dataesr/scientific-paragraphs-categorization huggingface dataset:
Pharmacological agents that inhibit protein kinases Protein kinase inhibitor (PKI) drugs have been used for several decades, in particular in drugs that inhibit mTor (-rolimus). However, the advent of this family did not materialize until imatinib was marketed in the early 2000s. Over the past 20 years, in the region of 450 PKIs have at least one clinical trial registered in the clinicaltrials. gov register. Of these inhibitors, almost 100 molecules have been discontinued during their development, due in particular to toxicities, lack of efficacy or commercial reasons. A regulated and updated list of these medicineswith a known International Nonproprietary Namecan be found at https://www. icoa. fr/pkidb [2, 3]. Currently, more than 60 drugs have been approved for marketing, primarily intended for the treatment of a variety of cancers but also directed against inflammatory diseases  [4, 5].
Based on data collected from the Cortellis database -a pharmaceutical pipeline database produced by Clarivate Analytics-all kinases targeted by at least one molecule have been represented using the kinome phylogenetic tree in Fig. 1. The tyrosine kinase family is the group for which there has been the most clinical development and the most drugs having obtained a marketing authorization. Kinase targeted by most of the approved drugs is VEGFR, EGFR, KIT, fms-like tyrosine kinase (FLT) and/or Abelson proto-oncogene (ABL). A few marketed drugs target the other groups such as CMGC, AGC and ste20 kinases (STE) familythe targeted kinases are the targeted kinases are cyclin-dependant kinases (CDK4/CDK6), protein kinase C (PKC) and mitogen-activated protein kinases MEK1/MEK2 respectivelybut many small molecule kinase inhibitors targeting these families are currently under clinical development.
CSK inhibition is an off-target effect of ibrutinib and may lead to increased inflammation and fibrosis, predisposing the heart to atrial fibrillation  [42]. Moreover, an analysis from the Word Health Organization pharmacovigilance database described other potential cardiovascular toxicities related to ibrutinib including hypertension, heart failure, ventricular arrhythmias, and conduction disorders with ibrutinib which remains to be further explored  [43].
Most of the lung ADRs reported during imatinib therapy are related to fluid retention, a common side effect  [64]. However, peripheral and periorbital oedema are far more frequent manifestations of fluid retention than are pleural or pericardial effusions and pulmonary oedema. Imatinib-induced pleural effusion appears to be dose dependent, with higher incidence in patients treated with 400 mg twice-a-day. For the other BCR-ABL PKIs, the incidence of pleural effusion in clinical trials ranged from 9 to 12% for bosutinib  [65] , 2. 1 to 9% for ponatinib  [66]  and less than 1% for nilotinib  [67]. A disproportionality analysis through data registered in the World Health Organization (WHO) safety report database (VigiBase ® ) showed that 29 of 33 PKIs included have significant RORs for pleural effusion with a highest value for dasatinib, bosutinib and ponatinib  [12].
Disclosure of interest Authors have no competing interest to declare. According to Cortellis database, all of the kinases being targeted by at least one inhibitor have been highlighted. Each circle for each protein kinase reflects the total number of inhibitors known to have an action on this target. All the drugs' mechanisms have been taken into account, i. e. for multiple mechanisms, the molecule has been assigned to each of its targets as well as its state of development. The greener the circle, the greater the number of inhibitors granted marketing authorisation. Conversely, the redder the circle is, the more inhibitors there are that have failed during clinical development. Grey circles represent targets for which inhibitors are all under development. This kinase dendrogram were created using the interactive web application CORAL.
Statistical evaluation For each plot, annelid variables (i. e. , total density, species density, and epigeic, anecic, and endogeic density) were calculated from the sum of the four samples and expressed as density (ind m -2 ). Mean values of each variable were then averaged on the four replicates of each treatment. The differences between treatments were assessed on log transformed data (log(x + 1)) using parametric tests (one-way ANOVA and Dunnett tests) if the homogeneity of variance (Bartlett test;  Snedecor & Cochran, 1989)  and the normality of residuals (Shapiro test) were respected. Nonparametric tests (Kruskal-Wallis tests) were used if these conditions were not respected. All statistical analyses were performed with n = 4. The level of significance was fixed at p < 0. 05. The analyses were carried out with R statistical software (R Core Team 2017, package version 1.
ACKNOWLEDGMENT We thank Jean-Pierre Pétraud, Jodie Thénard, and Véronique Etiévant (INRAE) for their useful help and advice on the experimental design. We also thank Christophe  Montagnier and Arnaud Huré (experimental unity, INRAE), Marc Hanrio, and Florent Viala (park department, INRAE)  for the rental and the mowing of the meadow before pesticide application. We thank Estelle Chan, Mary Gay, Daphné Guénée, Pauline Thiel, and Sacha Roudine for participating in earthworm identification. We also thank Ghislaine Delarue, Amélie Trouvé, and Sébastien Breuil for pesticide analyses. We also thank Sébastien Breuil for pesticide soil spreading and many other colleagues of UMR ECOSYS for soil and soil fauna sampling. We thank Arnaud Boivin and.
u-AMH contains an R451T mutation at the cleavage sites between the N-terminal prodomain and the C-terminal GF domain. The numbering corresponds to amino acid residues in UniProtKB accession number P03971 (human AMH). C, AMHR2-Fc/Fc was generated by coexpressing a complementary DNA encoding an AMHR2-Fc fusion protein together with a complementary DNA encoding the Fc portion of IgG1 in human embryonic kidney 293 cells. The numbering corresponds to amino acid residues in UniProtKB accession numbers Q16671 (human AMHR2) and P01857 (human 1g γ-1 chain constant region). The representations of c-AMH and u-AMH (shown in B) and AMHR2-Fc/Fc (shown in C) are used to depict these molecules in ELISA formats. AMH, anti-Müllerian hormone; AMHR2, anti-Müllerian hormone receptor type-2; c-AMH, cleaved AMH; GF, growth factor; u-AMH, uncleaved AMH.
S4A ). Also, an ELISA that detected RL by means of biotinylated c-AMH, performed as in Figure  3F , gave results similar to those obtained using mAb-N 1 (Fig. S4B ). Values for [R]/[R 0 ] and [RLR]/[R 0 ] were calculated from the experimental datasets as described in Table  1. Figure  5D  shows how the concentrations of RL, RLR, and free R vary as a function of AMH concentration. Global fitting of these three datasets to the equations of Perelson and DeLisi gave values of K 1 = 1. 1 nM and [R 0 ]/K 2 of 4. 9, which are in close agreement with the best fit values obtained from Figure  5C. As expected, the relationship of [RLR]/[R 0 ] to AMH concentration is a bell-shaped curve, with the value of K 1 -the dissociation constant for the monovalent interaction of the AMH complex with AMHR2 -corresponding to its midpoint.
A B D C Figure  7. Bivalent binding of AMHR2 to the AMH complex increases the K d of the complex by a factor of 1000. A, ELISA used to measure intact AMH complex. B, c-AMH and u-AMH were diluted in PBS containing 1% BSA and 1% goat serum to 1 ng/ml (8. 8 pM), and the level of intact AMH complex was assessed after 10 days (t 10 ; N = 21); samples diluted immediately before the ELISA are labeled t 0 (N = 9). C, concentration dependence of the apparent dissociation constant (K T ) determined for the AMH complex bound to AMHR2. K T values are from Table  1. D, relationship between complex dissociation and receptor occupancy. Data for intact AMH complex [NC T ]/[RL + RLR] and receptor occupancy [RL + 2RLR]/[R 0 ] are from Table  1. In C and D, the curves were generated using the quadratic equation for a reversible dissociation/association reaction in combination with the equations of Perelson and DeLisi.
The temperature of the sample is measured using the thermocouple connected with the viscosimeter. The fluid sample was heated up to reach as table temperature before the viscosity measurement. The spindle rotation speed is selected to ensure that the applied torque was set between 10% and 100%. Within this torque range, the instrument produced satisfactory result. The viscosimeter is connected to a computer which records the data automatically. The Rheocalc T 1. 2. 19 software is used to treat the experimental data such as spindle rpm, viscosity, temperature, shear stress, shear rate, torque % and time.
Dynamic viscosity measurements In order to verify the analysis setup, dynamic viscosity measurements are performed on the diethylene glycol as a function of temperature. The data obtained are compared with those that have been published  [36]. The experimental values match nicely with the published data, see Figure  6. The maximum difference is of ± 3 % between the experimental data and the published ones, with the temperature ranging from 30 to 60°C. Figure  7  shows shear stress (dyne/cm 2 ) versus shear strain rate (s -1 ) for the Gd 2 O 3 suspension at 20°C. The suspension clearly exhibits a Newtonian behavior. Since the carrying liquid of the nanoparticles exhibits a Newtonian behavior, it seems that the rheological property of the liquid dominates the whole mixture if the nanoparticle concentration is not high enough to modify the viscous properties  [37].
Polyphenols are bioactive compounds from plants with antioxidant properties that may have a protective role against body weight gain, with adipose tissue and systemic oxidative stress as potential targets. We aimed to investigate the dietary intake of individual polyphenols and their association with 5-year body weight change in a sub-cohort of the European Prospective Investigation into Cancer and Nutrition (EPIC). This study included 349,165 adult participants from nine European countries. Polyphenol intake was estimated through country-specific validated dietary questionnaires and the Phenol-Explorer database. Body weight was obtained at recruitment and after a mean followup time of 5 years. Associations were estimated using multilevel mixed linear regression models. From 91 polyphenols included, the majority (n = 67) were inversely associated with 5-year body weight change after FDR-correction (q < 0.
Dietary Data At baseline, validated country/centre specific dietary questionnaires were used to collect dietary data regarding the previous year  [16]. The standardized EPIC Nutrient Database  [17]  was used to estimate total energy and nutrient intake. Dietary intake of polyphenols was estimated using the Phenol-Explorer database  [18] , where more than 500 individual polyphenols have been included from over 400 different dietary sources, considering the effects of food processing and cooking. Further details were described previously  [19].
Statistical Analysis Dietary data on 419 individual polyphenols were available for this study. For inclusion in the analysis, a cut-off point was made for polyphenols for which over 50% of the study participants were consumers and with a mean intake ≥ 1 mg/d. A total of 91 individual polyphenols met these criteria and were analysed as continuous variables. To improve right skewness, polyphenol data was log2-transformed, meaning that one unit increase corresponded to a doubling in the intake. To manage very small intakes, values below 0. 0001 mg/d were transformed into zero. Then, half the minimum intake of the corresponding polyphenol was added to each zero value. Pearson's correlation coefficients were calculated between polyphenols and were considered strong when r ≥ 0. 8. Correlated polyphenols were clustered in groups.
From each group, those individual polyphenols presenting the strongest correlations and the highest median intake were selected (on behalf of the others) for the final analysis. Main food sources were selected from our previous EPIC study, where dietary data at baseline were linked to the Phenol-Explorer database  [24]. Distributions of participants' characteristics were calculated according to quintiles of body weight change over 5 years, with quintiles 2 and 3 as reference categories (i. e. , maintenance, range -2. 23 to 1. 77 kg/5 years). We performed multilevel mixed linear regression models with random effects on the intercept. EPIC centres were taken as random effects to control for differences in follow-up procedures and questionnaire designs among centres. ANOVA and Akaike's information criterion were used to verify their design.
Interactions have been discussed in more detail in our previous study regarding polyphenol classes and subclasses and body weight change in the EPIC-PANACEA cohort  [11]. The prospective design of this investigation, its large sample of participants from different European countries, and its sufficient statistical power is a major strength. However, this study has some limitations. Assessing the relationship between diet and health outcomes is challenging in epidemiological studies, due to biases linked to dietary measurement errors  [51]. To minimize them, we included validated dietary questionnaires and a standardized database to assess food and nutrient intakes  [16, 17] , as well as the use of Phenol-Explorer database  [18]  to estimate polyphenol intakes. We had data on a large variety of variables related to diet and lifestyle, but most of them were only collected at baseline, and therefore changes during the follow-up were not accounted for.
Acknowledgments: We thank all EPIC participants and staff for their contribution to the study. We also thank  Bertrand Hémon  and  Catalina Bonet  for their valuable help with the EPIC database. We also thank the  National Institute for Public Health  and the  Environment (RIVM) ,  Bilthoven , the  Netherlands ; the  University Paris-Saclay, University of Versailles Saint-Quentin-en-Yvelines (UVSQ) ,  Institut national de la santé et de la recherche médicale (INSERM) ,  Gustave Roussy ,  Centre de Recherche en Epidémiologie et Santé des Populations (CESP), Villejuif, France ; and the  Oxford University (UK)  for their contribution and ongoing support to the  EPIC Study.
Funding: This research has been funded by the  Instituto De Salud Carlos III  through the project  PI18/00191  (Co-funded by  European Regional Development Fund, ERDF , a way to build Europe). The  PANACEA  project was funded by the  European Union , in the framework of the  Public Health Programme  (project number: 2005328). The coordination of EPIC is financially supported by  International Agency for Research on Cancer (IARC)  and also by the  Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London  which has additional infrastructure support provided by the  NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by:  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale ,  Institut National de la Santé et de la Recherche Médicale (INSERM) (France) ;  German Cancer Aid ,  German Cancer Research Center (DKFZ) ,  German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) ,  Federal Ministry of Education and Research (BMBF) (Germany) ;  Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy ,  Compagnia di San Paolo and National Research Council (Italy) ;  Dutch Ministry of Public Health, Welfare and Sports (VWS) ,  Netherlands Cancer Registry (NKR) ,  LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland) ,  World Cancer Research Fund (WCRF) ,  Statistics Netherlands  (The Netherlands);  Health Research Fund (FIS)-Instituto de Salud Carlos III (ISCIII) ,  Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra , and the  Catalan Institute of Oncology-ICO (Spain) ;  Swedish Cancer Society, Swedish Research Council and County Councils.
| Data analysis The basal values and the values of the follow-up were calculated for each of the 4 treated zones and for each treatment. A sat- The non-inferiority of ART FILLER® Volume for one area was concluded if the lower confidence limit was > -15% the lower confidence limit of Juvéderm® Voluma. The mean and the standard deviation changes in scores between D0 and the following days were calculated. The statistical significance of the evolution of the scores was calculated by a Wilcoxon test.
GEMELLI M Design and Participants Detailed methods of the trial have been previously reported  [5]. In brief, GEMELLI M was a randomized, open-label, multinational, multicenter, active-controlled, parallel-group, phase 3 trial initiated in July 2019 and completed in August 2020. The study comprised a 2-week screening period, a 26-week efficacy and safety treatment period, and a 2-day post-treatment follow-up period. This study is registered on the European Union Drug Regulating Authorities Clinical Trials Database (2017-000092-84) and was conducted in accordance with the ethics principles of the Declaration of Helsinki of 1964 and its later amendments, the International Conference on Harmonization Guidelines for Good Clinical Practice, and all applicable laws, rules, and regulations. The protocol was approved by an independent ethics committee or institutional review board for each center except in Poland where approval was by a national ethics committee (Komisja Bioetyczna przy Okregowej, Lublin).
Statistical Analysis The HbA1c analyses were performed on data from the intent-to-treat (ITT) population, defined as all randomized participants  [5]. Insulin dose and safety analyses were based on data from the safety population, defined as all randomized participants who received at least one dose of study medication. The AIA population for AIA and NAb response analysis included all participants from the safety population with at least one AIA sample available for analysis (sample collected at least 8 h after last administration of premix insulin) during the 26-week on-treatment period, as defined previously  [5]. Statistical methods for the total study population have been previously reported  [5]. Subgroup analyses reported here were descriptive, with no formal statistical testing. As reported previously  [5] , the HbA1c change from baseline to week 26 was analyzed using a return to baseline multiple imputation approach for missing data, followed by an analysis of covariance (ANCOVA) model including treatment group, the randomization strata of geographical region (Indian, non-Indian) and type of diabetes (T1D, T2D), subgroup [prior use of NN-Mix (yes, no)] and subgroup-by-treatment interaction as fixed effects, and the baseline HbA1c value as a continuous fixed covariate.
In addition, the sponsor study team remained blinded to the treatment group until database lock. As reported previously  [5] , the COVID-19 pandemic also occurred during the last few weeks of the study in some countries. Systems were put in place to ensure participants' safety, retention, and data capture. The impact of COVID-19 on the study results was kept to a minimum and restricted to the primary efficacy endpoint with limited impact on safety data (mainly underreporting of hypoglycemia events, similarly in both treatment groups) but with no overall impact on difference between treatments groups in relation to safety or immunogenicity. No analyses of the COVID-19 impact were performed by prior use of premix insulin.
ysis plan, and data set specifications. Participant level data will be anonymized, and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi's data sharing criteria, eligible studies, and process for requesting access can be found at: https://www. vivli. org/. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
ACKNOWLEDGEMENTS The authors thank the participants and investigators who took part in this trial. Funding. The trial and the Rapid Service Fee were funded by  Sanofi. Sanofi participated in trial design, data collection, and performed all the statistical analyses, the results of which were reviewed by the authors and the sponsor. Medical Writing and Editorial Assistance. Editorial support was provided by  DJ Quinlan of Oberon Ltd (London, UK) , funded by  Sanofi. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work, and have given their approval for this version to be published. Author Contributions. Karin Wernicke-Panten ,  Suzanne Pierre ,  Baerbel Rotthaeuser , and  Daniel Kramer  contributed to the conception and design of the study;  S.
Epidemiology and risk factors Reporting in the literature of PH incidence data at the global level is poor. In the UK, a prevalence of 97 cases per million with a female:male ratio of 1. 8 has been reported. The age-standardized death rate in the USA ranges between 4. 5 and 12. 3 per 100,000 population. Comparative epidemiological data on the prevalence of the different groups of PH are not widely available, but it is clear that LHD (group 2) is believed to be the most common cause of PH, although severe PH is relatively uncommon in this setting. Although patients belonging to groups 2 and 3 represent an important part of the clinical practice, there is disproportionately little information about the demographics and clinical course of this segment of the PH population, suggesting that registry database methodology may be useful for these groups.
In a recent multicentre, placebo-controlled trial  [397] , 201 patients with PH due to systolic heart failure were randomized in four arms comparing three doses of riociguat (0. 5, 1 and 2 mg t. i. d. ) with placebo over 16 weeks. No effect on the primary endpoint (a change in PAPm after 16 weeks) was observed at any dose of riociguat compared with placebo  [397]. Two multicentre clinical trials in PH-LHD are currently under way with sildenafil [SilHF (NCT01616381)] and macitentan [Melody-1 (NCT02070991)], the latter being the only study requiring RHC validation. Therefore there is no new evidence supporting the use of PAH therapies in PH-LHD, due in part to the absence of studies specifically stratifying patients for PH and/or targeting this specific condition. Such trials are necessary to address this area of unmet medical need and patients with Cpc-PH should be included in clinical trials.
Splicing mutations at the canonical splice sites have also major impact on the expression of CFTR, as they affect the sequences that define exon-intron boundaries. The 5 splice site (sequence CAG/GUAAGU) and the 3 splice site (sequence NYAG/G) are recognized by components of the spliceosome, and changes in these sequences affect interactions that regulate intron removal. Thus, mutations located at + 1 and + 2 residues at the 5 donor splice site and at -1 and -2 residues at the 3 acceptor splice site usually lead to single exon skipping. From the 230 splicing alterations described in the CFTR Mutation Database, about 100 are at the canonical splice sequences at positions +/-1 or 2 and thus likely to have major impact. As with large indels, these mutations also abolish the production of full length CFTR mRNA and therapeutic strategies directed at rescuing mRNA have limited hypothesis of success -thus, the focus should be to act either at the gene level or by circumventing CFTR.