulations); Certara (technical support of Simcyp simulations); SimulationsPlus (conducted TCAT simulations). This work was sponsored by  Cosmetics Europe.
Objective • Atopic dermatitis is a chronic inflammatory disease characterized by eczematous skin lesions and multiple symptoms, including pruritus, sleep disturbance, and depression 1-4 • Tralokinumab is a high-affinity, fully human monoclonal antibody designed to specifically neutralize interleukin-13, a key driver of the underlying inflammation in atopic dermatitis 5-7 • Phase 3 trials have established the efficacy and safety of tralokinumab for up to 52 weeks in adult patients with moderate-to-severe atopic dermatitis 8,9 • An ongoing, open-label extension trial, ECZTEND (NCT03587805), is investigating the long-term safety and efficacy of tralokinumab in patients with atopic dermatitis who participated in previous tralokinumab trials • To present interim ECZTEND efficacy data collected through April 30, 2020 from a patient cohort receiving tralokinumab for at least 56 weeks Conclusions • In this ECZTEND interim analysis of the Week 56 cohort, tralokinumab 300 mg q2w plus optional TCS demonstrated sustained long-term improvements in itch, sleep, and the extent and severity of atopic dermatitis up to Week 56, with maintenance of robust EASI response rates (61% of patients achieved EASI-90 at Week 56) • Overall, tralokinumab plus optional TCS was well tolerated in patients enrolled in ECZTEND at data cut-off, with a safety profile consistent with the parent trials.
General experimental procedures NMR analysis The 1 H and 13 C-NMR spectra were recorded on a Bruker Avance III 600 spectrometer equipped with a cryo-platform ( 1 H at 600 MHz and 13 C at 150 MHz). 2D NMR experiments were performed using standard Bruker microprograms (Xwin-NMR version 2. 1 software). All chemical shifts (δ) are reported in parts per million (ppm) with the solvent signal as reference relative to TMS (δ = 0) as internal standard, while the coupling constants (J) are given in Hertz (Hz). Deuterated solvents, methanol (CD 3 OD), dimethyl sulfoxide (DMSO-d 6 ), and chloroform (CDCl 3 ) were used as solvents for the NMR experiments.
The assay was carried out with 3 ml of nutrient brothglucose-TTC medium, supplemented with varying concentrations of MeOH extract or compound 2 in 20 mL screw-capped test tubes. The TPF produced after each exposure period (0, 30, 60 min) was extracted in 4 mL of amyl alcohol and determined spectrophotometrically at 500 nm. The amount of formazan produced was determined from a standard dose-response curve (R 2 = 0. 9983). Dehydrogenase activity was expressed as the amount of TPF formed (μg) per amount of dry cell weight of cell biomass (in mg). Data were expressed as the mean ± standard deviation.
Acknowledgements The authors are grateful to the  University of Dschang  for financing some consumables used in this work, to the " Service Commun d'Analyses" and "Groupe Chimie des Substances Naturelles ", to the  "Institut de Chimie Moléculaire de Reims"  for the spectroscopic and spectrometric analysis on the ESIMS and  NMR equipement of the PlAnet Platform. The EUprogramme FEDER  to the  PlAneT CPER  project is gratefully acknowledged.
Data Thirty French veterinarian offices were randomly contacted to obtain their purchasing contracts with pharmaceutical firms during the 2008-2014 period. The data of five veterinary independent offices and three veterinary groups pooling their purchases were collected. All practices include both companion and farm production animal activities with various shared activities. Large variation is observed in the size of the office (one to several veterinarians per office) and the yearly revenue of the office. For inclusion, the purchase contracts should specify the medicine or group of medicines to be purchased, the objective of the purchases required for the veterinarian to obtain the rebate and the rebate provided by the pharmaceutical firm (in absolute value or percentage) if the purchase objective is achieved.
In total, 498 contracts, 23 pharmaceutical firms and 125 medicines were included in the raw data (Figure  2 ). Most contracts were related to bovine production and restriction on this species leaded to 382 contracts related to 8 main pharmaceutical companies. The following categories of veterinary medicine were created, and data not belonging to these categories were excluded: AMs, antiparasitics (APs, i. e. , pest control), anti-inflammatories (AIs) and vaccines (VACs). The final dataset included 382 contracts, 47 veterinary medicines and 2320 observations. Each medicine was coded according to the company (C1 to C8) and the medicine (P1 to P47), resulting in a combination from C1P1 to C8P47. For each medicine, the price of the medicine when the veterinarian bought the medicine from the wholesaler (i.
e. , before the rebate from the pharmaceutical firm) was selected. Then, a database including the following variables was created: veterinary office, firm, year (of the contract), range (of the medicine, i. e. , how the medicines were grouped Then, a database including the following variables was created: veterinary office, firm, year (of the contract), range (of the medicine, i. e. , how the medicines were grouped in the contract), medicine name, yearly revenue from the veterinarian office of each firm, duration of the contract (quarterly, biannually, or annually), monetary purchase objective to obtain the rebate, type of rebate (per medicine, per range or global as defined below), rebate value in percentage, price of the medicine, type of medicine (parenteral administration following body weight dosage (PerBW), intramammary syringe (SYR), VAC or per animal fixed dose (DOSE)), and category of medicine (AMs, APs, AIs, or VACs).
Finally, the medicines expressed as doses before 2010 had very low PUR (€1 vs. €3. 25 per dose), supporting the exclusion of these 5 observations. Then, the data were analysed with R software  [27]. A linear regression was performed using the nlme package of R. The outcome variable was PUR, and the explanatory variables were objective, year, yearly revenue from the veterinarian office of each firm, type of medicine (general administration, intramammary syringe, vaccine or per weight dose) and type of rebate (medicine, range, or global). The variable type of PUR was also created (per 100 kg BW or per dose). A step-by-step procedure was used to include the explanatory variables one-by-one, and then, final multivariate models were proposed based on the Akaike information criterion (AIC) values.
Background In 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced quality tolerance limits (QTLs) as a quality control in clinical trials. Previously, TransCelerate proposed a framework for QTL implementation and parameters. Historical data can be important in helping to determine QTL thresholds in new clinical trials. Methods This article presents results of historical data analyses for the previously proposed parameters based on data from 294 clinical trials from seven TransCelerate member companies. The differences across therapeutic areas were assessed by comparing Alzheimer's disease (AD) and oncology trials using a separate dataset provided by Medidata. Results TransCelerate member companies provided historical data on 11 QTL parameters with data sufficient for analysis for parameters.
Introduction In 2016, International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E6 introduced quality tolerance limits (QTLs) as a quality control in clinical trials. Since then, the industry has been learning about implementation. In support of this learning, TransCelerate prepared this manuscript to offer a frame of reference for future clinical trials. QTLs set limits for detection of systematic issues impacting on participants' safety or the reliability of trial results. Several recent papers fill in some of the missing details about implementing QTLs, including their relationship to risk-based monitoring  [1]  and a framework for implementation  [2]. Using a risk-based approach, sponsors typically define which of their clinical trials will benefit from QTLs as a risk control.
This manuscript was written to expand the available references that sponsors can consult when setting QTLs by providing historical benchmarks of parameter values. In 2020, TransCelerate published an expanded and updated framework for QTL implementation (available at www. trans celer atebi ophar ma. com and published as a peer-reviewed article  [2] ). The QTL parameters proposed by  Bhagat et al. were used as a basis for this article  [2]. TransCelerate collected and aggregated historical data provided by a subset of TransCelerate member companies and separately by Medidata. As the list of parameters used is predominantly applicable for medium and large trials, therefore, this exercise focused on trials recruiting more than 50 trial participants. At the time this study was designed, the volume and characteristics of the data to be received was unknown; thus, no statistical hypothesis testing was planned.