dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, we aimed to identify previously unreported genetic associations with IPF to improve our understanding of disease susceptibility and generate new hypotheses about disease pathogenesis. We conducted a large GWAS of IPF susceptibility by utilizing all European cases and controls recruited to any previously reported IPF GWAS  (5) (6) (7) (8)  and metaanalyzing the results. This was followed by replication in individuals not previously included in IPF GWAS and bioinformatic analysis of gene expression data to identify the genes underlying the identified association signals. As specific IPFassociated variants have also been shown to overlap with other related respiratory traits including lung function in the general population, chronic obstructive pulmonary disease (COPD) (with genetic effects in opposite directions between COPD and IPF)  (12) (13) (14) , and interstitial lung abnormalities (ILAs) (which might be a precursor lesion for IPF)  (15) , we tested for association of the IPF susceptibility variants with these respiratory phenotypes in independent datasets.
Study Cohorts We analyzed genome-wide data from three previously described independent IPF case-control collections (named here as the Chicago  [5] , Colorado  [6] , and UK  [8]  studies; please refer to the online supplement for summaries of these collections). Two more independent case-control collections (named here as the UUS [United States, United Kingdom, and Spain] and Genentech studies) were included as replication datasets. The new UUS study recruited cases from the United States, United Kingdom, and Spain and selected controls from UK Biobank  (19)  (full details on the recruitment, genotyping, and quality control of UUS cases and controls can be found in the online supplement). The previously described  (20)  Genentech study consisted of cases from three IPF clinical trials and controls from four non-IPF clinical trials (see the online supplement).
All studies were restricted to unrelated individuals of European ancestry, and we applied stringent quality control measures (full details of the quality control measures of each study can be found in the online supplement and Figure  E1  in the online supplement). All studies diagnosed cases using American Thoracic Society and European Respiratory Society guidelines  (21) (22) (23)  and had appropriate institutional review board or ethics approval. Genotype data for the Colorado, Chicago, UK, and UUS studies were imputed separately using the Haplotype Reference , R. G. J. , and L. V. W. were responsible for recruitment, screening and genotyping of cases and controls for idiopathic pulmonary fibrosis, interstitial lung abnormalities, and gene expression analyses. J. M. O. , D. A. S. , C. F.
, I. N. , R. G. J. , and L. V. W. supervised and coordinated the study. R. J. A. , R. G. J. , and L. V. W. led the writing of the manuscript. All authors contributed to drafting and providing critical feedback on the manuscript. Data availability statement: Full summary statistics for the genome-wide meta-analysis can be accessed from https://github. com/genomicsITER/PFgenetics.
In addition, we tested to see if any of the credible set variants were associated with gene expression using three expression quantitative trait loci (eQTL) resources (the Lung eQTL study [n = 1,111] [27-29], the NESDA-NTR [Netherlands Study of Depression and Anxiety-Netherlands Twin Register] blood eQTL database [n = 4,896] [30], and 48 tissues in GTEx  [31]  [n between 80 and 491]; see the online supplement). Where IPF susceptibility variants were found to be associated with expression levels of a gene, we tested whether the same variant was likely to be causal both for differences in gene expression and IPF susceptibility. We only report associations with gene expression where the probability of the same variant driving both the IPF susceptibility signal and gene expression signal exceeded 80% (see the online supplement).
However, the lack of replication for these signals in our data suggests that further exploration of their relationship to interstitial lung diseases is warranted. By combining the largest available GWAS datasets for IPF, we were able to confirm 11 of 17 previously reported signals. Conditional analysis at the 11p15. 5 region indicated that previously reported signals at MUC2 and TOLLIP were not independent of the association with the MUC5B promoter variant. Previously reported signals at EHMT2, OBFC1, and MDGA2 were only found to be associated in one of the discovery studies and showed no evidence of an association with IPF susceptibility in the other two discovery studies. Only the 11 signals that we confirmed in our data were included in subsequent analyses. The IPF susceptibility signals at DSP, FAM13A, 7q22.
1 (ZKSCAN1), and 17q21. 31 (MAPT) have also been reported as associated with COPD, although with opposite effects (i. e. , the allele associated with increased risk of IPF being associated with decreased risk of COPD). Spirometric diagnosis of COPD was based on a reduced FEV 1 /FVC ratio. In an independent dataset   The minor allele is the effect allele, and the MAF is taken from across the studies used in the discovery meta-analysis. Definition of abbreviations: Chr = chromosome; CI = confidence interval; eQTL = expression quantitative trait loci; IPF = idiopathic pulmonary fibrosis; rsid = reference SNP cluster ID. Annotation of the variant was taken from Variant Effect Predictor (VEP). A list of all variants included in the credible sets with their annotations and eQTL results can be found in Table  E3.
A strength of our study was the large sample size compared with previous GWAS and the availability of an independent replication dataset. A limitation of our study was that the controls used were generally younger in all studies included, and there were differences in sex and smoking distributions in some of the studies. As age, sex, and smoking status were not available for all individuals in four of our datasets, we were unable to adjust for these variables without substantially reducing our sample size. However, cases and controls in the UUS and UK datasets were matched for age, sex, and smoking. The three novel signals replicated in all of the discovery and replication datasets, providing reassurance that the signals we report are robust despite differences between the datasets. As we had limited information beyond IPF diagnosis status for a large proportion of the individuals included in the studies, we cannot rule out some association with other age-related conditions that are comorbid with IPF.
However, other age-related conditions were not excluded from either the cases or controls. For the signals near KIF15 and MAD1L1, there was substantial evidence for an association with gene expression in nonlung tissues but not in either of the two (nonfibrotic) lung tissue eQTL datasets. This could reflect cell type-specific effects that are missed when studying whole tissue or effects that are disease-dependent. Finally, our study was not designed to identify rare functional variant associations. As both common and rare variants are known to be important in IPF susceptibility  (39) , this is a limitation of our study. In summary, we report new biological insights into IPF susceptibility and demonstrate that further studies to identify the genetic determinants of IPF susceptibility are needed.
is an  Action for Pulmonary Fibrosis Research Fellow. L. V. W. holds a GSK/ British Lung Foundation Chair in Respiratory Research. R. G. J. is supported by a  National Institute for Health Research (NIHR)   Research Professorship (NIHR reference   RP-2017-08-ST2-014 ). I. N. is supported by the  NHLBI  ( R01HL130796 ). B. G. -G. is funded by  Agencia Canaria de Investigaci ón, Innovaci ón y Sociedad de la Informaci ón  ( TESIS2015010057 ) cofunded by  European Social Fund. J. M. O. is supported by the  NHLBI  ( K23HL138190 ). C. F. is supported by the  Spanish Ministry of Science, Innovation and Universities  (grant  RTC-2017-6471-1 ;  Ministerio de Ciencia e Innovacion/Agencia Estatal de Investigaci ón/Fondo Europeo de Desarrollo Regional, Uni ón Europea)  cofinanced by the  European Regional Development Funds "A way of making Europe " from the  European Union  and by agreement  OA17/008  with  Instituto Tecnol ógico y de Energías Renovables  to strengthen scientific and technological education, training, research, development and innovation in Genomics,  Personalized Medicine and Biotechnology.
The Spain Biobank array genotyping service was performed at  CEGEN-PRB3-ISCIII , which is supported by  PT17/0019 , of the  PE I1D1i 2013-2016 , funded by  Instituto de Salud Carlos III , and cofinanced by the  European Regional Development Funds. P. L. M. is an  Action for Pulmonary Fibrosis Research Fellow. M. O. is a fellow of the  Parker B. Francis Foundation  and a Scholar of the  Michael Smith Foundation for Health Research. B. D. H. is supported by  NIH   K08 HL136928 ,  Parker B. Francis  Research Opportunity Award. M. H. C. and G. M. H. are supported by  NHLBI  grants  R01HL113264  (M. H. C. ),  R01HL137927  (M. H. C. ),  R01HL135142  (M. H. C. and G. M. H. ),  R01111024  (G. M. H. ), and  R01130974  (G. M. H. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH.
I. S. is supported by  Medical Research Council  ( G1000861 ) and  Asthma UK  ( AUK-PG-2013-188 ). D. F. was supported by an  Intermediate Fellowship  from the  Wellcome Trust  ( 097152/Z/11/Z ). This work was partially supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. V. N. is funded by an  NIHR Clinical Lectureship. G. G. is supported by project grant  141513-051  from the  Icelandic Research Fund and Landspitali Scientific Fund   A-2016-023 ,  A-2017-029 , and A-2018-025. D. J. L. and A. M. are supported by  Multi-Ethnic Study of Atherosclerosis (MESA)  and the  MESA SNP Health Association Resource (SHARe)  project are conducted and supported by the  NHLBI  in collaboration with MESA investigators. Support for MESA is provided by contracts  HHSN268201500003I ,  N01-HC-95159 ,  N01 -.
background Immune checkpoint inhibitor (ICI)-associated early cardiac adverse events (CAEs), mostly acute and fulminant myocarditis, have been well characterized and mainly occur during the first 90 days after ICI therapy initiation. ICI-associated late CAEs (occurring after the first 90 days of treatment) have not yet been described. Methods First, we compared characteristics of a cohort involving early (defined as a CAE time to onset (TTO) of <90 days after ICI therapy initiation) and late (defined as a CAE TTO of ≥90 days after ICI therapy initiation) ICIassociated CAE consecutive cases who were referred to three French cardio-oncology units. Second, ICI-associated CAE cases were searched in VigiBase, the WHO global individual case safety report database, and early and late ICI-associated CAEs were compared.
results In the cohort study, compared with early CAE cases (n=19, median TTO of 14 days), late ICIassociated CAE cases (n=19, median TTO of 304 days) exhibited significantly more left ventricular systolic dysfunction (LVSD) and heart failure (HF) and less frequent supraventricular arrhythmias. In VigiBase, compared with early cases (n=437, 73. 3%, median TTO 21 days), the late ICI-associated CAE reports (n=159, 26. 7%, median TTO 178 days) had significantly more frequent HF (21. 1% vs 31. 4%, respectively, p=0. 01). Early and late ICI-associated CAE cases had similarly high mortality rates (40. 0% vs 44. 4% in the cohort and 30. 0% vs 27. 0% in VigiBase, respectively). Conclusions Late CAEs could occur with ICI therapy and were mainly revealed to be HF with LVSD. trial registration numbers NCT03678337, NCT03882580, and NCT03492528.
Therefore, we aimed to characterize and describe the presentation and clinical course of these new clinical cardiac manifestations. We used two complementary approaches: first, we performed a cohort study to describe the clinical manifestations, management, and outcomes of consecutive sporadic CAE cases referred to three French cardio-oncology units. A cut-off of 90 days was used to separate early (defined as a CAE occurrence of <90 days after ICI therapy initiation) and late (defined as a CAE occurrence of ≥90 days after ICI therapy initiation) ICI-associated CAEs; second, we searched for reports of ICIassociated CAEs in VigiBase, the WHO global individual case safety report (ICSR) database, Open access and we compared early (<90 days) versus late (≥90 days) ICI-associated CAE cases.
Informed consent was obtained from all the living subjects. The detailed list of collected variables is available in online supplementary material 1. The presence of a left ventricular systolic dysfunction (LVSD) was based on an echocardiographic or MRI left ventricular ejection fraction (LVEF) drop of <10% and LVEF of <50%, disregarding the presence of heart failure (HF) symptoms. 9 gibase pharmacovigilance cohort descriptive analysis VigiBase is the WHO global ICSR database. It contains more than 18 million reports from 130 countries. The database is fed with reports from healthcare professionals, pharmaceutical companies and, more recently, patients. All reports are referred to national pharmacovigilance centers within a country, which secondarily transmits them to VigiBase. The Uppsala Monitoring Centre (Uppsala, Sweden) is in charge of keeping VigiBase current by adding novel reports and removing duplicates.
Comparison between early and late ICI-associated CAe cases (cohort study) Figure  2  and table  1  show the comparison between early (n=19) and late (n=19) ICI-associated CAE cases from the French cardio-oncology unit database. The median TTO was 14 days (IQR 8-62) in the early CAE cases and 304 days (IQR 180-422) in the late CAE cases. Compared with the early CAE cases, the late CAE cases had significantly more cardiovascular risk factors (60% vs 100%, respectively; p=0. 01), significantly higher rates of HF (5. 3% vs 47. 4%, respectively; p=0. 01) and LVSD (36. 8% vs 73. 7%, respectively; p=0. 049), and a significantly lower supraventricular arrhythmia rate (47. 4% vs 10. 5%, respectively; p=0. 03). Recovery from LVSD was not different between early and late CAE cases (37. 5% vs 14. 3%, p=0.
Open access Figure  2  Immune checkpoint inhibitor-associated CAE rates in early (<90 days) and late (≥90 days) cases from three French cardio-oncology unit databases (A) and in VigiBase (B). CAE, cardiac adverse event; LVSD, left ventricular systolic dysfunction; ns, not significant. cases showed significantly higher rates of HF (21. 1% vs 31. 4%, respectively; p=0. 012) and pericardial disorders (11. 4% vs 18. 2%, respectively; p=0. 043) in the late CAE cases. Mortality rates were comparable between the early and late CAE cases (30. 0% vs 27. 0%, respectively; p=0. 55).
dIsCussIon In this study, we described for the first time a newly phenomenon of late ICI-associated CAEs, occurring after the first 90 days previously described as the highestrisk time window for ICI-associated CAEs. The alreadyknown and published ICI-associated CAEs are described as occurring rapidly after the initiation of ICI treatment. In a cohort study of two cardio-oncology unit databases, Escudier et al found a median TTO of 29 days for ICIassociated CAEs that were mainly represented by acute and fulminant myocarditis or takotsubo presentation. 3 A recent pharmacovigilance study found similar delays in the onset of myocarditis and pericardial disorders, both associated with inflammatory process. 5 Compared with these early descriptions, we described late CAEs with a median TTO of 6 months (17.
Acknowledgements The information presented in this study does not represent the opinion of WHO. We thank the custom searches team at the  Uppsala Monitoring Centre (Uppsala, Sweden ) research section, without whom this study would not have been possible. Contributors CD, SE, JA,  FRT , and JC conceived and designed the study, collected and interpreted the data, drafted the manuscript or revised it critically for important intellectual content. SA, QD, RG, JM, MS, A-FP, FC, and  AAC  collected and interpreted the data, revised the manuscript for critically important intellectual content. All authors gave their final approval of the manuscript submitted and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy of integrity of any part of the work are appropriately investigated and resolved.
Accessing human mobility data is now possible at several spatial and time scales, and often in nearly real-time. These data have been proven useful in many epidemiological contexts  4  -including for example the West Africa Ebola epidemic  5  -and are being used now for COVID-19 pandemic in many countries  6  namely, Belgium  7  , Germany 8 , India  9  , Italy 10 , Poland  11  , Spain  12  , UK  13  , USA  14, 15. Mobile phone records are one of the main sources of mobility data. They describe travel flows among the different locations of a country. These flows can be analyzed over time to study population patterns, with no information on individual users, safeguarding privacy  6. In this study, we used data provided by Orange Business Service Flux Vision, and studied how mobility in France changed before and during lockdown.
Timeline of COVID-19 epidemic in France. Three phases have marked the French response to COVID-19 epidemic (Figure  1 ). Phase 1 started on January 10, 2020. Its aim was to detect imported cases and identify local transmissions through case-contact investigations. Phase 2 started upon appearance of localized clusters, and added targeted social distancing interventions around reported clusters (e. g. school closure, gatherings and public transport bans). Phase 3 was declared on March 14, 2020 when the virus was recognized to actively circulate in the territory. Data. Mobility data were provided by the Orange Business Service Flux Vision in the form of displacement matrices. They comprised origin-destination travel flows among 1436 geographic areas of mainland France. For each pair of locations and any given day, data were provided stratified by age class (less than 18 years old, 18-64, 65 and more).
Timeline fit and prediction. To fit and forecast time series we used the forecasting procedure Prophet by Facebook Open Source  19. Prophet decomposes a time series into non-periodic and periodic components which then are fitted using MCMC (Appendix S2). We fitted Prophet on traffic flow data from January 6, 2020, to March 9, 2020 (training set of the model), and extrapolated traffic flow after March 9, assuming no perturbation due to COVID-19 or associated interventions. We then measured the deviation of observed traffic from the predicted temporal evolution of unperturbed traffic over time. Trip analysis. Our analyses were performed on all trips and on trips whose geodesic distance between location centroids is longer than 100 km (long trips). This cutoff effectively discards commuting, as ~95% of daily work-related trips are shorter than 100 km  17.
Statistical analysis. All statistical analyses were performed in R, version 3. 6. 1. Twosided significance of Pearson coefficients was determined at a level of 0. 05. The coefficient of variation is defined as the sample standard deviation divided by the sample mean, expressed in percentage points. Network analysis. Nodes in the networks represent the geographic locations in which we divided mainland France, and links represent trips between locations. Links are directed (trips have origins and destinations), weighted (by the number of trips linking two locations), and evolve in time. We used standard Python libraries, among which NetworkX. Link persistence probability at a given week was defined as the probability that a connection present in the network during week February 3-9 (benchmark week) was still present in the week under consideration.
DISCUSSION Using travel flow data extracted from mobile phone trajectories, we documented a large drop in both short-range and long-range population mobility following lockdown enforcement in France. Overall, trips were reduced by 65%, similarly to reductions found in Belgium 7 , Spain  12  , and Italy 10 during lockdown, albeit different data sources, spatial resolutions, and definitions of mobility proxies prevent direct numerical comparisons. The transition signaling the drop in mobility lasted almost a week, anticipating the enforcement of lockdown and creating opposite mobility behaviors. Individuals started spontaneously reducing their mobility on Saturday following the announcement of school closure (March 14), likely because of fear of the growing epidemic and heightened risk aversion  21, 22  generated by the first governmental decision on nationwide interventions.
Adjusting for disease perception and demography reversed this association. This is likely due to complex interactions between behavioral response, demography, and economic development. The limited available dataset however does not allow disentangling these effects and infer causal relationships (see Appendix S4). A strong response was documented in the older age class: as seniors are at highest risk of developing severe forms of COVID-19 if infected, they might also have exhibited increased risk aversion. Specifically, they almost stopped taking trips longer than 100 km, likely to avoid leisure activities and family trips, as recommended by authorities. The most effective reduction in overall mobility occurred during rush hours, associated with a disruption of commuting patterns. This reduction alone likely boosted the role of mobility restrictions in suppressing viral diffusion, as mounting evidence shows that public transportation is a main risk factor for transmission  26.
DATA AVAILABILITY Mobile phone data are proprietary and confidential. We obtained access to these data from the Orange Business Service Flux Vision within the framework of the research project ANR EVALCOVID-19 (ANR-20-COVI-0007). For legal reasons, the full dataset cannot be published. However, access to the mobility data can be requested from Orange SA on a contractual basis. Hospitalization data were obtained from Santé Publique France and are available at the cited source  16. Census and socioeconomic data can be accessed through the official website of the French National Statistical Institute (INSEE) at the cited source 17.
Background: Knee osteoarthritis (OA) is a painful condition affecting >250 million people worldwide and is a leading cause of disability. Intra-articular (IA) corticosteroids and/or oral opioids are often recommended for the management of knee OA pain. There are, however, concerns regarding their safety and tolerability. Study Question: Do patients diagnosed with knee OA show a decrease in opioids or IA corticosteroid injections prescribed/administered in hospitals following hylan G-F 20 treatment? Study Design: This case-crossover, retrospective study using Health Facts ® , a de-identified electronic health records database, enrolled patients ≥18 years with knee OA treated with hylan G-F 20 between January 1, 2000 and March 31, 2016, with data within 6 months before/after treatment. Measures and Outcomes: Primary endpoints compared days on opioids, amounts of opioids, and number of IA corticosteroid injections before/after hylan G-F 20 treatment via paired t-tests.
12 rowing recognition of the issues with opioids and IA corticosteroid injections indicates that reducing the use of these agents might be beneficial to knee OA patients. Treatment with hylan G-F 20, a high molecular weight, cross-linked, injectable HA product may help to reduce the use of other pain medications such as opioids and corticosteroids in knee OA. 13  The current retrospective study used electronic health records (EHRs) from a large US database to determine whether the days on opioids, the amount of opioids, or the number of IA corticosteroid injections changed following initial treatment with hylan G-F 20 in adult patients with knee OA.