dataesr/scientific-paragraphs-categorization huggingface dataset:
The authors also mention three ongoing clinical trials; two of which use pharmaceutical derivatives of RvE1 (i. e. , RX-10045 and RX-10001) and one study using a LxA4 analog, BLXA4-Me  (7). The indications are the following: signs and symptoms of dry eye (NCT00799552, available on the clinicaltrial. gov website), gingivitis (NCT02342691), and a single and multiple ascending oral dose study in healthy volunteers (NCT00941018). In addition to this review  (7) , a recent study reports the use of another SPM maresin-1 in experimental autoimmune encephalomyelitis (EAE), a mouse model for MS  (101). Besides these studies reporting the use of SPM in animal models of chronic diseases associated with non-resolving inflammation, SPM (7) -like other pro-resolving mediators (9)-have been tested in experimental peritonitis in order to assess their impact on neutrophils present in the peritoneal exudate and the kinetics of their elimination by macrophages.
Funding This work is supported by the  Agence Nationale de la Recherche (ANR)  under the program " Investissements d'Avenir " with reference  ANR-11-LABX-0021-LipSTIC , by the  Region Bourgogne Franche-Comté (support to  LipSTIC LabEX ), the  MiMedI  project funded by  BPI France  (grant No. DOS0060162/00 ), and the  European Union  through the  European Regional Development Fund of the Region Bourgogne-Franche-Comté (grant No. FC0013440 ).
2  Similarly, the 2018 Asia Pacific League of Associations for Rheumatology axSpA treatment guidance does not recommend stopping treatment completely. 3  The 2019 American College of Rheumatology recommendations for nr-axSpA advise against tapering of biologics as a standard approach. 4  Patients with axSpA typically have disease onset early in life  9  and are likely to start TNFi treatment early; therefore, treatment withdrawal upon achieving inactive disease is especially relevant. Data on outcomes following treatment cessation, as well as understanding which patients might best respond, would be highly informative to patients and physicians. For ETN, clinical trial data remain sparse regarding maintenance of "remission" in nr-axSpA after treatment discontinuation. RE-EMBARK was a postapproval study requested by the European Medicines Agency to assess the frequency of flare after ETN withdrawal among patients with inactive disease and the efficacy and safety of retreatment after flare.
METHODS Study design and participant selection. RE-EMBARK (ClinicalTrials. gov: NCT02509026) was a multicenter, open-label, 3-period study that ran from September 2015 through September 2019. Patients with nr-axSpA enrolled at 62 sites across 14 countries (Figure  1 ). In period 1 (P1), all patients were treated with subcutaneous ETN 50 mg once weekly for 24 weeks, plus a stable background NSAID at the optimal tolerated antiinflammatory dosage per the investigator. Patients who achieved ASDAS with C-reactive protein (ASDAS-CRP) < 1. 3 at week 24 (therapeutic target) entered period 2 (P2), a 40-week withdrawal period where patients discontinued ETN but maintained background NSAID. Patients who flared before week 64 entered a 12-week retreatment period (period 3 [P3]) with ETN 50 mg once weekly.
Enrollment of 200 patients was expected to result in 96 entering treatment withdrawal, and 70% were expected to flare following withdrawal, giving a half-width of the 95% CI on flare rate estimate of approximately 10%. Binary endpoints were summarized as numbers, percentages, and 95% CIs. For continuous endpoints, raw and change-from-baseline data were summarized using descriptive statistics. P1 baseline values were defined as the last available value of the screening and baseline visits. The P2 baseline was defined as the last value before treatment withdrawal. The P3 baseline was defined as the last P2 value before retreatment. P values for 480 Etanercept withdrawal in nr-axSpA change-from-baseline data were calculated using the paired t test. Analyses were performed for observed and last observation carried forward (LOCF) data, but no baseline values were carried forward.
3% vs 64. 3%) and Asian (27. 6% vs 5. 2%) and fewer were White (67. 6% vs 91. 3%; Supplementary Table  S3 , available with the online version of this article). Disease characteristics following 24 weeks of ETN treatment were broadly similar for RE-EMBARK and EMBARK; where differences were noted, disease burden was usually higher in EMBARK than in RE-EMBARK. A significant decrease in ASDAS-CRP scores was observed at all P1 postbaseline visits (ie, weeks 4-24; Supplementary Table  S4 , available with the online version of this article). Flare within 40 weeks of ETN withdrawal (P2). The proportion of patients experiencing flare increased during P2 from 22. 3% (25/112; data for 3 patients not available) at week 28 to 67% (77/115) at week 64 (LOCF, P2 baseline values not carried forward; Figure  3A ).
The current treatment paradigm for axSpA is "continuous treatment. "  2, 4  Studies such as RE-EMBARK challenge this paradigm, suggesting that Half of the RE-EMBARK patients who achieved inactive disease maintained ASDAS-ESR < 2. 1 (ie, low disease activity) over at least 16 weeks following ETN withdrawal. As temporary treatment discontinuations may be necessary (eg, planned surgery), these data can inform shared decision making regarding treatment interruptions and provide some reassurance that ETN interruptions of 16 weeks or less may be associated with sustained responses in a substantial portion of patients. Our analyses of baseline predictors of inactive disease maintenance after ETN withdrawal suggest that patients with both hs-CRP and MRI positivity at baseline, indicative of more severe disease, are significantly less likely to maintain inactive disease status following ETN withdrawal compared with patients with either negative MRI or normal hs-CRP.
30, 31  Nevertheless, sex was not a factor associated with maintenance of inactive disease following ETN withdrawal. Study limitations include the open-label design and absence of a control group, which may overestimate treatment effect. 32  Interpretation is also limited by the relatively small sample size in P2 and P3. To allow comparison of time-to-flare data from the EMBARK study, the 2 studies were similar for inclusion and exclusion criteria; however, this may limit generalizability given that participants in RE-EMBARK represent a selected clinical trial population. Supporting the cross-study comparison with EMBARK, patient demographics and baseline disease characteristics were generally similar between the studies, although more patients in EMBARK were Asian, likely because of differences in study locations.
Only those patients in EMBARK who met the entry requirements specified for the withdrawal period of the RE-EMBARK study were included in this analysis. Another limitation was using ASDAS-CRP to define inactive disease and ASDAS-ESR to define flare, for practical reasons; although these measures are clinically comparable, they are not identical. The ASDAS-ESR, however, serves as an identifier of flare/disease activity relapse. In addition, RE-EMBARK did not investigate dose reductions or changing the dosing interval, although tapering treatment is recommended for some patients. [2] [3] [4]  Data from the randomized controlled C-OPTIMISE study  26  as well as open-label  33, 34  and observational  [35] [36] [37] [38]  studies of TNFi suggest that dose tapering is an effective way of controlling axSpA in some patients and that such tapering is noninferior to full dose.
Longitudinal axial MRI and radiographic data were not collected, so the effect of ETN withdrawal on irreversible structural damage could not be assessed. In addition, it was not necessary for patients in P1 to have sustained inactive disease at week 24 (ie, over > 1 study visit) to enter P2. As patients in RE-EMBARK were retreated with ETN after 1 flare, the study also did not address the potential effect of ETN retreatment in patients with more than 1 flare after ETN withdrawal. Finally, the analysis of baseline predictors of maintaining inactive disease after ETN withdrawal and regaining inactive disease upon retreatment was post hoc in nature. In conclusion, whereas most patients with nr-axSpA who achieved inactive disease with ETN flared after ETN withdrawal, a quarter of the patients did not experience ASDAS flare for 40 weeks despite treatment discontinuation.
ACKNOWLEDGMENT The authors wish to thank all patients and study investigators who participated in the RE-EMBARK study. Medical writing support was provided by  Lorna Forse, PhD , and  Iain McDonald, PhD , of  Engage Scientific Solutions , and was funded by  Pfizer. Some of the data in this manuscript were previously presented at the 2020 virtual European Alliance of Associations for Rheumatology Congress as an oral presentation (abstract number:  OP0107  39 ).
DATA AVAILABILTY The RE-EMBARK clinical trial protocol is available at https://clinicaltrials. gov/ProvidedDocs/26/NCT02509026/Prot_001. pdf. Upon request, and subject to review, Pfizer will provide the data that support the findings of this study. Subject to certain criteria, conditions, and exceptions, Pfizer may also provide access to the related individual deidentified participant data. See https://www. pfizer. com/science/clinical-trials/trial-data-and-results for more information.
Vaccinia-specific long-lived MBCs are enriched in a splenic CD21 hi IgG + MBC subset. Selection of long-lived MBCs as a separate MBC lineage during the early stages of an immune response would most likely result in a unique transcriptional signature for this subset. To test this hypothesis, we next performed single-cell RNA sequencing (scRNAseq) on single-cell sorted B5-specific IgG + MBCs, as well as paired total IgG + MBCs and naive B cells from the spleen of six donors. The generated scRNA-seq dataset was further integrated with a previously published dataset which included splenic MBCs from three adult organ donors and three immune thrombocytopenic purpura (ITP) adult patients as well as splenic naive, GC and MBCs from four young patients with sickle cell disease  (Crickx et al. , 2021)  (see Table  S2A ).
S7L-M). IgG + splenic MBCs acquire elongated telomeres that last for decades. An additional key requirement for long-term survival of any cell is to avoid entry into senescence. Telomeres, which protect chromosome extremities, are notably shortened upon each cellular division as well as under external stress  (Rossiello et al. , 2022)  and this can lead to shortening of telomeres below a critical length, usually 4-5 kb. HSCs or tumor cells thus specifically re-express enzymes participating to telomere elongation, in particular TERT, the catalytic subunit of the telomerase complex  (Blackburn et al. , 2015). None of these enzymes, apart from NHP2, appeared significantly over-expressed at the mRNA level in resting MBCs in our scRNA-seq dataset (Figure  7A ). In contrast, strong expression of numerous members of the shelterin (dedicated to structure and protection of telomeres (de Lange, 2018)) and telomerase complex could be detected in actively dividing Accepted manuscript / Final version GC B cells, in line with the previously described telomerase activity in GC B cells  (Hu et al.
, 1997; Martens et al. , 2002; Norrback et al. , 2001; Weng et al. , 1997). TERT transcripts could not be detected in any of the assayed cell types in our scRNA-seq dataset. To study telomere length in long-lived MBCs, in a setting that would allow studying only a few hundred cells and allow the resolution of B cell populations beyond the simple separation of CD27 + memory B cell and CD27 -naive B cells  (Martens et al. , 2002; Son et al. , 2003; Weng et al. , 1997) , we turned to a FISH-based assay  (Benyelles et al. , 2019) (Figure  7B ). Separating IgG + CD27 + MBCs from unswitched IgD + CD27 + marginal zone B cells revealed a striking difference between the two populations in the intensity, but not number, of imaged telomere spots (Figure  7C-E ). The significantly increased fluorescence intensity in IgG + MBCs as compared to naive B cells suggested elongated telomere length that was further confirmed using a classical TRF assay (13.
Cells were then washed in RPMI-1640 and fixed in 2% PFA. Samples were acquired using a LSR Fortessa SORP (BD Biosciences). Data were analyzed with FlowJo software. Detailed gating strategies for individual markers are depicted in Figure S1. To account for donor-specific variations in CD21 fluorescent intensities, CD21 hi CD20 hi and CD21 int CD20 int gates were always set-up so that the CD21 hi CD20 hi gate would encompass 5% of the naive B cell population in that sample (Figure  3D ). For CD18, CD11b, NOTCH2, CD84, TLR10, TACI and BAFFR (Figure  5C-E  For cell sorting of major B cell population, cells were stained using the same protocol, without the final fixation step, and then sorted on a BD FACS cell sorter Aria III (BD Biosciences, 4-way purity mode). Throughout this study naive B cells were sorted as live Accepted manuscript / Final version were incubated 30 min at 37 °C in RPMI 2% FCS with TMRE (tetramethylrhodamine, ethyl ester) at 25 nM (Thermo Fischer, Cat#T669) and Mito ID Red (1/10 000, Enzo, Cat# ENZ-51007-0100).
Affinity measurement using biolayer interferometry (Octet) Biolayer interferometry assays were performed using the Octet Red96 (Sartorius). Anti-Human Fc Capture (AHC) biosensors (Sartorius Cat# 18-5060) were immersed in supernatants from single-cell MBC cultures or re-expressed monoclonal antibody at 25 °C for 500 seconds. Biosensors were equilibrated for 180 s in 1x PBS buffer with 0. 1% BSA and 0. 005% Tween 20 surfactant (PBS-BT) prior to measurement. Association was performed for 600s in PBS-BT with B5 protein at 100nM followed by dissociation for 900s in PBS-BT for each cycle. Biosensor regeneration was performed by alternating 30s cycles of regeneration buffer (glycine HCl, 10 mM, pH 2. 0) and 30 s of PBS-BT for 3 cycles. Traces were reference sensor (unloaded sensor) and reference well (additional association cycle in the absence of B5) subtracted and curve fitting was performed using a local 1:1 binding model in the HT Data analysis software 12.
Single-cell RNA-seq The scRNA-seq dataset from 3 ITP patients (ITP1 to ITP3), 4 YD (YD1 to YD4) and 3 HD donors (HD1 to HD3) has been previously published (ArrayExpress database: E-MTAB-9955)  (Crickx et al. , 2021). A second dataset containing sorted naive, IgG + and B5 + IgG + memory cell had been generated in parallel from 6 HD donors (HD4 to HD9) using the same protocol. Briefly, single-cell mRNA sequencing was performed according to an adapted version of the SORT-seq protocol  (Muraro et al. , 2016)  with primers described in van den  Brink et al. (van den Brink et al. , 2017). Cells were single-cell FACSsorted (AriaIII) into 384-well plates containing 384 primers and Mineral oil (Sigma). After sorting, plates were snap-frozen on dry ice and stored at -80 °C. Each plate was designed to contain cells from each donor group.
Of note, sorted naive/IgG + and IgG + B5 + B cells included in the dataset described in this study were prepared using a fast thawing protocol Accepted manuscript / Final version and a subsequent apoptotic cell removal step using the Dead Cell Removal kit (Miltenyi). Sorted cells included in the previously published dataset  (Crickx et al. , 2021)  were prepared using a slow thawing protocol at 37 °C (adapted from 10X genomics CG00039 demonstrated protocol; https://support. 10xgenomics. com/single-cell-vdj/sampleprep/doc/demonstrated-protocol-fresh-frozen-human-peripheral-blood-mononuclearcells-for-single-cell-rna-sequencing), without any additional step. Further processing of cells, cDNA libraries generation, sequencing and reads alignment was performed at Single Cell Discoveries. For amplification, cells were heat-lysed at 65 °C followed by cDNA synthesis using the CEL-Seq2 protocol  (Hashimshony et al.
(Grün et al. , 2014). Mapping and generation of count tables was automated using the MapAndGo/starmap script (https://github. com/annaalemany/transcriptomics/tree/master/mapandgo). A total of 3867 MBCs, 1194 Vaccinia-specific MBCs, 1812 naives B cells and 192 GC B cells were sequenced in both datasets (Table  S3A ). Initial quality control and cell filtering was performed using the R Scater package  (McCarthy et al. , 2017). We removed cells where more than 50% of transcripts mapped to ERCC spike-ins, cells with less than 2000 UMIs or 500 genes detected and kept only genes that were detected in at least 2% of any given sorted cell population. Index-sorting information and parallel in vitro validation using single culture were additionally used to optimized gating strategies and remove potential sort contaminants from further analysis in the context of IgG + B5 + MBCs sorts.
Accepted manuscript / Final version Normalization, integration and clustering were then performed through the R Seurat v3  (Stuart et al. , 2019)  pipeline. Transcript counts were first normalized using the scTransform algorithm  (Hafemeister and Satija, 2019) , additionally correcting for potential bias related to the detected percentage of mitochondrial genes and selecting for the top 3000 variable features for downstream visualization and clustering analysis. After principal component analysis, sort-specific batch effects were removed using the Harmony algorithm  (Korsunsky et al. , 2019). The first 30 corrected principal component analysis dimensions were then used to construct a knn graph (k = 20 neighbors) and perform graph-based clustering (Louvain) with a resolution parameter of 0.
6 as well as compute the UMAP coordinates for each cell. Final clustering steps as presented in the manuscript were focused solely on sorted MBCs. Dotplots displaying scRNA-seq gene expression (scaled normalized counts) were generated using the Seurat Dotplot() function. Size of dots represents the percentage of cells in the cluster in which transcripts for that gene are detected. Dot color represents the average expression level (scaled normalized counts) of that gene in the population. Heatmap displaying row-scaled pseudobulk expression averaged by cluster and donor were done combining the Seurat AverageExpression() function and the ComplexHeatmap package  (Gu et al. , 2016). Differential expression analyses between clusters of interest were then performed using the general linear model (GLM) likelihood ratio test implemented in the edgeR package  (Chen et al.
, 2009). Open regions were determined with Model-Based Analysis of ChIP-Seq2 (MACS2)  (Feng et al. , 2012)  and filtered with a value of q < 0. 001. Read counts were determined by using featureCounts. py from the SubRead package  (Liao et al. , 2013) , and differentially opened (DO) regions were identified with DESeq2  (Love et al. , 2014)  with a fold-change in accessibility > 2 and an adjusted value of P < 0. 05 after Wald's test and Benjamini-Hochberg correction. Generated libraries from one of the donors did not reach our quality control cut-offs and all ATAC-seq data from that donor were excluded from further analysis. Graphical representations, data integration, and statistical analyses were generated with R.
Acknowledgements We thank  Sandra Weller  for her invaluable advices;  Garnett Kelsoe  for the MS40L lo human cell culture system;  Alexander van Oudernaarden  and  Judith Vivier  ( Hubrecht Institute, Netherlands ) for initial advices regarding scRNA-seq analysis;  MetaToul (Metabolomics & Fluxomics Facitilies, Toulouse, France)  and its staff members ( Maud Heuillet ,  Floriant Bellvert  and  Edern Cahoreau ) for carrying out the metabolomic analysis;  Nicolas Goudin  and  Beatrice Durel  ( SFR Necker, Paris, France ) for help regarding image acquisition and analysis;  Laëtitia Kermasson  ( Imagine Institute, Paris, France ) for help regarding the setup of Telo-FISH assays;  Christophe Quétard  ( SARTORIUS, France ) for advice on the use of the OCTET Red instrument;  Jerry W. Shay  and  Woodring E.
Funding: This work was funded by the  Fondation Princesse Grace , by a Mérieux grant and by  ERC Advanced Grants  (Memo-B and B-response). M. A. E was supported by a grant from the  Fondation EDF. MetaToul is part of the national infrastructure MetaboHUB- ANR-11-INBS-0010. P. R. is a scientist from  Centre National de la Recherche Scientifique (CNRS).
Computational analyses of VDJ sequences Processed FASTA sequences from cultured single-cell VH sequencing were annotated using Igblast v1. 16. 0 against the human IMGT reference database. Clonal cluster assignment (DefineClones. py) and germline reconstruction (CreateGermlines. py) was performed using the Immcantation/Change-O toolkit  (Gupta et al. , 2015)  on all heavy chain V sequences. Sequences that had the same V-gene, same J-gene, including ambiguous assignments, and same CDR3 length with maximal length-normalized nucleotide hamming distance of 0. 15 were considered as belonging to the same clonal group. Mutation frequencies in V genes were then calculated using the calcObservedMutations() function from the Immcantation/SHazaM v1. 1. 0 R package. VH repartitions were calculated using the countGenes() function from the Immcantation/alakazam v1.
Common Disease Characteristics Several characteristics of GPP have become apparent from published case reports. It has been suggested that there may be a female preponderance in GPP; however, the extent of this varies by geographical region and between ethnicities. A study in 102 patients from Malaysia reported a 2:1 ratio of GPP in females versus males; a study of 74 patients in the US reported a 1. 03:1 ratio; and in a study of over 700 patients in Japan, 51. 5% were female  [9, 10, 14]. Findings from a recent inpatient database of 1516 people with GPP in Japan reported that 56% were male, and a registry in Western Japan observed that 52% of 102 GPP cases were male  [15, 16]. GPP can present at any age, including as juvenile GPP  [8] ; however, the majority of cases present in the fifth decade and the mean age at diagnosis has been reported as 45.
Challenges in Determining the Epidemiology of GPP Since GPP is a rare disease, clinical awareness is limited and inaccurate diagnosis presents challenges to estimating disease prevalence. Considering the available evidence, GPP is understood to affect all populations, but prevalence varies by geographical region and between ethnicities. There is a large degree of global and regional variability in the prevalence of GPP in the published literature. Interpreting the available data is challenging because of the different methodologies, populations, and definitions of GPP used across studies, as well as the inclusion, or lack of differentiation from, palmoplantar pustulosis. In many cases, data from hospital audits or claims database analyses have not been extrapolated to reflect national population-level prevalence estimates.
This means caution must be used in drawing wide-ranging or comparative conclusions of prevalence estimates from the available studies. Over the past two decades, several initiatives that provide national population-level insights into the epidemiology of GPP have been published (Table  1 )  [32] [33] [34]. In France, a study conducted in 2004 using a questionnaire-based assessment in 121 hospital dermatology wards showed 99 cases of GPP across 46 wards, leading to an estimated disease occurrence of 1. 76 cases per 1,000,000 persons  [32]. The authors noted this estimate was similar to earlier findings in 1998 and 2001 from the Caisse Nationale d'Assurance Maladie des Travailleurs Salariés (CNAMTS) French health insurance database  [32]. Prevalence data in another study were obtained using questionnaires sent to 575 community center hospitals throughout Japan that captured details of visiting patients with GPP from 1983-1989.