dataesr/scientific-paragraphs-categorization huggingface dataset:
Objectives. Nailfold videocapillaroscopy (NVC) plays a well-established role in differentiating primary from secondary RP due to SSc. However, the association of NVC with novel severe organ involvement/progression in SSc has never been evaluated in a multicentre, multinational study, which we now perform for the first time. Methods. Follow-up data from 334 SSc patients [265 women; 18 limited SSc (lSSc)/203 lcSSc/113 dcSSc] registered between November 2008 and January 2016 by seven tertiary centres in the EUSTAR-database, were analysed. Novel severe organ involvement/progression was defined as new/progressive involvement of the peripheral vasculature, lungs, heart, skin, gastrointestinal tract, kidneys, musculoskeletal system, or death, at the 12-or 24month follow-up. NVC images at enrolment were quantitatively and qualitatively evaluated according to the standardized definitions of the EULAR Study Group on Microcirculation in Rheumatic Diseases.
Finally, two systematic reviews devoted to this subject have corroborated these findings  [11, 12]. However, as the association between NVC and future severe organ involvement and/or progression has never been thoroughly evaluated in a multicentre, multinational study, we set up such a study for the first time in a joint collaboration of the European Scleroderma Trials and Research Group (EUSTAR) and the EULAR Study Group on Microcirculation in Rheumatic Diseases. S1 , available at Rheumatology online). The study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from each patient before registration in the EUSTAR-database.
Study population and design Data were collected over a 24-month follow-up period on consecutive SSc patients (excluding overlapping syndromes), classified according to the 1980 ACR or 2013 ACR/EULAR criteria for SSc, and stratified into subsets according to LeRoy and Medsger [limited systemic sclerosis (lSSc), lcSSc and dcSSc], prospectively registered in the EUSTAR-database between November 2008 and January 2016  [27, 29, 30]. Patients eligible for this study were adult SSc patients, regardless of disease duration, in whom NVC had been performed on their first date of registration in the EUSTAR-database (considered the baseline). Data on demographics and disease-specific investigations (i. e. clinical, serological and diagnostic data) at baseline and at dates closest to the 12-and 24-month follow-up were included for analysis.
The strength of associations was summarized as odds ratios (ORs) and 95% CIs. Firth logistic regression was used in case of (quasi-)complete separation  [37]. The risk of overfitting was assessed using the 1-in-10 rule of thumb (Supplementary Data S1b, available at Rheumatology online)  [38]. For descriptive statistics, data for categorical variables are presented as absolute numbers and percentages, and for continuous variables as means with S. D. , unless otherwise stated. Significance was defined as a Pvalue < 0. 05. All statistical analyses were performed using IBM SPSS statistics Version 26 (IBM SPSS Inc. , Chicago, Illinois, USA), extension 'STATS FIRHTLOG'.
Against this background, an important caveat for our study is that, although a large sample size was examined, an unexpectedly low proportion of patients developed novel pulmonary involvement (1. 4% PAH and 5. 3% ILD) during follow-up. One would have expected an incidence of 10% and 50% for novel PAH and ILD, respectively  [34, 46, 47]. Our observed low incidence could be partly explained by the already relatively long disease duration of our patients and partly by an insufficient follow-up time  [46] [47] [48] [49] [50]. Another explanation could be that there was a flaw in pulmonary involvement reporting inherent in the data collection for the EUSTAR-database. FIG. 1  Significant associations between NVC and novel severe organ involvement/progression in SSc, after MLR. a Cross-sectional association.
Note that capillary density is cross-sectionally associated with the presence of ILD. For capillaroscopic image acquisition, a nailfold videocapillaroscope with a Â200 magnification was used. Each centre was asked to place a 1-mm grid on each image, using centre-dependent image analysis. Images were evaluated quantitatively (i. e. capillary density, capillary dimension, capillary morphology and presence of microhaemorrhages) and qualitatively [i. e. presence/absence of a scleroderma pattern (early, active or late)] according to the standardized definitions of the EULAR Study Group on Microcirculation in Rheumatic Diseases, by a single trained assessor (K. M. ) who was blinded to the clinical data  [17].
g. gastrointestinal involvement); this was inherent in the design of the EUSTAR-database. Consequently, events in these groups may be infrequent, and our study may be underpowered. Although not within the aims of our study, future studies may overcome this limitation by examining associations with more frequent, and therefore less severe, SSc-related organ involvement. Third, because NVC was performed only once, at the time of enrolment, we may have overlooked associations with the microvascular alterations that usually occur during the natural course of the disease. Moreover, the statistical analysis only considered the effect of the covariables that were present at the time of enrolment, thus excluding possible changes that may have occurred during follow-up. Another limitation was the missingness of data inherent an an observational dataset, such as the EUSTAR-database, which forced us to perform several imputations (i.
, 2020; Denizot et al. , 2012; Sasaki et al. , 2007; Douadi et al. , 2022). Based on these studies, many AIEC-targeting strategies are currently under investigation in preclinical models and in clinical trials to limit AIEC blooming and invasion of ileal mucosa. For example, the efficacy of antibiotics (TEOREM, NCT02620007) or phagotherapy  (Galtier et al. , 2017)  (EcoActive, NCT03808103) to kill AIEC, or FimH antagonists  (Sivignon et al. , 2015b)  (Sibofimloc, NCT03943446) and yeast probiotics  (Sivignon et al. , 2015a (Sivignon et al. , , 2021) )  to decrease AIEC gut colonization, are currently under study. However, a well-established and validated preclinical model closely mimicking AIEC-induced CD pathogenesis is required to validate new therapeutic agents targeting AIEC bacteria. To date, most published studies have used antibiotic-treated mice, dextran sulfate sodium (DSS)-induced colitis or genetically susceptible mouse models to reach AIEC colonic colonization  (Spalinger et al.
Membranes were incubated for 1 h in blocking buffer (PBS-0. 05% Tween 20, 5% bovine serum albumin) and then blotted with the primary antibodies anti-CEACAM6 (1:5000, 9A6 clone, Genovac GmbH, Germany) and anti-GAPDH (1:5000, Cell Signaling Technology, USA) overnight at 4°C. After three washes with PBS-0. 05% Tween 20, the membranes were incubated for 1 h at room temperature (RT) with horseradish peroxidaseconjugated secondary antibodies diluted in blocking buffer (1:10,000). Membranes were washed three times, and proteins were detected using Enhanced Chemiluminescence substrate (Clarity™ Western ECL substrate, Bio-Rad). Quantification of CEACAM6 expression relative to GAPDH was performed for Vill-hCC6 mice by assessing band intensities (n=4 mice) using Image Lab software 6. 1. 0 (Bio-Rad Laboratories Inc.
Acknowledgements We thank genOway ( Lyon, France ) for generating the Vill-hCC6 mouse model. pUC18R6KT-mini-Tn7T-km-Stp, pUC18R6KT-mini-Tn7T-km-GFP, E. coli MFDpir and E. coli MFDpir/pTNS3 were kind gifts from  Damien Balestrino  ( Laboratoire Microorganismes: Geńome Environnement, Clermont Auvergne University ). We also express our thanks to  Abdelkrim Alloui  from the animal facility ( University Clermont Auvergne );  Alexandra Rezard  and  Sandra Lebeau  ( M2iSH ) for their help with the breeding/genotyping of mice;  Christelle Blavignac  and  Lorraine Novais Gameiro  ( Centre Imagerie Cellulaire Santé-UCA Partner ) for their technical support for paraffin inclusion of intestinal tissues; and  Christelle Soubeyrand-Damon  from the Anipath platform for her help with immunohistochemical analysis.
Study design and participants This was a Phase III open-label, controlled, randomized study conducted from September 2018 to April 2020, at two clinical centers in the Philippines (clinicaltrials. gov, NCT03700242). The study was undertaken in compliance with the principles established by the Declaration of Helsinki and the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Humans Use (ICH) guidelines for good clinical practice (GCP) as well as with all local and/or national regulations and directives. Written informed consent, or assent for those aged younger than 18 years along with parental consent, was obtained from the participants before any study procedures were performed. Healthy individuals aged ! 2 years were eligible for inclusion.
The sample sizes for Groups 3, 4, and 5 were arbitrarily set to 76 participants. Statistical analyses for secondary endpoints were descriptive. Safety and immunogenicity data were summarized by group at each time point. The main safety and immunogenicity parameters were described with their 95 % CI. The CIs for proportions were calculated using the exact binomial method (Clopper-Pearson method). Immunogenicity and safety were also described by the following age groups: 2 to 11 years, 12 to 17 years, and 18 to 64 years. Geometric mean titers (GMTs) and CIs were calculated using the normal approximation of the log10 transformed titers, followed by a back transformation. Immunogenicity analyses were performed on the per-protocol analysis set (PPAS) and confirmed on the full analysis set (FAS).
Acknowledgements The authors would like to thank  Joanna Korejwo  ( Sanofi ) for assistance with initiation of the study, and all study staff and volunteers who participated. Editorial assistance with the preparation of the manuscript was provided by  Juliette Gray  and  Richard Glover , inScience Communications,  Springer Healthcare Ltd, London, UK. Funding for this assistance was provided by  Sanofi. The authors would like to thank  Roopsha Brahma PhD  and  Gouthami Kannabattula  for editorial assistance and manuscript coordination on behalf of  Sanofi.
Acknowledgements: We are very grateful to the patients and to  Bounthaphany Bounxouei , the former Director of  Mahosot Hospital , the late  Rattanaphone Phetsouvanh , Director of the  Microbiology Laboratory, and the staff of the wards and Microbiology Laboratory of Mahosot Hospi-Transactions of the Royal Society of Tropical Medicine and Hygiene tal. We also thank  Bounnak Saysanasongkham , the former Director of the  Department of Healthcare and Rehabilitation, Ministry of Health, and Bounkong Syhavong, Minister of Health, Lao PDR  for their very kind help and support. We thank the stakeholders of the  SEAe  project, 45 members of the  Unité des Virus Émergents (Christine Isnard and Camille Placidi)  and the  CNR des Arbovirus  ( Patrick Gravier ,  Gilda Grard ,  Isabelle Leparc-Goffart  and  Mathilde Galla ).
Gliogenesis at clonal levels To investigate astrocyte clonal arrangements and subtype composition during cortical development, Laura Lopez-Mascaraque's laboratory has developed the StarTrack method that provides a fluorescent code inherited by sister cells and thereby enables the tracking of multiple clones during development. Using this in vivo long-term tracing of astrocyte lineages at clonal levels, Garcia-Marques and collaborators have shown a widespread dispersion of glial clones throughout cerebral cortex and restricted pial and fibrous astrocytic clones in corpus callosum and in cerebral cortex that suggests the existence of specialized progenitors that will contribute to generate astrocyte diversity during development  [35]. Still using the StarTrack strategy, the authors also showed that the clonal behavior of olfactory bulb (OB) astrocytes differ from the cortical ones as the vast morphological diversity of OB astrocytic clones appears layer-dependent rather than determined by lineage, with astrocyte clones comprised of a very limited number of cells and arranged following a radial pattern  [37].
Furthermore, they performed large tissue volume multichannel tracing of multiple layer 2/3 pyramidal neuron processes within a densely multicolor labeled environment which was obtained by in utero electroporation of the non-integrating CAG-Cytbow transgene in mouse cortical progenitors. At the organ scale, serial mosaic imaging of transgenic MM (CAG-Cytbow; Nestin-Cre) brains resulted in a multicolor 3D whole-brain dataset consisting of multiple self-registered three-channel coronal planes acquired every 100 μm from olfactory bulb to brainstem. It provided a comprehensive neuroanatomical map of labeled neurons and glial cells with individual cell resolution at the whole-organ scale where color contrasts facilitate cell distinction. In addition, the authors performed multiplexed brain-wide mapping of neuronal projections on mouse brains injected with three distinct anterograde AAV tracers (AAV-tdTomato, AAV-EYFP and AAV-mTurquoise2) in three distinct areas of the sensorimotor cortex.
Using the whole-brain serial 2D mode of ChroMS combined with high-resolution continuous-mode imaging of sub-volumes within the striatum, they obtained both a global map of the labeled projections and a detailed view of their axonal components in a given region  [47]. Combining expansion and light microscopies with a multicolor approach to decipher the connectome with nanometric details and over several millimeters is nowadays within reach. The technical aspects of expansion microscopy methods are discussed in more detail in the "Imaging solutions and new software tools for quantitative analysis of multicolor data sets", while their main applications and advances are presented hereafter. ProExM has been applied to illustrate the mammalian brain circuitry at high-resolution using confocal microscopy allowing to unambiguously resolve intermingled neurites  [126].
Based on StarTrack labeled images, Salvi and collaborators have developed a different method: the Fluorescent cell Analysis Segmentation Tool (FAST) which is an adaptive, fully automated and multichannel method for the automatic segmentation of neural cells in fluorescent microscopy images and for their classification into clones  [169]. Developed by Ghigo and collaborators, the analysis software "ClusterQuant" enables to determine the clustering index of CFP + cells in Ubow multicolor mice, is specifically designed for statistical analysis of colored cell spatial distribution, and can be applied to any multicolor fate mapping system  [17]. Using ClusterQuant analysis software, Ubow cell types are manually labeled within microscopic images. The subsequent automatic computation of the corresponding Voronoi diagrams is then combined with Monte Carlo simulations to obtain a quantitative statistical measure of the spatial distribution (i.
e. , cluster formation or diffuse cell spreading) of FP-expressing cells  [17]. Previously used for analysis of cell clusters in single Ubow + tissue planes, a 3D version of the ClusterQuant software has been developed and used to explore the tissue clonality of the conventional dendritic cells (cDC). These cDC are a type of leukocytes that play a key role in innate immunity and form a network of immune sentinel cells in most tissues of mice and humans. Cabeza-Cabrerizo and collaborators have developed a multicolor Rosa26 Confetti-based genetic model coupled to flow cytometric spectral analysis which allows separation of closely related fluorophores, including GFP and YFP to label individual cDC progenitors and pre-cDC with one of four possible fluorophores. Using this strategy associated to image analysis and 3D cluster quantification with ClusterQuant, Cabeza-Cabrerizo and collaborators have studied how cDC precursors seed tissues at the single-cell level in the absence of cell transfer.
Collection of data, serum samples and placental tissues Following enrolment, general clinical characteristics were collected from the patients, including maternal age, pregestational BMI, gravidity and parity, smoking habits, preconception comorbidities (such as chronic hypertension, diabetes mellitus, endocrinopathies), pre-conception diseases and current medications. Information on ART treatment was retrieved from the clinic's electronic patient records system (FertiLab Manager), including: the indication for ART treatment, the medication regimen used for the treatment, the treatment's outcome (endometrium thickness, oestradiol and progesterone levels), data on the number of oocytes retrieved at the time of aspiration, sperm source, fertilization method (IVF or IntraCytoplasmic Sperm Injection, ICSI), transfer of fresh or frozen embryos, and the number of embryos transferred.
During pregnancy, relevant obstetric information was gathered from the patient, including Bi-test results, TSH levels during the first trimester, obstetric complications (such as gestational hypertension, PE, gestational diabetes mellitus, cholestasis, dysthyroidism, placental abnormalities), and prescription taken during pregnancy, especially with respect to acetylsalicylic acid. After birth, data on the type of delivery (vaginal delivery, induction of labor, caesarean section), gestational age at birth, gender and weight of the newborn, placental weight, and peripartum maternal and foetal complications were collected. From each enrolled patient, serum samples and term placental tissue were collected. Blood samples were collected three times, one sample during each trimester of pregnancy; for the groups I, II and III: the first blood sample was collected in conjunction with the blood sampling for the B-test (11-12 th week of gestation), the second following the morphological ultrasound scanning (20 th week of gestation), and the third following the growth monitoring ultrasound scanning (30 th week of gestation).
Statistical analysis Mean and standard deviations were calculated for continuous variables, whereas frequencies and percentages were reported for categorical variables. Non-parametric data were assessed by Mann-Whitney U-tests. Patient data were analyzed by t-Student test. Analysis of different groups of data was performed using two-way analysis of variance (ANOVA). Results were expressed as mean ± standard deviations. P-values <0. 05 were considered statistically significant. All statistical analyses were performed using GraphPad Prism software 9. 0 (GraphPad Software Inc. , La Jolla, CA, USA).
1 zoospore genomic equivalents (GE). Samples with greater than 0. 1 GE were considered Bd positive. Metabolome samples were analysed in a randomised order by ultra-high-pressure liquid chromatographymass spectrometry (UHPLC-MS) (Supplementary methods). Shotgun metagenome sequencing was performed using an Illumina HiSeq 2000 on a subset of samples from the field study (n = 50) and day 30 of the experiment (n = 20). DNA sequences were merged, quality checked and annotated using the Metagenomics Rapid Annotation (MG-RAST) server (vs. 4. 0. 3. ) with default parameters  [38]. Taxonomic analysis was performed using the RefSeq database with the following cut-off parameters: e-value of 1E-5, minimum identity of 60%, maximum alignment length of 15 bp and constrained to only bacterial reads. Functional analyses of bacteria were performed using the KEGG subsystem database.
Beta diversity and differential abundance Fungal taxonomic data was filtered to remove samples with fewer than 233 and 200 sequences for the field and laboratory studies respectively. For shotgun metagenomic field data, we filtered samples with fewer than 9000 KO reads resulting in 47 samples remaining. Bacterial 16S and shotgun data were subsequently filtered to include features with a mean relative abundance > 0. 01% and fungal data was filtered to include taxa with a relative abundance > 0. 05%. Zero values attributed to rare taxa/features were replaced using the Bayesian "CZM" method using the zCompositions R package  [40]  before centred-log ratio (CLR) transformation. To determine if beta diversity of 16S, ITS2, shotgun and metabolome data differed based on population and disease state in the wild, or Bd treatment in the laboratory, each dataset was visualised using principal components analysis (PCA) using mixomics  [41].
To quantify differences in beta diversity based on host population and disease dynamic (wild) or day, Bd and their interaction (laboratory), we applied permutational multivariate analysis of variance (PERMANOVA) on Euclidean distances using the adonis function in the R package vegan  [42]  with 999 permutations. To identify ASVs/OTUs/KOs driving differences in beta diversity based on wild disease dynamic or on experimental treatment on day 30 (the time point corresponding to peak Bd infection), we performed sparse partial least squares-discriminant analysis (sPLS-DA)  [43]  using mixomics  [41]. We tuned our sPLS-DA models (function tune. splsda, mixomics package) using 10 × 5-fold cross-validation to determine optimal model parameters (number of components and feature selection). The performance of our sPLS-DA models were assessed using 100 × 5-fold cross-validation.
We further identified ASVs/OTUs/KOs that differed in abundance in the epizootic and enzootic populations or experimental treatment for days 1, 30 and 60 of the experiment using ALDEx2  [44]  and selected informative features based on Benjamini-Hochberg (BH) corrected Welch's t test p-values < 0. 05 and an effect size 1> and < -1. To assess the congruency of taxonomic data generated from shotgun metagenomics and the bacterial taxonomic 16S data in the field, we performed Procrustes analysis on Euclidean distances of RefSeq CLR-transformed abundance and CLR-transformed 16S data using the R package vegan  [42]. The PROTEST function was used which performs repeated symmetric analyses to estimate if the degree of association of the two matrices is greater than that expected by chance alone. Wilcoxon's tests were performed to determine differentially abundant metabolites based on disease dynamic (wild) or Bd exposure (laboratory).
P-values were adjusted using the false discovery rate procedure and significant features that also had a log2 fold change > 1. 5 were considered most informative as possible biomarkers. Partial least squares discriminant analysis (PLS-DA) was performed using mixomics  [41]. The PLS-DA model was assessed using 5-fold cross-validation repeated 50 times and model significance was tested using a permutation analysis (999) implemented using MVA. test in the RVAideMemoire package  [45]. Three components were selected for PLS-DA for field data (NMC = 0. 02, p = 0. 001) and laboratory data (NMC = 0. 078, p = 0. 001). Metabolites driving differences in metabolome variation based on wild disease dynamic or laboratory pathogen exposure were identified based on the variable influence on the projection (VIP) parameter on component 1.
Omics integration To determine the degree of association between bacterial/fungal taxonomic community structure and overall metabolite composition, we performed Procrustes analysis on Euclidean distance matrices using the vegan package  [42]. To determine if a multi-omics signature could differentiate enzootic/epizootic disease dynamics or laboratory Bd exposure on day 30 for bacteria and metabolite data, we applied the supervised method, data integration analysis for biomarker discovery using latent components (DIABLO)  [46]. We used the CLR-transformed microbiome data (filtered to the top 0. 01% of bacterial ASVs) and g-log-transformed metabolite data as inputs. The block link was set to 0. 1. Model parameterisation (perf function) was performed to select the number of components to use in our final models.