dataesr/scientific-paragraphs-categorization huggingface dataset:
Registration to template was undertaken using FSL-FLIRT  (Jenkinson and Smith, 2001) , and FSL-FAST was used for tissue type segmentation  (Zhang, et al. , 2001). The hippocampus was segmented into cytologically-defined subfields (Figure  1A ) using version 6. 0 of the FreeSurfer image analysis suite  (Fischl, 2012). The pre-processing pipeline included the removal of non-brain tissue, segmentation of the subcortical white matter and deep grey matter structures, intensity normalization, tessellation of the grey matter-white matter boundary, and automated topology correction. The hippocampal stream of the FreeSurfer package was used for the accurate segmentation of the following hippocampal subfields: CA1, CA2/3, CA4, fimbria, hippocampal fissure, presubiculum, subiculum, hippocampal tail, parasubiculum, molecular layer; granule cell layer of the dentate gyrus (GC-DG), hippocampalamygdala transition area (HATA)  (Iglesias, et al.
White matter analyses White matter tractography was conducted using the Brainance DTI Suite (Advantis Medical Imaging, Eindhoven, the Netherlands). First, the raw DWI datasets were motion and eddy-current corrected. Three ROIs were used for the reconstruction of perforant pathway zone (PPZ); (1) on a coronal slice at the middle of the splenium of the corpus callosum the perforant pathway was identified subjacent the corpus callosum, (2) on a coronal slice anterior to the first one at the level of the cerebellar dentate nucleus and (3) the white matter area of the mesial temporal lobe just underlying the hippocampus on a coronal slice anterior to the pons at the level where the projections of the posterior limb of internal capsule are prominent (Figure  1B ). For the reconstruction of the fornix, three ROIs were placed along the fornix in three coronal slices where the fornix was visible as one bundle (Figure  1C ).
Funding information Foteini Christidi is supported by the  State Scholarships Foundation  (I. K. Y. ; Postdoctoral Support;  EP ANADEDBM/ESPA 2014-2020 ). Peter Bede  is supported by the  Health Research Board (HRB -Ireland ;  HRB EIA-2017-019 ), the  Andrew Lydon scholarship , the  Irish Institute of Clinical Neuroscience IICN -Novartis   Ireland Research Grant , the  Iris O'Brien Foundation , and the  Research Motor Neuron (RMN-Ireland) Foundation. The sponsors of this study had no impact on the contents, analyses and conclusions of this paper or the decision to submit it for publication.
Once a biosimilar is approved, it can be prescribed to patients CONTACT Christopher J. Edwards cedwards@soton. ac. uk NIHR Clinical Research Facility, University Hospital Southampton NHS Foundation Trust, Southampton, UK Supplemental data for this article can be accessed here. who are naïve to the reference product. In addition, for patients already treated with a biologic, physicians can switch treatment with the reference product to treatment with a biosimilar. Before approval, the FDA expects additional safety, efficacy and immunogenicity data for patients who undergo a switch from a reference product to a biosimilar  [26]. In line with this, the vast majority of pivotal biosimilar studies conducted to date have included a switch component in the study design, in order to demonstrate that efficacy, safety, and immunogenicity are maintained after switching  [27].
Literature search Randomized controlled trials and real-world studies that include a switching design were identified from three previously published systematic reviews  [28] [29] [30]. Additional studies were identified through a narrative literature search conducted in March 2019. The PubMed and European Union Clinical Trials Register databases were searched using the following search terms: infliximab, etanercept, adalimumab, rituximab, CT-P13, SB2, BOW015, GP2015, SB4, ABP 501, BI 695501, CHS-140, FKB327, GP2017, MSB11022, SB5, CT-P10, GP2013, biosimilar, and switch. Only articles in English were included. We also searched abstracts presented at the EULAR, ECCO and ACR congresses using the search terms biosimilar and switch.
Additional studies will be required to address remaining evidence gaps and to gain insights into the impact of multiple switching, including switching between biosimilars. The data obtained from real-world studies have indicated that treatment discontinuations can be higher with biosimilars compared with reference products. However, most studies concluded that these discontinuations were predominantly for subjective reasons rather than as a result of changes in objective clinical outcomes. Although the reasons for this are uncertain, it has been postulated that this observation may be due to a nocebo effect as a result of patients' preconceived perceptions of biosimilars. As a result of the growing body of evidence supporting switching, medical societies in Europe are now generally supportive of prescriber-initiated switching.
Acknowledgments Fresenius Kabi SwissBioSim GmbH participated in the writing, review, and approval of the manuscript. All authors contributed to the development of the content; all authors and Fresenius Kabi reviewed and approved the manuscript; and the authors maintained control over the final content. Editorial and medical writing support was provided by  Stephanie Carter, PhD , and  Dan Hami, PhD , of  Arc Medical Communications, Manchester, UK.
6%, 40. 3%, and 36. 2% of RP, SB2 and RP/SB2 groups • ADAs occurred in 14. 6%, 14. 9%, and 14. 1% of RP, SB2 and RP/SB2 groups • 5. 0%, 7. 5%, and 6. 4% of RP, SB2, and RP/ SB2 pts discontinued before w78 NR Efficacy outcomes ACR20/50/70 similar between groups Mean DAS28-ESR similar between groups ASAS20/40 and ASAS partial remission were similar between groups RP/CT-P13 switch was not inferior to RP maintenance (measured by disease worsening) Disease worsening occurred in 16. 8% maintenance pts vs 11. 6% switch pts Clinical worsening and CDAI-70 responses were similar between the 4 groups To date 1 pt experienced a relapse (switch group) No significant differences in DAS28-ESR, DAS28-CRP, or ACR20/50/70 responses between treatment groups ACR20 was sustained and comparable across treatment groups Disease activity was similar between the 2 groups throughout the OLE • • • • • • • • • • Duration after switch (weeks) 40 105 40 52 78 24 30 24 24 38 Study/switch design  † OLE study Single switch after a 62w DB study CT-P13 maintenance pts vs RP/CT- P13 switch pts OLE study Single switch after a 54w DB study CT-P13 maintenance pts vs RP/CT- P13 switch pts OLE study Single switch after a 54w DB trial: CT-P13 maintenance pts vs RP/CT- P13 switch pts DB RCT Single switch after ≥6 months of treatment with RP RP maintenance vs RP/CT-P13 switch OLE study Single switch after a 52w DB trial CT-P13 maintenance pts vs RP/CT- P13 switch pts DB RCT Single switch at w30 RP maintenance vs CT-P13 maintenance vs RP/CT-P13 switch vs CT-P13/RP switch DB RCT Single switch in patients receiving RP prior to randomization RP maintenance vs RP/CT-P13 switch OLE study Single switch after a DB 30w trial Continued NI-071 vs RP/NI-071 switch DB RCT Single switch at w54 RP maintenance vs SB2 maintenance vs RP/SB2 switch OLE study Single switch after a 16w DB study BOW015 maintenance pts vs RP/ BOW015 switch pts N* 302 71 174 481 380 220 47 242 584 157 Indications RA RA AS IBD, SpA, RA, PsA, PsO IBD, SpA, RA, PsA, PsO IBD IBD RA RA RA Study CT-P13 (infliximab) PLANETRA-OLE Yoo 2017 [79] Tanaka 2017 [80] PLANETAS-OLE Park 2017 [81] NOR-SWITCH Jorgensen 2017 [60] NOR-SWITCH OLE Goll 2019 [82] NCT02096861 Kim 2017 [83] Ye 2018 [84] SIMILAR Volkers 2017 [85] NI-071 (infliximab) NCT01927263 Matsuno 2018 [71] SB2 (infliximab) NCT01936181 Smolen 2018 [70] BOW015 (infliximab) UNIFORM OLE Kay 2015 [72] GP2015 (etanercept) • OL study • No continuous RP control arm • Not powered to evaluate switching.
1% of switched pts and 1. 0% of contin- ued pts Frequency of AEs and SAEs was similar between the 2 groups Non-neutralizing, low titer ADAs were detected in 4 GP2015 maintenance pts Rates of TEAEs were comparable between the groups 1 pt in each group developed ADAs Approx. 5% of both groups discontinued before the end of the trial Incidence of AEs and new ADAs was comparable between groups AEs were balanced between groups % of pts with ADAs and NABs were similar between groups Rates of TEAEs and ADAs were similar between groups Immunogenicity and safety and tolerability profiles were similar between all three groups Similar incidences of TEAEs and ADAs between groups Safety profiles were comparable for all treatment sequences • • • • • • • • • • • • • • Efficacy outcomes PASI 50/75/90 response rates and all other efficacy parameters were similar between the pooled switched and pooled continued treatment groups Mean change in DAS28-CRP and EULAR and ARC 20/50/70 response rates were comparable between the two groups ACR20 response rates were comparable between the two groups DAS28-ESR scores were maintained in both groups to end of study PASI % improvements were similar across all 3 groups No significant differences in % of PASI 50, 75, 90, 100 respon- ders Efficacy results were similar between ABP501 maintenance and RP/ABP501 switch groups Mean change in DAS28-ESR and ACR20/50/70 response rates were similar across all 3 groups No statistically significant differences in PASI75 response rates, HAQ-DI, or hsCRP between groups at w24 ACR20 was comparable after continuous or switched treatment • • • • • • • • • • Duration after switch (weeks) 40 24 48 48 36 72 34 8 76 Study/switch design  † DB RCT Multiple switches: 3 alternating 6-week periods between w12 to 30 Pooled RP and GP2015 maintenance groups vs pooled switch groups DB RCT Single switch after a 24w DB study GP2015 maintenance pts vs RP/ GP2015 switch pts OLE study Single switch after 52w DB study SB4 maintenance pts vs RP/SB4 switch pts OLE study Single switch after a 52w DB study Continued LBEC0101 pts vs RP/ LBEC0101 switch pts DB RCT Single switch at w16 Continued ABP501 pts vs continued RP pts vs RP/ABP501 switch pts OLE study Single switch after a 26w DB study Continued ABP501 pts vs RP/ ABP501 switch pts DB RCT Single switch at w24 Continued BI 695501 pts vs continued RP pts vs RP/BI 695501 switch pts DB RCT Single switch at w16 Continued CHS-1420 pts vs RP/ CHS-1420 switch pts OLE study Multiple switches: 1/3 switched to alternate treatment at week 24 of parent trial, all switched to FKB327 at week 52 N* 531 376 245 148 326 467 593 545 645 Study Indications EGALITY PsO Gerdes et al 2018 [73] EQUIRA RA Matucci-Cerinic et al 2018 [74] SB4 (etanercept) NCT01895309 RA Emery et al 2017 [75] LBEC0101 (etanercept) Song et al.
2018 [76] RA ABP501 (adalimumab) NCT01970488 PsO Papp et al 2017 [61] NCT02114931 RA Cohen et al. 2016 [62] BI 695501 (adalimumab) VOLTAIRE-RA RA Cohen et al 2018 [63] CHS-1420 (adalimumab) PsO, PsA NCT02489227 Hodge et al 2017 [64] FKB327 (adalimumab) RA ARABESC-OLE Genovese et al 2017 [65] GP2017 (adalimumab).
4% of continued SB5, continued RP, and RP/SB5 pts discontinued treatment before end of study ADAs were detected in 13. 2% and 15. 0% of pts in the continued and switch groups, respectively CT-P10 treatment was well-tolerated 8% and 15% continued CT-P10 and switch pts discontinued before end of study Incidence of hypersensitivity and infusion- related reactions was similarly low in both treatment groups No NABs were reported No clinically meaningful differences in AEs between treatment groups Efficacy outcomes • PASI improved over time and • was similar between treatment groups • No impact of switch on efficacy • • Switching from RP to • MSB11022 did not impact efficacy • ACR20/50/70 rates were • comparable across treatment groups • • Mean change in DAS28-ESR and • proportion of pts with good/ moderate EULAR responses was comparable between groups • • NR • • • Duration after switch (weeks) 34 22 36 28 56 24 Study/switch design  † DB RCT Multiple switches: 3 alternating 6w periods between w17 and w35 Pooled continuous treatment pts vs pooled switched pts OL RCT Single switch at w24 Continued GP2017 pts vs RP/ GP2017 switch pts Double blind RCT Single switch at w16 Continued MSB11022 pts vs continued RP pts vs RP/ MSB11022 switch pts DB RCT Single switch at w24 Continued SB5 pts vs continued RP pts vs RP/SB5 switch pts OLE study Single switch after completion of a 48w or 72w DB study Continued CT-P10 pts vs RP/CT-P10 switch pts DB RCT Single switch in patients receiving the RP prior to randomization Continued RP pts vs RP/GP2013 switch pts N* 379 284 443 542 87 107 Study Indications ADACCESS PsO Blauvelt et al 2018 [66] NCT02744755 RA ADMYRA [69] MSB11022 (adalimumab) NCT02660580 PsO AURIEL-PsO [68] SB5 (adalimumab) NCT02167139 RA Weinblatt et al.
2018 [67] CT-P10 (rituximab) NCT01873443 RA Park et al. 2017 [77] GP2013 (rituximab) ASSIST-RT RA Tony et al. 2019 [78] [28][29][30]tudy• Small group sizes *Number of patients randomized to a whole study with a switch design; for OLEs, patients randomized to the OLE. †Switching could occur in a single arm or both arms. Studies included in this table were identified from three systematic reviews of the literature[28][29][30]combined with a PubMed search performed in March 2019. ACR: American College of Rheumatology; ADA: antidrug antibody; AE: adverse event; AS: ankylosing spondylitis; ASAS: Assessment of Spondyloarthritis international society; CRP: C-reactive protein; DAS28-ESR: Disease Activity Score in 28 joints using erythrocyte sedimentation rate; DB: double-blind; HAQ-DI: Health Assessment Questionnaire Disability Index; hsCRP: high sensitivity C-reactive protein; IBD: inflammatory bowel disease; OL: open-label; OLE: open-label extension; NAb: neutralizing antibody; PASI: Psoriasis Area and Severity Index; PK: pharmacokinetics; PsA: psoriatic arthritis; PsO: psoriasis; pts: patients; RA: rheumatoid arthritis; RCT: randomised controlled trial; RP: reference product; SAE: serious adverse event; SpA: spondyloarthritis; TEAE: treatmentemergent adverse event; w: weeks.
Image analysis was performed by using ZEISS ZEN software (Zeiss SAS, Germany) and IMARIS Software (Bitplane, Switzerland). ETs were identified as shapes of DNA extruding from cell and stained by nucleic acid probe SYTOX orange. NETs were defined as extracellular DNA filaments stained by Sytox Orange extruding from cells labeled by MPO Ab, while cell MBP, EDN, ECP or IL-5R labeling characterized EETs. To qualify eosinophil and neutrophil cell infiltrate, hematoxylin-eosin coloration of serial sections was performed for these 11 biopsy specimens. A rating from 0 to + + + was assigned by two operators to eosinophil and neutrophil infiltrates in the papillary dermis and in the blister cavity to allow a comparative semi-quantification of these cells.
S. National Institutes of Health, Bethesda, USA. ). Briefly, we skeletonized the fluorescent images, setting 40 pixels as the minimal resolution, and transformed these images into drawings of DNA filaments, which were measured and processed by the "Analyze particles" tool (Supplementary Figure  S1 ). Area of each filament was measured, and the mean was calculated, making the conversion from pixels to µm (1pixel = 0. 625 µm). The total number of cells was counted using the counter cell tool of Image J R software on the transmitted image acquired simultaneously with fluorescent image. Thus, this method allowed measuring the total number of PMNs, and the total area of NETs. Results were expressed in mean area of NETs per PMN (as proposed by Rebernick et al. (36). As variations were observed when using PMNs from different subjects, results were also expressed in ratio between mean area of NETs per cell for condition X and mean area of NETs per cell for the reference condition with the same PMNs.
Statistical Analysis As the distribution of the variables could not be assumed to be normal, comparisons between two groups were performed using the Wilcoxon matched pairs signed rank test for paired data and the Mann-Whitney test for unpaired data. When more of two matched groups were compared, Friedman test was performed. Ratio paired t-test was used to compare NETs induction by sera with and without treatment (by cytokines, methylprednisolone or compound A). Statistical difference was considered significant if p-value was 0. 05 or less.
Subsequently, our results showing NETs at the dermal-epidermal splitting area, along with the serum capacity to induce NETs at the same time, really advocate for a role of neutrophil in extracellular DNA release, although NET implication in BP still needs to be further demonstrated. Based on the literature, we hypothesize that NET-associated mechanisms may be involved in BP180 antigen immunogenicity, and further in epitope spreading in BP. histological metadata collection. DG, EB, CT, KD, SL, SN, CM, and BP contributed to the sample processing. CT conceived the macro allowing ex vivo NET quantification. DG, EB, and SL performed the statistical analyses. DG, PB, FA, and BP wrote the manuscript and all authors reviewed and approved the manuscript.
Molecular data We combined the RNA-sequencing (RNA-seq) datasets from Bueno and colleagues  [12]  (n = 211) and the TCGA  [13]  (n = 73). Note that we excluded from the full TCGA-MESO cohort of 86 samples, the samples that were excluded in the final report  (13 samples). Additionally, we conducted immunohistochemistry (IHC) on two datasets: (1) Tissue MicroArrays (TMAs) of a subset of 106 samples from the Bueno and colleagues study  [12] , which acts as an orthogonal technical validation; and (2) an independent cohort of 77 samples from the French MESOBANK, which acts as a replication of our results. TMAs were done from 106 cases of MPM, three cores per sample of 0. 6 mm of diameter each were used to make six recipient blocks of TMA. The replication dataset of 77 samples come from the French MESOBANK, a multi-centric virtual and exhaustive repository of national data, biological samples, and standardized operational procedures for mesothelioma.
This database contains histopathological data for more than 10,000 specimens  [14]. The 77 samples were selected from three groups: a longsurvival epithelioid group (survival >30months), a short-survival epithelioid group (survival <10 months), and a sarcomatoid group (survival <10 months). The samples from the three groups were matched for age (≤6 years difference) and sex, were all chemonaive at the time of sample collection but all underwent cisplatin and/or pemetrexed chemotherapy afterwards. Although not matched, we confirmed that smoking status and asbestos exposure were balanced between the three groups (Fisher's exact tests p > 0. 05).
Pathological review and clinical data Tumour grade, infiltration, and the presence of necrosis were assessed for all 284 samples from digital H&E slides of FFPE. The slides from the TCGA cohorts were visualized from the cancer digital slide archive (http://cancer. digitalslidearchive. net/, accessed in January and February 2018). The histopathological types based on the 2015-WHO classification (epithelioid, biphasic, sarcomatoid) and the clinical information (sex, age, survival, asbestos exposure, pre-treatment, surgery) were retrieved from the supplementary tables of the corresponding manuscripts. The 77 samples from the French MESOBANK replication cohort have all undergone a Central Pathological Review (French standardized procedure of certification of mesothelioma) and contained clinical information on the same variables.
RNA-seq data processing The 284 raw reads files were processed in three steps (bioinformatic workflow freely available at https://github. com/IARCbioinfo/ RNAseq-nf):  [15] [16] [17]  (i) reads were scanned for Illumina adapter sequence using software Trim Galore v0. 4. 2; (ii) reads were mapped to reference genome GRCh38 (gencode version 24) using software STAR v2. 5. 2b; and (iii) reads were counted for each gene of the comprehensive gencode gene annotation file using software htseq v0. 8. 0. We quantified the proportion of cells that belong to different immune cell types from the RNA-seq data using software quanTIseq  [18]  (Table  S1 ). In a nutshell, quanTIseq performs a supervised deconvolution based on the expression signature of a reference panel of blood-derived immune cells from ten different types.
Low-dimensional summary of expression data The raw read counts of the 284 samples were normalized using the variance stabilization transform (R package DESeq2 v1. 18. 1)  [19]. The genes that displayed the largest variance (7145 genes representing 50% of the total variance; Table  S2 ) were then meancentered and selected to compute a low-dimensional summary using Principal Component Analysis (PCA; Table  S1 ). Indeed, the visualization of the expression levels across samples for 7145 genes theoretically requires a plot in 7145 dimensions, which would be impossible to interpret. PCA overcomes this issue by reducing the high-dimensional data to a few independent dimensions, each representing groups of genes with correlated expression levels. One limitation of PCA is that it assumes linear relationships between the low-dimensional representation and original variables (gene expression, in our case).
Nevertheless, contrary to alternative nonlinear techniques, this linear relationship results in interpretable dimensions  [20]. In other words, PCA makes data easy to explore and visualize by reducing the dimensionality of a data set consisting of many variables correlated with each other, while retaining as much as possible the variation present in the original dataset. Note that we report the first two dimensions of the PCA in the results section, because the other main dimensions (from three to seven) explain each <5% of the gene expression variation among the 7145 selected genes, they were not significantly associated with the histopathological type (ANOVA q > 0. 05), and none except Dimension 5 were associated with survival (Wald test p > 0. 05; Table  S3 ). Importantly, the samples with pre-treatment (31 samples with pre-surgical chemotherapy from the Bueno et al.
cohort) did not have significant associations with any of the seven axes. Also note that all 199 samples included in the survival studies underwent similar treatments (chemotherapy plus surgery). Correlation circles were constructed from the correlations of variables on each dimension. We performed a five-gene PCA following the same protocol, but using only a subset of five genes highly correlated with PCA Dimensions 1 and 2, for which IHC-validated antibodies were available (CD8A, VEGFR2, VEGFR3, PDL1, and VISTA). We compared the PCA performed on all genes (hereafter simply denoted "PCA") to that performed on the reduced gene set (denoted "five-gene PCA") using the Kabsch algorithm, which finds the rotation that minimizes the deviation between two sets of points. PCAs were independently conducted on the validation and replication IHC datasets, after mean-centering expression levels.
We used hierarchical clustering on the replication IHC dataset to ensure that the protein expression profiles of the short-and long-survival epithelioid sets were not biased by misclassifications. Indeed, if misclassifications-which are common in MPM, in particular biphasic samples misclassified into epithelioids and sarcomatoids-disproportionately happened between sarcomatoids and short-term survival epithelioids in our series, we would expect the protein expression of the epithelioid short-survival set to be biased toward that of the sarcomatoid set. Using hierarchical clustering analysis on the five-protein expression data (Fig. S2 ), we found that most sarcomatoid and epithelioid samples had distinct expression patterns, and that there were similar numbers of shortand long-survival epithelioids that had an expression profile inbetween that of epithelioid and sarcomatoid samples (four and two samples, respectively, possibly misclassified biphasics).