dataesr/scientific-paragraphs-categorization huggingface dataset:
Characteristics of the Study Population on Inclusion Following the data extraction and then application of the exclusion criteria, the final cohort comprised 325,319 (54. 3%) patients (Figure  1 ). A total of 1,693,214 hospital stays by 325,319 different patients between January 1, 2008, and December 31, 2014, were included in the analysis. There were 182,796 (56%) females and 142,523 (44%) males, giving a sex ratio of 0. 78. The mean ± SD age on inclusion was 58 ± 19. The median (IQR) number of hospital stays per patient was 4 (2;6), and the median (IQR) length of stay was 3 (1; 7) days. With regard to the disease of interest, 118,528 (36. 4%) of the patients had rheumatoid arthritis, 54,982 (16. 9%) had psoriatic arthritis or plaque psoriasis, 54,380 (16. 7%) had ulcerative colitis, 45,674 (14.
Two patients treated for Crohn's disease and ulcerative colitis attempt suicide after repeated IFX injections  (Roblin et al. , 2006; Eshuis et al. , 2010). Manic symptoms (euphoria, insomnia, agitation…) appeared in two others patients after their first IFX administration, for one of them symptoms reappeared after 6 injections  (Elisa and Beny, 2010; Austin and Tan, 2012). In the Portugese pharmacovigilance database, three cases of psychiatric disorders with no more details were reported  (Mendes et al. , 2013; Mendes et al. , 2014). In an efficacy study about IFX and biosimilars, 10% of the 94 patients experience various adverse effects including depression  (Razanskaite et al. , 2017). In a safety study, a patient committed suicide four months after his last IFX infusion  (Fidder et al.
Strengths of the Study The present study's main strength was its creation of a large historical cohort from a national database. To the best of our knowledge, our study is the first to have included such a large number of IFX-treated patients. We were therefore able to screen for rare adverse events. Moreover, most studies in the literature were limited to a single disease, whereas the present study covered all the indications for IFX approved in France. We used a control group of patients with the same diseases to eliminate the indication as a confounding factor, since it is well known that these diseases and/or their treatments are associated with psychiatric disorders  (Dickens et al. , 2002; Bougue ́on and Misery, 2008; Graff et al. , 2009; Olivier et al. , 2010). Even though chronic comorbidities are not described in detail in the PMSI database, we took them into account as best we could; indeed, studies have shown that the prevalence of mental health problems (e.
g. , depressive disorders) among patients with a chronic disease (cardiovascular disease, diabetes, epilepsy, obstructive pulmonary disease, etc. ) is higher than in the general population  (Miorelli and Abe, 2016). The reuse of data from reimbursement databases is limited by the fact that some patients do not actually take the medication that has been dispensed. Since IFX is exclusively administered in hospital in France, we considered that a record in the PMSI database is a reliable marker of exposure to the drug. Moreover, by excluding patients admitted to for-profit hospitals and by using the patients' unique identifiers, we were able to establish each patient's true timeline for IFX exposure with no missing values. Furthermore, by considering at least one year of data prior to the index IFX infusion, we could reasonably suppose that all patients included in the analysis were new IFX users.
This exclusion criterion enabled us to avoid bias related to the depletion of susceptible individuals. We also limited bias by only considering the patient's first recorded psychiatric adverse event, since psychiatric disorders like major depression have a high risk of recurrence  (Gotlib and Hammen, 2010). Our pharmacovigilance analysis of the PMSI database is an novel technique that has rarely been used to detection adverse events  (Weill et al. , 2010; Neumann et al. , 2012; Heng et al. , 2015). A recent report validated the use of the PMSI database for detecting serious adverse drug reactions  (Osmont et al. , 2013). Furthermore, the Cox regression models used in the present study are the most appropriate for survival analyses of medical data  (Desquilbet and Meyer, 2005). A time-dependent variable allowed us to take account of changes in IFX's exposure during patient follow-up.
Weaknesses of the Study The major limitation of our study was its inability to take full account of some important confounding factors. Firstly, the PMSI database lacks information on the patient's other drug prescriptions (usual treatments, self-medication etc. ), which the exception of certain expensive treatments administered in hospital. This is a problem, give that IFX can be combined with methotrexate in some indications, and that corticoids are the firstline treatments for inflammatory diseases. Indeed, the SPC for methotrexate mentions the possible occurrence of psychiatric disorders such as depression and psychoses [Agence Nationale de sécurité du médicament et des produits de santé (ANSM), 2020], and the SPCs for corticosteroids also mention occasional manic episodes or depressive symptoms upon treatment discontinuation [Agence Nationale de sécurité du médicament et des produits de santé (ANSM), 2020].
These psychiatric symptoms are also reported in the scientific literature  (Kenna et al. , 2011). This limitation is partially taken into account by the stratification method used in the present study, since combination therapy is usually related to the disease being treated. Moreover, we were not able to determine whether or not our results were dose-dependent. Secondly, we were not able to consider the severity of the disease treated with IFX. A difference in severity between exposed and non-exposed groups could result in an indication bias: the administration of IFX might then simply be a severity marker, with severity as the cause of the psychiatric event. Thirdly, the PMSI database lacks data about the patient's environment and background. It is well known that genetics and family history are risk factors for psychiatric disorders  (Meyer-Lindenberg and Weinberger, 2006).
The higher death rate in non-exposed patients could be explained by the efficacy of the IFX treatment, or by an indication bias: the IFX treatment could have been proposed to patients with less comorbidities. The observational design of this study does not enable to interpret this difference. Lastly, all studies of administrative databases have a number of inherent limitations. The data quality is strongly influence by each institution's collection and entry methods. For example, coding errors can lead to an inappropriate diagnosis. Heterogeneity in coding has been observed in various studies and various fields of medicine  (Chantry and Bouvier-Colle, 2010; Gerbier et al. , 2011; Bernier et al. , 2012; Chantry et al. , 2012; de Mey et al. , 2012; Quantin et al. , 2014; Guerra et al. , 2015; Laplace et al.
, 2015; Pierron et al. , 2015; Fermaut et al. , 2016; Perozziello et al. , 2018). Codification of the PMSI database is a complex procedure because of the large number of codes: more than 42,000 for ICD-10 (WHO, 2020), 13,000 for CCAM [L'Assurance Maladie (Ameli), 2020], and 2,000 for UCD [Agence Technique de l'information sur l'Hospitalisation (ATIH), 2020]. The French Technical Agency for Information on Hospital Care (Agence Technique de l'Information sur l'Hospitalisation) provides methodological guides for physicians and technicians responsible for coding. To the best of our knowledge, the coding validity for psychiatric events, inflammatory diseases or the drugs used to treat inflammatory diseases has not been studied.
Perspectives Our present results highlighted possible psychiatric adverse events associated with IFX therapy. These preliminary results must now be confirmed. It might be useful to link to other administrative databases, such as the French National Health Insurance Database (Système National d'Information Interre ́gimes de l'Assurance Maladie); the latter includes information on all dispensed prescription medicines (e. g. antidepressants, as a new outcome), and chronic medical conditions (as confounders). Moreover, it would be interesting to study the more recent data on IFX biosimilars and look at whether TNFa inhibitors have a class effect.
Next-generation sequencing Genomic DNA was extracted from whole blood. For the 1193 patients and controls included, the whole exome (N = 687) or whole genome (N = 506) was sequenced. We used the Genome Analysis Software Kit (GATK) (version 3. 4-46 or 4) best-practice pipeline to analyze our whole-exome-sequencing data  (30). We aligned the reads obtained with the human reference genome (hg19) using the maximum exact matches algorithm in Burrows-Wheeler Aligner software  (31). PCR duplicates were removed with Picard tools (http://broadinstitute. github. io/picard/). The GATK base quality score recalibrator was applied to correct sequencing artifacts. All of the variants were manually curated using Integrative Genomics Viewer (IGV) and confirmed to affect the main functional protein isoform by checking the protein sequence before inclusion in further analyzes.
The main functional protein isoforms were TLR3 (NM_ 003265), UNC93B1 (NM_030930. 4), TICAM1 (NM_182919), TRAF3 (NM_145725. The analysis of IKBKG was customized to unmask the duplicated region in IKBKG using a specific pipeline previously described  (32). We searched the next-generation-sequencing data for deletions in the 13 genes of interest using both the HMZDelFinder (33) and CANOES (34) algorithms.
We checked that our adjusted burden test was well calibrated by also performing an analysis of enrichment in rare (MAF <0. 001) synonymous variants of the 12 genes. PCA was performed with Plink version 1. 9 software on whole-exome-and wholegenome-sequencing data and the 1000 Genomes (1kG) Project phase 3 public database as a reference, using 27,480 exonic variants with a MAF >0. 01 and a call rate >0. 99. The OR was also estimated by logistic regression and adjusted for ethnic heterogeneity.
Reporter assays Cell lines or SV40-Fib cells with known defects were transiently or stably transfected with WT, mutant variants, IFN-b-or ISRE-firefly luciferase reporter, and pRL-TK-Renilla luciferase reporter. Reporter activity was measured with the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer's instructions. Firefly luciferase activity was normalized against Renilla luciferase activity and expressed as a fold change. TRAF3-deficient human embryonic kidney (HEK) 293T cells were kindly provided by M. Romanelli  (39). pDC activation by SARS-CoV-2 and cytokine production pDCs from an IRF7 -/-patient and a healthy donor matched for age and sex were cultured in the presence of medium alone, influenza virus (A/PR/8/34, 2 mg/ml; Charles River Laboratories), or the SARS-CoV-2 primary strain 220_95 (GISAID accession ID: EPI_ISL_469284) at a multiplicity of infection (MOI) of 2.
SARS-CoV-2 infection in patient SV40-Fib To make patient-derived fibroblasts permissive to SARS-CoV-2 infection, we delivered human ACE2 and TMPRSS2 cDNA to cells by lentivirus transduction using a modified SCRPSY vector (GenBank ID: KT368137. 1). SARS-CoV-2 strain USA-WA1/2020 was obtained from BEI Resources. ACE2/TMPRSS2-transduced cells were either left untreated or treated with 500 U/ml IFN-b (11415-1, PBL Assay Science) 4 hours before infection. Cells were infected with SARS-CoV-2 (MOI = 0. 5) for 1 hour at 37°C. After 24 hours of infection, cells were fixed and taken out of the BSL3 for staining. After fixation, cells were stained with SARS-CoV-2 and ACE2 primary antibodies (0. 5 and 1 mg/ml, respectively). Primary antibodies were as follows: for SARS-CoV-2, human monoclonal anti-spike-SARS-CoV-2 C121 antibody  (40) , and for ACE2, mouse monoclonal Alexa Fluor 488conjugated antibody (FAB9332G-100UG, R&D Systems).
Images were acquired with an ImageXpress Micro XLS microscope (Molecular Devices) using the 4× objective. MetaXpress software (Molecular Devices) was used to obtain singlecell mean fluorescence intensity (MFI) values. Data analysis on single-cell MFI values was done in the R environment (version 4. 0. 2). ACE2/TMPRSS2-transduced cells were classified as ACE2 positive when the ACE2 log MFI was superior to the log mean MFI of mocktransduced cells plus 2. 5 SDs. We excluded all wells with <150 ACE2-positive cells before SARS-CoV-2 scoring. ACE2-expressing cells were classified SARS-CoV-2 positive when the fluorescence intensity value was superior to  Single-molecule array (Simoa) IFN-a digital ELISA Serum IFN-a concentrations were determined using Simoa technology, with reagents and procedures obtained from Quanterix Corporation (Quanterix SimoaTM IFNa Reagent Kit, Lexington, MA, USA).
ACKNOWLEDGMENTS We thank the patients, their families, and healthy donors for placing their trust in us;  Y. Nemirowskaya ,  D. Papandrea ,  M. Woollet ,  D. Liu ,  C. Rivalain , and  C. Patissier  for administrative assistance;  A. Adeleye ,  D. Bacikova ,  E. McGrath Martinez ,  A. R. Soltis ,  K. Dobbs ,  J. Danielson ,  H. Matthews , and  S. Weber  for technical and other assistance;  M. M. A. Ata  and  F. Al Ali  for their contribution to VirScan experiments;  S. Elledge  ( Brigham and Women's Hospital and Harvard Medical School, Boston, MA ) for kindly providing the VirScan phage library used in this study;  A. W. Ashbrook , the BSL3 manager of the  Rice laboratory assistance ;  M. Lazzaro , Director of  Immigration and Academic Appointments , for assistance;  W. Chung ,  K. Kiryluk ,  S.
S. Department of Defense (DoD). Funding: This work was supported by a generous donation from the  Fisher Center for Alzheimer's Research Foundation. The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St. Giles Foundation , the  NIH  ( R01AI088364 ), the  National Center for Advancing Translational Sciences (NCATS) , the  NIH Clinical and Translational Science Award (CTSA) program  ( UL1 TR001866 ), a  Fast Grant  from  Emergent Ventures ,  Mercatus Center at George Mason University , the  Yale Center for Mendelian Genomics  and the  GSP Coordinating Center  funded by the  National Human Genome Research Institute (NHGRI)  ( UM1HG006504  and  U24HG008956 ), the  French National Research Agency (ANR)  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for 1 Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research ,  NIAID ,  NIH, Bethesda, MD, USA.
Data mining and transcriptome analyses For gene expression analysis, RNA was extracted from the available frozen tumor samples (n=38 of 48). cDNA was prepared from minimum 500 pg RNA per sample and hybridized on GeneChip Human Gene HTA 2. 0 Affymetrix microarrays (Affymetrix UK Ltd. ), by the Lyon University genomic facility ProfileXpert-LCMT (Lyon, France), were done according to the manufacturer's protocol. Data are available on the NCBI Gene Expression Omnibus website (http://www. ncbi. nlm. nih. gov/geo/): GEO dataset GSE148070. Details of the methods used are presented in the Online Supplementary Methods.
Mutation profile analysis with next-generation sequencing After DNA extraction of 51 available FL FFPE (n=33 from training cohort and n=18 from validation cohort), samples were sequenced on an Illumina MiSeqDx using our lymphopanel of 43 genes involved in B-cell lymphomagenesis. 17 Sequencing and data analysis was performed as previously described  17, 18  (see details in the Online Supplemental Methods).
Statistics analysis PFS was defined as the time from the first cycle of immunochemotherapy to progression or death (event) or last follow-up/change of treatment (censored data). POD24 was defined as primary-refractory disease (less than partial response), progression, transformation or relapse within 24 months after diagnosis. OS was defined as the time from the start of therapy to death or last follow-up. All survival rates were estimated using the Kaplan-Meier method, and log-rank test were assessed using R software. Median follow-up was calculated with the Kaplan-Meier reverse method. 19 Optimal cutoff to predict PFS was determined using the R 'Survival' package  20, 21  and log-rank test-based P-values. Indeed, the multiple tests performed by this package were corrected using the Benjamini-Hochberg method  22  to control the false discovery rate (FDR) and determine the optimal threshold.
Baseline SUVmax does not correlate with immune infiltration In order to investigate whether the immune infiltration signature in baseline biopsies was associated with an increased  18  FDG uptake, we compared patients with SUVmax>14. 5 and SUVmax≤14. 5, and the immune profile determined by immunochemistry (IHC) and transcriptome approaches. We first measured the sample enrichment score (SES) of both 'T-cell activation' and 'IEGS33' gene sets in each transcriptome from our FL training cohort and from 1,446 NHL and normal lymphoid tissue downloaded from public cohorts available in GEO data set. 8, 19 he 38 available frozen FL samples from our training cohort and 148 of the 1,446 NHL samples had a high score for IEGS33, indicating up-regulation of immune escape genes. The score for T-cell activation signature in these FL samples was also among the highest.
Acknowledgements The authors would like to thank  Nathalie Van-Acker  (immunochemistry staining, Dept. of  Pathology, IUCT-O, Imag'in Plateform, Toulouse) ,  Frédéric Escudié  and  David Grand  (NGS analysis, Dept. of Pathology, IUCT-O, Toulouse,  Karin Gordien  ( Registre des Cancers du Tarn, Albi), Sophie Péries (CRB CHU Toulouse ). We also thank  Suzanne Rankin  from the  Dijon-Bourgogne University Hospital  for proofreading the manuscript. Baseline SUVmax  in FL and tumor proliferation haematologica | 2022; 107(1).
Results A total of 400 ancient teeth from 145 individuals collected in nine European sites with different times of burial were analysed using qPCR. All negative controls were negative, and qPCR detected B. quintana DNA in all sites. The results indicated that a total of 78 civilians from six sites had 17. 9% positivity, and 67 soldiers from three sites had 20. 1% positivity. The positive detection at the Besancon site was the oldest, with data from the 1st-4 th centuries (Table  1 ). Seven of the 17 archaeological sites had data from the 18th-19 th centuries (Tables  1  and 2 ), where the prevalence of B. quintana DNA was 3/16 (18. 8%) among civilians and 24/120 (20%) among soldiers; the difference in the prevalence was not significant (Table  3 ).
Assessment of T cell precursor frequencies for secukinumab and ixekizumab Antibody-specific CD4 T cells were generated as previously described  31  to evaluate the size of the preexisting T cell repertoire specific for secukinumab and ixekizumab. As determined by genotyping, the donors represented the most common Human Leukocyte Antigen -DR isotype (HLA-DR) alleles found in an ethnically mixed European population (Supplementary Table  1 ). Of the 31 donors, plates from 30 were available to determine the generation of a T cell response to keyhole limpet hemocyanin (KLH [positive control]). All 30 plates demonstrated the ability of the donors to mount an in vitro T cell response to KLH (Figure  1 ). Although no positive control KLH data was available for one donor, samples from this donor were subsequently found to react to secukinumab and, therefore, were not excluded from the study.
Identification of potential T cell epitopes by MAPPs Immature monocyte-derived dendritic cells (DC) from healthy human treatment-naïve HLA typed (Supplementary Table  2 ) blood donors were loaded with secukinumab or ixekizumab in the presence of lipopolysaccharide (LPS). HLA class II-associated peptides were isolated from secukinumab-or ixekizumab-loaded DC by immunoprecipitation with anti-HLA class II beads. Peptides were subsequently analyzed by liquid chromatography-mass spectrometry and identified via database search using the SEQUEST algorithm. 29 For both secukinumab and ixekizumab, similar numbers of presented sequence regions, represented as 'potential' T cell epitopes, were detected in the variable domains. Secukinumab showed 13 presented sequence regions, 8 in the heavy chain and 5 in the light chain, and ixekizumab showed 12 presented sequence regions, 5 in the heavy chain and 7 in the light chain.
HLA class II molecules were immunoprecipitated with anti-HLA-DR/DP/DQ Mab IVA12-conjugated beads and peptides were eluted from HLA class II molecules by adding 0. 1% trifluoroacetic acid (Fluka, Buchs, Switzerland) at 37°C. Lyophilized peptides were re-suspended in hydrophilic buffer containing 5% acetonitrile and 1. 1% formic acid. Peptide composition was analyzed by liquid chromatography (nano capillary system, Dionex Corporation, Sunnyvale, California, USA) on a self-packed fused-silica C18 reversed-phase nanohigh-performance liquid chromatography column connected to a mass spectrometer (Q-Exactive, Thermo, California, USA) via electrospray ionization (LC-ESI-MS/MS). Peptides were identified via a database search approach using the SEQUEST algorithm as detailed previously. 29.
Sequence alignment Amino acid sequences of secukinumab and ixekizumab were aligned to germline sequences published on imgt. org. 37 In the case of ixekizumab, IGHV1-69*01|IGHJ1*01 and IGKV2D-29*02|IGKJ2*01 were found as the germline sequences with the highest sequence identity for the heavy and light chain, respectively. In the case of secukinumab, IGHV3-7*01| IGHJ2*01 and IGKV3-20*01|IGKJ5*01 were found as closest germline sequences for heavy and light chain, respectively. To assess whether amino acids deviating from the closest germline are found in other germline family members the following sequences were considered from IMGT database:  37  For ixekizumab heavy chain: IGHV1-2; IGHV1-3; IGHV1-8; IGHV1-18; IGHV1-24; IGHV1-38-4; IGHV1-45; IGHV1-46; IGHV1-58; IGHV1-68; IGHV1-69; IGHV1-69-2; IGHV1-69D; IGHV1-NL1.
Acknowledgments The authors thank  Trudy McGarry, PhD  and  Jackie L. Johnson, PhD  of  Novartis Ireland Ltd  for providing medical writing support/editorial support, which was funded by  Novartis Pharma GmbH ,  Nürnberg, Germany  in accordance with Good Publication Practice (GPP3) guidelines (http://www. ismpp. org/gpp3). The authors thank  Stephan Koepke  for conducting the LC-MS analysis for the MAPPs assay.
Methods In this narrative review, we performed a historical bibliographic search for references to the medical treatment of endometriosis and a systematic Pubmed search covering articles in any language published since 1958 with the following terms: ('Endometriosis'[Mesh] OR 'Endometrium'[Mesh]) AND ('Progestins'[Mesh] OR 'Medrox- yprogesterone Acetate'[Mesh] OR 'Norethindrone'[Mesh] OR 'Desogestrel'[Mesh] OR 'dienogest' [Supplementary Concept] OR 'Dydrogesterone'[Mesh] OR 'Levonorgestrel'[Mesh] OR 'Mifepri- stone'[Mesh] OR 'ulipristal acetate' [Supplementary Concept] OR vilaprisan [Supplementary Concept] ). Two reviewers screened the titles and abstracts to select articles for full-text assessment. We also searched clinicaltrials. gov and EudraCT for study protocols on hormonal treatments of endometriosis with the status of 'not yet recruiting', 'recruiting', 'active, not recruiting' or recently (until December 2017) 'completed' or 'terminated'.