dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, the strategy was subdivided into the core strategy for ISES Europe with strategic objectives and actions for the WG on models (see Fig. 1  and below), and recommendations to other stakeholders (section "Recommendations to stakeholders for advancing exposure modelling in Europe"). Improvement of existing models and tools (SO-1). Model evaluation and generation of measurement data (for model development and evaluation) are crucial for model improvement. ISES Europe should assist in organising the peer review of models or, in cooperation with the respective model/tool developers, provide guidance for self-review and documentation. For this, ISES Europe should bring together the general scientific community, model developers and the regulatory bodies for developing evaluation criteria for testing models and tools.
This includes defining appropriate quality criteria for the measurement data, and is therefore an area for cooperation with the ISES Europe WG on data repositories. Model evaluation should be conducted, for example, under the auspices of an independent expert panel, whose members are free of competing interests. On these grounds, some tool owners have already established a supervising board (e. g. ConsExpo and Stoffenmanager®  [89] ), but for many models, such an independent expert panel still needs to be defined. A commented model inventory. The quality of exposure modelling is influenced not only by the model, but also by the model use. Hence, a lack of quality in exposure modelling may be caused by the user not being aware of the available models, their features and applicability domains.
In order to facilitate a good overview on models, the ISES Europe WG on exposure models initiated the creation of the ISES Europe model inventory, introduced in section "ISES Europe exposure model inventory". This is planned to be a living document hosted on the ISES Europe website, which may later be developed into an online database. As a final aim, we intend to summarise a tiered set of exposure models  [90]  for the assessment of exposure situations. This will be further developed into a decision matrix for selecting models for a specific decision context in a tiered approach. The ISES Europe Model WG as a platform for scientific exchange for exposure modellers. With computational power growing, the integration of models for human and environmental exposure assessment is becoming achievable.
The analysis and use of current and available data can improve existing models and tools or initiate new developments for models and tools in areas where they are missing. In addition to the improvement of existing or the development of missing models and tools, further needs have been identified above. These primarily address the need for harmonisation and independent evaluation of models and tools but also modelling parameters. For example, a database may be beneficial. This database should include source parameters for different equipment, devices and techniques. Additionally, an independent platform could include agreed default parameters for specific scenarios from different regulatory frameworks. Currently, several bodies exist in parallel that could serve as an independent platform.
g. neglected in all standard models, effect relevant parameters turbulence) often unclear → research need Development of new methodologies, support for understudied research fields (SO-2) Development of read-across Agreed methodology Develop criteria for high- strategies using models Data (with an agreed minimum quality data, that can be used standard) for read-across for read-across Use of Data Access to relevant databases Cooperation with WG Data, Compatibility of databases (e. g. by promote FAIR principle using a common identifier) Harmonisation of data dictionaries and quality criteria of data.
These tools support equivalence properties  [13, 6] , although they remain limited to a restricted form of equivalence, called diff-equivalence. Some e-voting schemes have been analysed with these automated tools in the symbolic model, e. g. the Neuchâtel  [20]  or BeleniosVS  [19]  protocols. Proverif even has an extension called ProSwapper  [14] , that specifically handles swapped branches that typically occur in the SWAP definition. These tools have proved very helpful for the study of e-voting systems. However, they still suffer from limitations that restrict their applicability, as they e. g. cannot handle homomorphic encryption, or manipulate lists of arbitrary size to encode the bulletin board, and tend to quickly run into performance issues when the number of agents in parallel increases.
Data sources and data collection Patients registered with a first diagnosis of indolent FL between 2007 and 2014 were identified through the Swedish Lymphoma Register (SLR). Discordant and primary cutaneous FL patients were not included. The SLR was initiated in 2000 and has a coverage above 95% compared to the nationwide Swedish Cancer Register. 27  The SLR records detailed data on patient and clinical characteristics at diagnosis, first-line treatment, and progression. For this study, the register data were validated and supplemented with data on progression, transformation, and second-line therapies from a medical chart review. Informed consent was enquired for all patients alive by October 18, 2018, except in 2 Swedish regions where the ethical permission allowed for complementary data collection without active consent (Skåne and Uppsala).
Statistical methods Frequencies and proportions of demographical, clinical, and follow-up characteristics were calculated by type of firstline treatment. Chi-square tests were used to test difference in distribution between categorical variables. Among patients with POD, the proportion of patients where POD (and POD24) was represented by a transformation was calculated by first-line treatment. Follow-up ended at date of death due to any cause, or end of study period, whichever came first. End of study period depended on when the data collection was performed, and varied between December 31, 2018, and December 31, 2020. Follow-up was restricted to the first 10 years following diagnosis. The POD rate was predicted by first-line treatment type from a univariable flexible parametric survival model allowing for non-proportional effects, with 4 degrees of freedom for both the baseline rate and the time-dependent effect.
Given these concerns, we ask which of these statements is best supported by our current understanding of the physical climate system? Both refer to a physical science perspective, and are not restricted to specific scenarios. SR1. 5's and AR6's scenario databases both show that the bulk of scenarios halting warming in the middle of the 21st century reach net zero CO 2 emissions, which then become net negative, along with declining net non-CO 2 RF  (Huppmann et al. , 2018). But most of these scenarios deliver net zero GHG emissions (evaluated with GWP 100 ) later in the century, achieving a "peak and decline" global temperature pathway. Here we focus on the physical requirements for stabilizing temperatures, a necessary condition for limiting warming to any level. Since the transient climate response to cumulative CO 2 emissions parameter (TCRE; defining the linear relationship between cumulative CO 2 emissions and resultant warming) is broadly scenario independent  (Jenkins et al.
Acknowledgments SJ acknowledges  NERC  Grant  NE/ L002612/1  and support from the  European Space Agency's Climate Change Initiative. SJ, GP, TLF, PF and MA acknowledge funding from the  4C  project under the European Union's  Horizon 2020 research and innovation program  with Grant 821003. TLF also acknowledges funding from the  Swiss National Science Foundation  (No. PP00P2_198897 ).
Since the 1960s, several drugs have been linked to the onset or aggravation of pulmonary arterial hypertension (PAH): dasatinib, some amphetamine-like appetite suppressants (aminorex, fenfluramine, dexfenfluramine, benfluorex) and recreational drugs (methamphetamine). Moreover, in numerous cases, the implication of other drugs with PAH have been suggested, but the precise identification of iatrogenic aetiologies of PAH is challenging given the scarcity of this disease and the potential long latency period between drug intake and PAH onset. In this context, we used the World Health Organization's pharmacovigilance database, VigiBase, to generate new hypotheses about drug associated PAH. Methods: We used VigiBase, the largest pharmacovigilance database worldwide to generate disproportionality signals through the Bayesian neural network method.
1, 4 5] [6] [7]  However, given the scarcity of this pathology and the potential long latency period between drug intake and PAH onset, identifying iatrogenic aetiologies is challenging. In this context, mining large pharmacovigilance databases constituted from spontaneously reported cases of suspected adverse drug reactions (ADRs) may be an interesting way to identify drugs possibly linked to PAH. 8  In this study, we used the largest pharmacovigilance database worldwide, the WHO's pharmacovigilance database, to generate disproportionality signals. All disproportionality signals were further reviewed and assessed for plausibility by experts in pulmonary hypertension, pharmacovigilance and vascular pharmacology.
| Disproportionality analysis in the WHO pharmacovigilance database The study was an observational, post-marketing study using the ADRs reported in the WHO pharmacovigilance database, VigiBase. At the date of extraction (June 2020), >22 million of cases were reported in this database. Reports were collected from among the 134 countries participating in the WHO Programme for International Drug Monitoring since 1968. Disproportionality analyses constitute a set of methods largely used by researchers and regulatory/drug agencies to generate disproportionality signals, i. e. putative links between drugs and ADRs. 9 In this analysis, we used the Bayesian neural network method developed by the Uppsala Monitoring Centre research team, which displays the best sensitivity and specificity among disproportionality analyses, notably for rare events.
| Population, cases, exposure and outcomes We first extracted from VigiBase all suspect individual case safety reports (ICSRs) of PAH mentioning the MedDRA Preferred Term "Pulmonary arterial hypertension". To ensure that drugs exposures were preliminary to the onset PAH, all ICSRs containing a drug used to treat PAH (sitaxentan, bosentan, ambrisentan, macitentan, riociguat, sildenafil, tadalafil, epoprostenol, treprostinil, iloprost, selexipag and nitric oxide), a drug used in conditions associated with PAH (HIV, systemic scleroderma, portal hypertension, congenital heart disease or schistosomiasis) or an antecedent of PAH were excluded (Table  S1 ). 13 All remaining ICSRs were included in the disproportionality analysis using the whole database as comparator (first analysis). We anticipated that drugs already known to induce PAH (i.
What is already known about this subject • To date, the link between dasatinib, amphetamine-like appetite suppressants or methamphetamine and the onset of pulmonary arterial hypertension has been demonstrated. • Numerous preclinical and clinical studies have suggested a link with other drugs, but evidence is lacking. and methamphetamine) would be associated with a significant number of reports, due to extensive media coverage, and could therefore cause a competition bias (or masking effect). 1  Indeed, drugs associated with large number of reports may hide the signals for other drugs. 14, 15 mpetitors were identified using the competition index at the PT level with a cut off at 5%. 16  Thus, in a secondary analysis, such drugs were removed from the dataset and we re-ran disproportionality analyses (second analysis).
| Causality assessment We performed a literature search to identify plausible pharmacological mechanisms for hitherto unsuspected drugs using drug names and "pulmonary arterial hypertension", "pulmonary hypertension", "vascular remodelling" and "vasoconstriction" in PubMed. All drugs were then independently assessed for plausibility by each of the 5 authors (C. K. , J. L. C. , M. R. , M. C. C. and D. M. ) using categories adapted from the WHO causality categories (very likely/ probable/possible/unlikely). 17 Criteria used to judge for causality were the signal strength (number of cases, robustness in sensitivity analyses), time to onset, evolution, patient characteristics (age, sex, underlying pathology), concomitant drugs and plausible pharmacological mechanisms. The data were synthesized and discrepancies resolved through discussion among the team.
Preclinical experiments are a valuable tool to further study drug contributions to PAH pathogenesis. T development and the extrapolation of results obtained in animal experiments is, however, challenging in PAH, due to the absence of animal models that reproduce the full spectrum of the human disease, and due to interspecies differences in drug metabolism and response. 19  For example, several experiments failed to reproduce PAH with well identified drugs such as aminorex or fenfluramine. u , 31  Furthermore, the considerable advances in bioinformatic and machine learning in recent years makes it possible to mine drug, disease or -omic databases to find new links between drugs and PAH. 32, 33  Combining these data with PAH registries, pharmacovigilance databases or epidemiological studies may represent promising approaches to discover common pathways in drug-associated PAH and to predict pulmonary vascular toxicities of newly marketed drugs.
[34] [35] [36] [37]  Risk assessment and identification of susceptible individuals are challenging in multifactorial and rare diseases such as PAH. Indeed, even in larger nationwide healthcare databases, power is insufficient to perform robust pharmacoepidemiological studies in PAH. However, the recent advances in common data models to create platforms allowing international collaborations across different databases at a regional scale, such as the Exploring and Understanding [44]  The ICSRs aggregated in the WHO pharmacovigilance database are spontaneously reported by healthcare professionals and patients in 134 countries around the world. This system increases power to detect scarce safety signals, but also suffers from heterogeneity in reported cases, regional drugs utilization and pharmacovigilance systems.
Until then, we advise clinicians to be particularly attentive to these drugs when faced with PAH and to report cases to the pharmacovigilance systems. In conclusion, we have found several drugs that were previously not known to be associated with increased reporting of PAH in the WHO pharmacovigilance database. Notably we identified new protein kinase inhibitors, one angiogenesis inhibitor, several chemotherapeutics and antimetabolites, and several immunosuppressants, most of which have a plausible pharmacological mechanism. Although confirmatory studies need to be performed, the signals identified may help clinicians envisage an iatrogenic aetiology when faced with a patient who develops PAH.
20 mg/day, lorlatinib with a daily dose of 100 mg and ruxolitinib with doses >20 mg/day. 4 | DISCUSSIONTo our knowledge this is the first study that systematically retrieved and assessed all cases of PAH reported in a large pharmacovigilance database. Our approach allowed us to identify all drugs already known to induce PAH and 43 new signals, of which 15 were considered probable and 28 possible. Notably, we identified 4 protein kinase inhibitors (lapatinib, lorlatinib, ponatinib and ruxolitinib), 1 angiogenesis inhibitor (bevacizumab), several antimetabolites (cytarabine, fludarabine, fluorouracil and gemcitabine) and chemotherapeutics (etoposide and trastuzumab) and some immunosuppressants (leflunomide, thalidomide and ciclosporin). Such signals represent plausible ADR if one considers our current understanding of iatrogenic PAH, biological and pharmacological drug activity and reported case characteristics.
ACKNOWLEDGEMENTS We thank  VigiBase  for giving us access to the data. We also thank  Dr Alison Foote  ( Grenoble Alpes University Hospital ) for critically editing our manuscript. The data supplied to VigiBase come from a variety of sources and the likelihood of a causal relationship is not the same in all reports. The information does not represent the opinions of the UMC or the  World Health Organization. This work has been partially supported by  MIAI @ university Grenoble Alpes , ( ANR-19-P3IA-0003 ).
Methods The present study enrolled 108 patients with HF aged 60 years or older in pharmacies in the northeast region of France between August 2019 and September 2020. HF diagnosis was defined by the guidelines of the European Society of Cardiology  [5]. Based on our algorithm, we included patients that had either a history of HF hospitalization, which was registered by the national social security system, or were treated with angiotensin receptor neprilysin inhibitor, which was the specific treatment for HF. This study used completely anonymized data acquired within routine practice and consequently did not require ethics committee approval. Pharmacists interviewed all patients with a short questionnaire, focusing on HF within an HF awareness initiative. The questionnaire assessed their knowledge regarding disease progression, adherence to treatment, education, and how to self-manage signs and symptoms of congestion (Supplementary Figure  S1 ).
Study design and participants CKD-REIN is a prospective cohort study conducted by 40 nationally representative nephrology outpatient facilities in France. Details of the study protocol have been published  [14]. Briefly, the main inclusion criteria are ≥18 years, a confirmed diagnosis of moderate or advanced CKD, an eGFR <60 mL/min/1. 73 m2 and the absence of dialysis or transplantation. Between July 2013 and April 2016, 3033 patients were enrolled during a routine nephrology outpatient appointment and are actively followed up for 5 years at most. The recruitment of outpatients ensured that the study participants did not present with acute illness (such as acute kidney injury or digestive tract haemorrhage) on inclusion. Furthermore, all patients were receiving stable background therapy. The study protocol was approved by the institutional review board at the French National Institute of Health and Medical Research (INSERM; reference: IRB00003888) and was registered at ClinicalTrials.
gov (NCT03381950). For the purposes of the present analysis, we excluded patients with missing data for the serum urea level at baseline or who had an aberrant serum urea value (i. e. <2. 5 or >100 mmol/L). To limit the effects of nonrenal factors on serum urea levels, patients who were on corticosteroids or had a history of metastatic cancer were excluded. Patients who had an eGFR <15 mL/min/1. 73 m2 at baseline were also excluded, considering that they had reached the stage of kidney failure (Figure  1 ). A total of 2507 patients were analysed here.
Study data Trained clinical research associates (CRAs) collected data from patient interviews and medical records at baseline and then annually. The patients' characteristics (age, sex and smoking status) were recorded and the patients were screened for a history of hypertension, diabetes, CVD or acute kidney injury (definitions in Supplementary data, Table  S1 ). Blood pressure was measured, as were height and weight to calculate body mass index (BMI). All patients were prescribed a set of standard blood and urine tests (recommended by French health authorities for routine CKD care), with sample to be taken at their usual medical laboratory. Data on the serum creatinine, serum urea level, blood haemoglobin, serum albumin and albumin-or protein-to-creatinine ratio (Supplementary data, Table  S1 ) were recorded.
The GFR was estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation and the isotope dilution mass spectrometry-traceable creatinine concentration determined in a Jaffe assay (in 38% of participants) or an enzymatic assay (in 57%; the assay type was unknown for 5%)  [15]. Patients were asked to bring all their drug prescriptions for the preceding 3 months to the enrolment visit. The CRAs used an electronic case report form [linked to the international Anatomical Therapeutic and Chemical (ATC) thesaurus] to enter standardized ATC codes. Longitudinal clinical data (nephrology consultations, hospital admissions, laboratory test results, medications and any transition to dialysis or transplantation) were collected every year from medical records and patient interviews.
The secondary outcomes included first major CV events (any CV death, myocardial infarction, stroke and hospitalization for heart failure) and all-cause death. Deaths before RRT were identified from medical records or reported by family members at the annual follow-up visit. All the considered events occurred before RRT. RRT was defined in the present study as kidney dysfunction requiring initiation of chronic dialysis or preemptive transplantation (as identified from medical records or by linking to the French National Kidney Failure Registry)  [17]. CV events were available for the first 3 years of follow-up at the time of analysis and so data were censored on the date of the patient's third annual follow-up visit, the date of the last followup before the third annual visit, the date of RRT or the date of death from a non-CV cause.
As a sensitivity analysis, we built a cause-specific Cox model for each type of CVD outcome, i. e. an analysis of (i) fatal and nonfatal atheromatous CVD events and (ii) fatal and nonfatal non-atheromatous CVD events. Missing covariate data were managed by multivariate imputation by chained equations (MICE)  [18]. The assumption whereby data were missing at random was plausible. By using the MICE package in R statistical software (version 4. 0. 3) [19] , we created 50 datasets (20 iterations). All covariates present in the Cox models and baseline serum bicarbonate were included in the imputation model. Fitted Cox models were generated for each dataset and pooled regression coefficients were obtained using Rubin's rules. All statistical analyses were performed with R software.
Background: In biomedical, life or environmental science research, two different strategies exist depending on the starting point of the researchers: "what makes us ill? " or "what makes us healthy?". Indeed, a risk-based strategy (RBS) attempts to minimize risk factors increasing the likelihood of developing a disease, while an asset-based strategy (ABS) attempts to promote and strengthen the factors that support good health and wellbeing. We provided an up-to-date overview of both research strategies in peer-reviewed scientific literature, in the fields of human health, animal and plant health and ecosystem health, to fit with the One Health framework. More particularly, we focused on human health by studying publications related to the COVID-19 at the beginning of the pandemic. Design: A rapid review of research science literature was carried out to identify in the PubMed/MEDLINE database the proportion of peer-reviewed articles adopting either a RBS or an ABS, in the main global environment fields from January 01, 1900 to December 31, 2019 and, related to COVID-19, from December 1, 2019 to May 31, 2020.
It seems highly likely that the two strategies we have analyzed can now be chosen in such a way as to promote a balance in public health measures, at every level: the individual  [68] , the city  [69] , the hospital  [70] , the health system  [71]  and even the country  [72]  as well as the planet  [26]. To guarantee the rigor of the study, we constructed several search equations on the PubMed ® database using MeSH terms. This strategy enabled an accurate, relevant and representative search in all languages through a controlled vocabulary system and standardized indexation  [42]. However, our search strategy based on the PubMed ® database limited us to relevant literature on life and biomedical sciences  [73]. We did not consider the Web of Science database, which is nonetheless largely integrated in the PubMed ® database.