dataesr/scientific-paragraphs-categorization huggingface dataset:
Patient population All consecutive patients undergoing lumbotomy or robotic PN at our institution from November 2014 to April 2017 were prospectively included in our database. A consent was obtained for each patient operated on by kidney surgery in our department. PN was completed after a hilar or parenchymal clamping, with classical resection of the tumor and renorrhaphy. Collected data included patient and renal tumors characteristics, perioperative data and postoperative follow up. The tumor size was classified according to the TNM and to the RENAL score (0-4 cm, 4 cm and ⩾7 cm). In addition, surgical approach, occurrence of hematuria or postoperative hemorrhage, type of postoperative radiological examination, the ultrasound diagnosis of a PA-AVF and the delay before the onset of hemorrhagic complication were assessed.
Statistics Continuous variables are described using means and standard deviations (SDs) or median and interquartile ranges (IQRs). Nominal data were expressed as counts and percentages. We compared means and proportions between groups using a Student's t test (or Mann-Whitney test if appropriate) and Fisher's exact test. Logistic regression was applied to quantify the odd ratios (ORs) and the 95% confidence intervals (CIs) for the risk of postoperative hemorrhagic complication. The final multivariable regression model was obtained by entering the risk factors from the univariate model that achieved p ⩽ 0. 20 as the thresholds in a single multivariable regression model. Factors with a p value ⩽0. 05 were considered as independently associated with the risk of postoperative hemorrhagic. All analyses were performed using R using GPU ® software and Stata ® version 15.
In our study, no prophylactic angioembolization has been performed for asymptomatic PA, 9] 12  Of note, because data were collected prospectively, our study reports all hemorrhagic events including pauci-symptomatic hematoma with spontaneous resolution and blood transfusion whereas most authors mainly focus on HCs requiring surgical or radiologic intervention. 16, 20 e to its prospective design and its significant sample size, our cohort enabled us to conduct univariate and multivariate analysis. Tumor size,.
Design International cohort study. setting Three cohorts including kidney transplant recipients from 10 academic medical centres from Europe and the United States. ParticiPants Derivation cohort: 4000 consecutive kidney recipients prospectively recruited in four French centres between 2005 and 2014. Validation cohorts: 2129 kidney recipients from three centres in Europe and 1428 from three centres in North America, recruited between 2002 and 2014. Additional validation in three randomised controlled trials (NCT01079143, EudraCT 2007-003213-13, and NCT01873157).
17 he objectives of this study (NCT03474003) were to develop a practical risk stratification score in a multicentre, prospective cohort of kidney transplant recipients that could be used to identify patients at high risk of future allograft loss; to validate the score on a large scale in geographically distinct independent cohorts with different allocation policies and types of transplant management; and to test the performance of the risk score for predicting graft failure in randomised controlled trials covering distinct clinical scenarios of transplant.
Methods study design and participants Derivation cohort. The derivation cohort consisted of 4000 consecutive patients over 18 years of age who were prospectively enrolled at the time of transplantation of a kidney from a living or deceased donor at Necker Hospital (n=1473), Saint-Louis Hospital (n=928), Foch Hospital (n=714), and Toulouse Hospital (n=885) in France between 1 January 2005 and 1 January 2014. We excluded patients with grafts that never functioned (primary non-functioning grafts; n=116). The clinical data were collected from each centre and entered into the Paris Transplant Group database (French data protection authority (CNIL) registration number: 363505). All data were anonymised and prospectively entered at the time of transplantation, at the time of post-transplant allograft biopsies, and at each transplant anniversary by using a standardised protocol to ensure harmonisation across study centres.
Datasets from the validation centres were prospectively collected as part of routine clinical practice, entered in the centres' databases in compliance with local and national regulatory requirements, and sent anonymised to the Paris Transplant Group. In France, the transplantation allocation system followed the rules of the French National Agency for Organ Procurement (Agence de la Biomédecine). The European centre outside France (Leuven) followed the rules of the Eurotransplant allocation system (https://www. eurotransplant. org), and the US centres (Johns Hopkins Hospital, Mayo Clinic, and Virginia) followed the rules of the US Organ Procurement and Transplantation System (https://unos. org/).
Post-transplant risk evaluation times Risk evaluation after transplantation was conducted at the time of allograft biopsy performed for clinical indication or as per protocol, which was performed after transplantation according to the centres' practices. In patients with multiple biopsies, risk evaluation used the date of the first biopsy. The distribution of post-transplant risk evaluation times is provided in supplementary figure A. Risk evaluation after transplant comprised demographic characteristics (including recipients' comorbidities, age, sex, and transplant characteristics), biological parameters (including kidney allograft function, proteinuria, and circulating anti-HLA antibody specificities and concentrations), and allograft pathology data (including elementary lesion scores and diagnoses).
For patients who died with a functioning allograft, allograft survival was censored at the time of death as a surviving or functional allograft. 22 n the derivation cohort, we used univariable Cox regression analyses to assess the associations between allograft failure and clinical, histological, functional, and immunological factors measured at the patient's risk evaluation (see above). We used the log graphic method to test hazard proportional assumptions. The factors identified in these analyses were thereafter included in a final multivariable model. We confirmed the internal validity of the final model by using a bootstrap procedure, which involved generating 1000 datasets derived from resampling the original dataset and permitting the calculation of optimism corrected performance estimates.
Prediction model performance in internal and external validation cohorts We first internally validated the final multivariable model via a bootstrapping procedure with 1000 samples from the original dataset of the derivation cohort (supplementary methods). Using this approach, we confirmed the robustness of the final multivariable model: the internal validity of the final model using a bootstrap procedure, which involved generating 1000 datasets derived from resampling the original dataset, thus permitting the calculation of optimism corrected performance estimates. Models were fitted for each of the 1000 samples by using backwards elimination. The eight independent predictors identified in the final multivariable Cox model were replicated in more than 85% of the 1000 estimated models. We also confirmed the discrimination ability of the model at three, five, and seven years (C index 0.
The main limitations to achieving a robust and validated scoring system depend on multiple factors including the insufficient data quality of the previously  studied cohorts and the fact that no registry or database system has been primarily designed to tackle the specific aspect of prognostication. An even more important aspect is external validation in different populations, which prompted us to conduct a large external validation in multiple centres worldwide. Despite some expected loss of discriminative performance, models are typically considered useful for clinical decision making when the C statistic is greater than 0. 70 and strong when the C statistic exceeds 0. 80, suggesting that the iBox risk prediction score could support decision making. 28 For prognostication systems in other fields such as oncology (for example, locally advanced pancreatic cancer and metastatic colonic cancer), the C index is typically closer to 0.
Another limitation is that information on the adherence to drug treatment of individual patients was lacking in our dataset. Although non-adherence is inherently difficult to capture, especially at a population level,  30  the iBox score, because its mechanistically informed design could likely capture the consequences of non-adherence (development of de novo donor specific anti-HLA antibodies, allograft injury, scarring, inflammation, and diminished glomerular filtration rate). Although the iBox risk prediction score was primarily generated using a large, prospective, unselected cohort, a prospective validation of the iBox in daily clinical practice remains desirable. Finally, despite the validation of the iBox risk prediction score in an interventional setting, future trials are needed to determine whether a strategy based on a systematic risk evaluation compared with an empirical approach might improve clinical management.
ibox risk prediction score performance when assessed in different clinical scenarios and subpopulations Histological diagnoses defined by last update of Banff international classification: antibody mediated rejection, T cell mediated rejection, BK virus nephropathy, primary nephropathy recurrence. †Highly sensitised patients defined by panel of reactive antibodies >90%. ‡Donor's age, donor's sex, donor's hypertension, donor's diabetes, recipient's age, recipient's sex, human leukocyte antigen (HLA) mismatches, retransplantation, and anti-HLA DSA at time of transplantation. §Status was retrieved in US participating centres' databases (no ethnicity data allowed in French development cohort database according to the French law and regulation). African-Americans in US validation cohort represented 390 (27.
Live imaging and cell tracking Bright-field live imaging of beating gastruloids was performed with a Nikon Ti inverted microscope equipped with an incubation chamber at 37°C, 5%CO2. Light-sheet live imaging of Flk1-GFP and Mesp1-GFP gastruloids was performed with a prototype of LS1 live inverted light-sheet microscope (Viventis Microscopy Sarl, Switzerland), at 37°C, 5% CO 2. A volume of 150-200µm was acquired with a Z spacing of 2-3µm between slices and pictures were captured every 20 min for Flk1-GFP gastruloids and every 10 min for tracking of Mesp1 + cells. Flk1-GFP light-sheet video montages were obtained with the Arivis Vision4D software. To track Mesp1 + cells in Gastruloids from 96 to 120 h, LS1 live light-sheet images were processed with the Fiji Mastodon plugin, using a semi-automatic tracking.
For light-sheet imaging (Fig. 4 ), samples were mounted in 1% low-melt agarose and cleared overnight with CUBIC mount solution  (Lee et al. , 2016). Light-sheet imaging was performed on a Zeiss Light-sheet Z1 microscope equipped with a Plan-Neofluar 20x/1. 0 Corr nd=1. 45 objective. Light-sheet images were further processed with Imaris software, for 3D rendering and surface generation.
Crescent analysis Analysis of crescent geometrical properties was performed using a custom-made Matlab script for Imaris files with a Imaris XT feature and EasyXT. Initially, we generated artificial shapes to be used as reference for defined geometrical metrics. Using a custom script (crescent_generator. m), 3D objects were generated to mimic crescent structures with different characteristics. To do so, the script creates two spot objects, makes channels from these spots and finally subtracts one channel to the other. To analyze surfaces, 3D images from non-beating organoids at 144 h and beating organoids at 168 h were acquired with a Light-sheet Z1 microscope (Zeiss) after clearing, as described above. For each individual stack, a surface was created using a dedicated user interface (GUI_DetectAndAnalyze.
m), defining the object that should be created for each channel (settings_. m). Due to variability in background and signal intensity, the threshold (absolute intensity) was adjusted manually for each surface. Using a custom script (makeCroissantMeasure_final. m) geometrical measurements were computed and the results exported in csv table. In the graph, we plot the measure of Spareness, which is described as the ratio between the volume of the object and the volume of the best fitted ellipsoid. All scripts and settings used for analysis are available at go. epfl. ch/Crescent_Analysis.
Statistics All data shown in column graphs are expressed as mean ± SD, apart from the graph showing Calcium spikes frequency, which is expressed as mean ± whiskers from min to max. All other graphs show single data points. Statistical analysis between two columns was performed using two-tailed unpaired Student's t test, whereas data containing more than two experimental groups were analyzed with one-way analysis of variance followed by Bonferroni's test. To calculate the significance of the percentage of increase over baseline frequency after Isoproterenol administration, we applied a one sample t test. Statistical significance was calculated using the Graphpad Prism software, that was also used to generate all graphs. *P < 0. 05; **P < 0. 01; ***P < 0. 001; confidence intervals 95%; alpha level 0.
Acknowledgements We thank  Alfonso Martinez Arias  for great support throughout the project. We thank  Denis Duboule  and  Nadia Mercader  for useful feedback on the manuscript. We thank members of the  Lutolf laboratory  for discussions and sharing materials,  Cédric Blanpain  ( ULB ) for providing Mesp1-GFP cells,  Alexander Medvinsky  ( MRC Edinburgh ) for providing Flk1-GFP cells,  Andy Oates  (EPFL) and  Peter Strnad  ( Viventis Sàrl ) for making light-sheet LS1 microscope available and for technical support,  Olivier Burri  for EasyXT development,  Thierry Laroche  for support with Z1 light-sheet imaging,  Arne Seitz  and other members of  Bioimaging and Optics Facility (EPFL)  for microscopy support,  Jessica Sordet-Dessimoz  and  Gian-Filippo Mancini  from  Histology Core Facility  for RNAscope, and all personnel of  Histology Core Facility, Flow Cytometry Core Facility and Gene Expression Core Facility (EPFL)  for their technical support.
Defects attributable to drug therapy actually represent about 1% of congenital defects of known etiology  [15]. The large number of exposed women is of concern, but also represents an opportunity to evaluate the safety of medicinal product in real life. Several countries have implemented different systems to monitor drug use  [16] ; for example electronic healthcare database collecting healthcare data have been used to evaluate the safety of medication in pregnancy. Others have collected information on birth diseases and deaths from healthcare facilities. Information, stored in various national health registers and information on prescribed drugs dispensed by pharmacies are recorded in specific drug registers. This paper describes the different data sources allowing pharmacoepidemiologic studies in pregnancy and several examples of the used methods.
Case report and pharmacovigilance databases Spontaneous reporting of adverse outcomes is an important source of information and the teratogenicity of drugs such as thalidomide, isotretinoin and more recently, mycophenolate  [17] [18] [19] [20]  have been identified through case series. Similarly, repeated feature of early reports of exposure to maternal therapy with angiotensin converting enzyme inhibitors associated with severe oligohydramnios and/or neonatal anuria alerted to the risk of those drugs in pregnancy. [21]  Thus, the occurrence of several reports of a same adverse effect in the newborns, with a same drug, supports the hypothesis of a potential teratogenicity or fetal toxicity of this drug. In the early sixties, "thalidomide drama" was the first event that has made aware of the importance of risk assessment during pregnancy.
It was closely followed by the emergence of pharmacovigilance  [22]. In subsequent years, several countries have set up a pharmacovigilance system to prevent such drug-related disasters. In 1968, the World Health Organization (WHO) International Drug monitoring Program was implemented to identify the earliest possible pharmacovigilance signals. The programme now has more than 110 member countries from all parts of the world contributing individual case safety reports (ICSRs) to the WHO global ICSR database system: VigiBase®  [23]. Nevertheless, pharmacovigilance often appears inadequate to identify the magnitude of the risk of medication in pregnancy because of under-reporting  [24]. What is reported may be considered as the tip of the iceberg compared to what we do not know. In addition, the total number of pregnant women exposed to the drug is often missing and the level of the risk cannot be quantified.
ENTIS also pools data from each TIS to carry out health monitoring. Often, pharmacovigilance centres are drug information centres and have an activity similar to TIS. For example, French pharmacovigilance centers answering questions about pregnant women contribute to Terappel database which contains pregnancy follow-ups and participate to the ENTIS  [26]. All pregnant women and healthcare professionals have access to TIS. They voluntarily contact them for information about one (or more) drug safety at a specific time of pregnancy. Data collection is carried out prospectively, insofar as the outcome of pregnancy is not known on the first call. The caller answers questions regarding age, medical and family history, pregnancy disease, medical treatment, drugs and other substances exposure and antenatal diagnosis.
The patient or her doctor is then contacted shortly after the expected date of delivery to get information on the actual taking of medicines after the first call and the pregnancy outcome. Several studies have been performed with these data. For example, a study was conducted to assess the embryotoxic potential of methotrexate treatment at dosages typically used in the treatment of rheumatic diseases. Data on methotrexate-exposed and comparison women were obtained from pregnancies identified by members of the ENTIS, as well as from the Organization of Teratology Information Specialists (OTIS) [a similar network in US and Canada]. The main result is that post-conception administration of methotrexate was associated with an increased risk of major birth defects and spontaneous abortion. Such evidence was not found after pre-conception administration  [31, 32].
The strength of TIS is that patients and healthcare professionals who call them are motivated responders because they get information for free. Many data are collected, including over the counter drugs intake and many potential confounding variables. In most cases, requests involve drugs poorly evaluated during pregnancy, which is a good way to increase knowledge on them. Moreover, prospective data collection minimizes recall bias. TIS constitute a valuable tool to precisely document courses of pregnancies under treatment with rare drugs or those suspected to be developmentally toxic. Data can also be evaluated through observational cohort studies comparing the occurrence of a specific outcome between a group of pregnant women exposed to a drug suspect to be embryotoxic or foetotoxic and a non-exposed group.
Congenital anomaly registries Population-based registries of children born with congenital malformations were implemented after the "thalidomide disaster" in order to detect teratogenic potential of new drugs as soon as possible. Registries continuously and comprehensively collect nominative data relevant to congenital malformations in a defined geographical area. Data collection is carried out retrospectively once the pregnancy outcome is known. To that end, field agents perform an active and systematic review of medical records. Registries provide a systematic epidemiological surveillance of birth defects since they detect unexplained variations in the frequency of malformations. In 1979, the European Surveillance of Congenital Anomalies (EUROCAT) was implemented to develop a health monitoring system of congenital anomalies.
Different types of healthcare databases The limitations of previous data sources and the rather recent availability of health or administrative databases encouraged the emergence of electronic healthcare databases as data sources for pharmacoepidemiologic research. There are two types of databases: those using data from medical records and those using administrative claims for drug reimbursement that can be augmented/improved with medical data. Obviously, a database that can be used for risk assessment of medication during pregnancy must provide information on mother linked to information on her child.
Medical databases Some data sources, such as the clinical practice research datalink (CPRD) in the UK contains anonymized longitudinal medical data routinely input by the general practitioner (GP)  [38]. CPRD provides much information related to mother characteristics and disease, pregnancy, antenatal diagnosis and all types of pregnancy outcomes (live deliveries, stillbirth, spontaneous pregnancy losses and induced terminations). Maternal and children medical records can be linked using a family number. CPRD also contains data on congenital anomalies and on children health status. Mother drug exposure is captured through the prescription issued by GPs. Information on Over The Counter drug exposure might also be available in the CPRD if the mother informs her GP. In addition, data sometimes include information on lifestyle factors as for example alcohol or smoking.
Claims databases The second type of databases is claims databases. Information on drug exposure come from reimbursement data of drug prescribed and dispensed. Some of these claims databases have been augmented/improved with other types of data and/or linked to other databases that contain medical information. (e. g. national or regional birth registry data, maternal survey data). For example, the Nordic countries (Denmark, Finland, Iceland, Norway and Sweden) have pooled together information about births (livebirths and stillbirth), hospitalization, cancer diagnosis, dispensed drugs and causes of death from different registries  [39]. This is possible thanks to the personal identification number (PIN) assigned to each Nordic inhabitant  [40]. In France, the National system of health data (système national des données de santé [SNDS]) includes two health insurance databases that can potentially be used for the evaluation of medication safety in pregnancy.
The first is the système national d'information inter-régime de l'Assurance maladie (National interscheme information system of the health insurance [SNIIRAM]). It is the main health care claims database, which now covers 98. 8% of the French population. The SNIIRAM database merges anonymous information of reimbursed claims from the French health insurance system, linked to the national hospital-discharge summaries database system (programme de médicalisation des systèmes d'information  [PMSI] ) and the national death registry. The power of the database is high, but its main limit is the administrative process to access it. The second database is the échantillon généraliste des bénéficiaires (EGB). It corresponds to the 1/97th of insured persons in France, with 20-year longitudinal data. It includes the same information than the SNIIRAM.
Data sources intended for drug evaluation in pregnancy Some databases have been created for the particular purpose of assessing medication during pregnancy. For example the Quebec pregnancy cohort (QPC) gathers data on pregnancies and children born in Quebec since 1997. Information comes from the linkage of four claim databases including the régie de l'Assurance maladie du Québec (RAMQ, public health insurance) with medical and drug exposure data, the hospitalization database, the birth and death registry and the database of the Ministry of Education  [43].